214 results on '"Eklund, Martin"'
Search Results
2. Use of the ISUP e-learning module improves interrater reliability in prostate cancer grading
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MS Urologische Oncologie, Cancer, Pathologie, Flach, Rachel N, Egevad, Lars, Eklund, Martin, van der Kwast, Theodorus H, Delahunt, Brett, Samaratunga, Hemamali, Suelmann, Britt B M, Willemse, Peter-Paul M, Meijer, Richard P, van Diest, Paul J, MS Urologische Oncologie, Cancer, Pathologie, Flach, Rachel N, Egevad, Lars, Eklund, Martin, van der Kwast, Theodorus H, Delahunt, Brett, Samaratunga, Hemamali, Suelmann, Britt B M, Willemse, Peter-Paul M, Meijer, Richard P, and van Diest, Paul J
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- 2024
3. Differences and similarities in false interpretations by AI CAD and radiologists in screening mammography
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Salim, Mattie, Dembrower, Karin, Eklund, Martin, Smith, Kevin, Strand, Fredrik, Salim, Mattie, Dembrower, Karin, Eklund, Martin, Smith, Kevin, and Strand, Fredrik
- Abstract
OBJECTIVE: We aimed to evaluate the false interpretations between artificial intelligence (AI) and radiologists in screening mammography to get a better understanding of how the distribution of diagnostic mistakes might change when moving from entirely radiologist-driven to AI-integrated breast cancer screening. METHODS AND MATERIALS: This retrospective case-control study was based on a mammography screening cohort from 2008 to 2015. The final study population included screening examinations for 714 women diagnosed with breast cancer and 8029 randomly selected healthy controls. Oversampling of controls was applied to attain a similar cancer proportion as in the source screening cohort. We examined how false-positive (FP) and false-negative (FN) assessments by AI, the first reader (RAD 1) and the second reader (RAD 2), were associated with age, density, tumor histology and cancer invasiveness in a single- and double-reader setting. RESULTS: For each reader, the FN assessments were distributed between low- and high-density females with 53 (42%) and 72 (58%) for AI; 59 (36%) and 104 (64%) for RAD 1 and 47 (36%) and 84 (64%) for RAD 2. The corresponding numbers for FP assessments were 1820 (47%) and 2016 (53%) for AI; 1568 (46%) and 1834 (54%) for RAD 1 and 1190 (43%) and 1610 (58%) for RAD 2. For ductal cancer, the FN assessments were 79 (77%) for AI CAD; with 120 (83%) for RAD 1 and with 96 (16%) for RAD 2. For the double-reading simulation, the FP assessments were distributed between younger and older females with 2828 (2.5%) and 1554 (1.4%) for RAD 1 + RAD 2; 3850 (3.4%) and 2940 (2.6%) for AI+RAD 1 and 3430 (3%) and 2772 (2.5%) for AI+RAD 2. CONCLUSION: The most pronounced decrease in FN assessments was noted for females over the age of 55 and for high density-women. In conclusion, AI could have an important complementary role when combined with radiologists to increase sensitivity for high-density and older females. ADVANCES IN KNOWLEDGE: Our results highlight the, QC 20240403
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- 2023
- Full Text
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4. Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants
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Wang, Anqi, Shen, Jiayi, Rodriguez, Alex A., Saunders, Edward J., Chen, Fei, Janivara, Rohini, Darst, Burcu F., Sheng, Xin, Xu, Yili, Chou, Alisha J., Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N., Plym, Anna, Sahimi, Ali, Hoffman, Thomas J., Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Laisk, Triin, Figueredo, Jessica, Muir, Kenneth, Ito, Shuji, Liu, Xiaoxi, Uchio, Yuji, Kubo, Michiaki, Kamatani, Yoichiro, Lophatananon, Artitaya, Wan, Peggy, Andrews, Caroline, Lori, Adriana, Choudhury, Parichoy P., Schleutker, Johanna, Tammela, Teuvo L. J., Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G., Southey, Melissa C., MacInnis, Robert J., Cybulski, Cezary, Wokolorczyk, Dominika, Lubinski, Jan, Rentsch, Christopher T., Cho, Kelly, Mcmahon, Benjamin H., Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Martin, Richard M., Nordestgaard, Borge G., Nielsen, Sune F., Weischer, Maren, Bojesen, Stig E., Roder, Andreas, Stroomberg, Hein V., Batra, Jyotsna, Chambers, Suzanne, Horvath, Lisa, Clements, Judith A., Tilly, Wayne, Risbridger, Gail P., Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordstrom, Tobias, Pashayan, Nora, Dunning, Alison M., Ghoussaini, Maya, Travis, Ruth C., Key, Tim J., Riboli, Elio, Park, Jong Y., Sellers, Thomas A., Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie, Cook, Michael B., Mucci, Lorelei A., Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J., Penney, Kathryn L., Turman, Constance, Tangen, Catherine M., Goodman, Phyllis J., Thompson, Ian M., Jr., Hamilton, Robert J., Fleshner, Neil E., Finelli, Antonio, Parent, Marie-Elise, Stanford, Janet L., Ostrander, Elaine A., Koutros, Stella, Freeman, Laura E. Beane, Stampfer, Meir, Wolk, Alicja, Hakansson, Niclas, Andriole, Gerald L., Hoover, Robert N., Machiela, Mitchell J., Sorensen, Karina Dalsgaard, Borre, Michael, Blot, William J., Zheng, Wei, Yeboah, Edward D., Mensah, James E., Lu, Yong-Jie, Zhang, Hong-Wei, Feng, Ninghan, Mao, Xueying, Wu, Yudong, Zhao, Shan-Chao, Sun, Zan, Thibodeau, Stephen N., McDonnell, Shannon K., Schaid, Daniel J., West, Catharine M. L., Barnett, Gill, Maier, Christiane, Schnoeller, Thomas, Luedeke, Manuel, Kibel, Adam S., Drake, Bettina F., Cussenot, Olivier, Cancel-Tassin, Geraldine, Menegaux, Florence, Truong, Therese, Koudou, Yves Akoli, John, Esther M., Grindedal, Eli Marie, Maehle, Lovise, Khaw, Kay-Tee, Ingles, Sue A., Stern, Mariana C., Vega, Ana, Gomez-Caamano, Antonio, Fachal, Laura, Rosenstein, Barry S., Kerns, Sarah L., Ostrer, Harry, Teixeira, Manuel R., Paulo, Paula, Brandao, Andreia, Watya, Stephen, Lubwama, Alexander, Bensen, Jeannette T., Butler, Ebonee N., Mohler, James L., Taylor, Jack A., Kogevinas, Manolis, Dierssen-Sotos, Trinidad, Castano-Vinyals, Gemma, Cannon-Albright, Lisa, Teerlink, Craig C., Huff, Chad D., Pilie, Patrick, Yu, Yao, Bohlender, Ryan J., Gu, Jian, Strom, Sara S., Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Kaneva, Radka, Slavov, Chavdar, Mitev, Vanio, Leach, Robin J., Brenner, Hermann, Chen, Xuechen, Holleczek, Bernd, Schoettker, Ben, Klein, Eric A., Hsing, Ann W., Kittles, Rick A., Murphy, Adam B., Logothetis, Christopher J., Kim, Jeri, Neuhausen, Susan L., Steele, Linda, Ding, Yuan Chun, Isaacs, William B., Nemesure, Barbara, Hennis, Anselm J. M., Carpten, John, Pandha, Hardev, Michael, Agnieszka, De Ruyck, Kim, De Meerleer, Gert, Ost, Piet, Xu, Jianfeng, Razack, Azad, Lim, Jasmine, Teo, Soo-Hwang, Newcomb, Lisa F., Lin, Daniel W., Fowke, Jay H., Neslund-Dudas, Christine M., Rybicki, Benjamin A., Gamulin, Marija, Lessel, Davor, Kulis, Tomislav, Usmani, Nawaid, Abraham, Aswin, Singhal, Sandeep, Parliament, Matthew, Claessens, Frank, Joniau, Steven, Van den Broeck, Thomas, Gago-Dominguez, Manuela, Castelao, Jose Esteban, Martinez, Maria Elena, Larkin, Samantha, Townsend, Paul A., Aukim-Hastie, Claire, Bush, William S., Aldrich, Melinda C., Crawford, Dana C., Srivastava, Shiv, Cullen, Jennifer, Petrovics, Gyorgy, Casey, Graham, Wang, Ying, Tettey, Yao, Lachance, Joseph, Tang, Wei, Biritwum, Richard B., Adjei, Andrew A., Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Yamoah, Kosj, Govindasami, Koveela, Chokkalingam, Anand P., Keaton, Jacob M., Hellwege, Jacklyn N., Clark, Peter E., Jalloh, Mohamed, Gueye, Serigne M., Niang, Lamine, Ogunbiyi, Olufemi, Shittu, Olayiwola, Amodu, Olukemi, Adebiyi, Akindele O., Aisuodionoe-Shadrach, Oseremen I., Ajibola, Hafees O., Jamda, Mustapha A., Oluwole, Olabode P., Nwegbu, Maxwell, Adusei, Ben, Mante, Sunny, Darkwa-Abrahams, Afua, Diop, Halimatou, Gundell, Susan M., Roobol, Monique J., Jenster, Guido, van Schaik, Ron H. N., Hu, Jennifer J., Sanderson, Maureen, Kachuri, Linda, Varma, Rohit, McKean-Cowdin, Roberta, Torres, Mina, Preuss, Michael H., Loos, Ruth J. F., Zawistowski, Matthew, Zollner, Sebastian, Lu, Zeyun, Van Den Eeden, Stephen K., Easton, Douglas F., Ambs, Stefan, Edwards, Todd L., Magi, Reedik, Rebbeck, Timothy R., Fritsche, Lars, Chanock, Stephen J., Berndt, Sonja I., Wiklund, Fredrik, Nakagawa, Hidewaki, Witte, John S., Gaziano, J. Michael, Justice, Amy C., Mancuso, Nick, Terao, Chikashi, Eeles, Rosalind A., Kote-Jarai, Zsofia, Madduri, Ravi K., Conti, David V., Haiman, Christopher A., Wang, Anqi, Shen, Jiayi, Rodriguez, Alex A., Saunders, Edward J., Chen, Fei, Janivara, Rohini, Darst, Burcu F., Sheng, Xin, Xu, Yili, Chou, Alisha J., Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N., Plym, Anna, Sahimi, Ali, Hoffman, Thomas J., Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Laisk, Triin, Figueredo, Jessica, Muir, Kenneth, Ito, Shuji, Liu, Xiaoxi, Uchio, Yuji, Kubo, Michiaki, Kamatani, Yoichiro, Lophatananon, Artitaya, Wan, Peggy, Andrews, Caroline, Lori, Adriana, Choudhury, Parichoy P., Schleutker, Johanna, Tammela, Teuvo L. J., Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G., Southey, Melissa C., MacInnis, Robert J., Cybulski, Cezary, Wokolorczyk, Dominika, Lubinski, Jan, Rentsch, Christopher T., Cho, Kelly, Mcmahon, Benjamin H., Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Martin, Richard M., Nordestgaard, Borge G., Nielsen, Sune F., Weischer, Maren, Bojesen, Stig E., Roder, Andreas, Stroomberg, Hein V., Batra, Jyotsna, Chambers, Suzanne, Horvath, Lisa, Clements, Judith A., Tilly, Wayne, Risbridger, Gail P., Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordstrom, Tobias, Pashayan, Nora, Dunning, Alison M., Ghoussaini, Maya, Travis, Ruth C., Key, Tim J., Riboli, Elio, Park, Jong Y., Sellers, Thomas A., Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie, Cook, Michael B., Mucci, Lorelei A., Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J., Penney, Kathryn L., Turman, Constance, Tangen, Catherine M., Goodman, Phyllis J., Thompson, Ian M., Jr., Hamilton, Robert J., Fleshner, Neil E., Finelli, Antonio, Parent, Marie-Elise, Stanford, Janet L., Ostrander, Elaine A., Koutros, Stella, Freeman, Laura E. Beane, Stampfer, Meir, Wolk, Alicja, Hakansson, Niclas, Andriole, Gerald L., Hoover, Robert N., Machiela, Mitchell J., Sorensen, Karina Dalsgaard, Borre, Michael, Blot, William J., Zheng, Wei, Yeboah, Edward D., Mensah, James E., Lu, Yong-Jie, Zhang, Hong-Wei, Feng, Ninghan, Mao, Xueying, Wu, Yudong, Zhao, Shan-Chao, Sun, Zan, Thibodeau, Stephen N., McDonnell, Shannon K., Schaid, Daniel J., West, Catharine M. L., Barnett, Gill, Maier, Christiane, Schnoeller, Thomas, Luedeke, Manuel, Kibel, Adam S., Drake, Bettina F., Cussenot, Olivier, Cancel-Tassin, Geraldine, Menegaux, Florence, Truong, Therese, Koudou, Yves Akoli, John, Esther M., Grindedal, Eli Marie, Maehle, Lovise, Khaw, Kay-Tee, Ingles, Sue A., Stern, Mariana C., Vega, Ana, Gomez-Caamano, Antonio, Fachal, Laura, Rosenstein, Barry S., Kerns, Sarah L., Ostrer, Harry, Teixeira, Manuel R., Paulo, Paula, Brandao, Andreia, Watya, Stephen, Lubwama, Alexander, Bensen, Jeannette T., Butler, Ebonee N., Mohler, James L., Taylor, Jack A., Kogevinas, Manolis, Dierssen-Sotos, Trinidad, Castano-Vinyals, Gemma, Cannon-Albright, Lisa, Teerlink, Craig C., Huff, Chad D., Pilie, Patrick, Yu, Yao, Bohlender, Ryan J., Gu, Jian, Strom, Sara S., Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Kaneva, Radka, Slavov, Chavdar, Mitev, Vanio, Leach, Robin J., Brenner, Hermann, Chen, Xuechen, Holleczek, Bernd, Schoettker, Ben, Klein, Eric A., Hsing, Ann W., Kittles, Rick A., Murphy, Adam B., Logothetis, Christopher J., Kim, Jeri, Neuhausen, Susan L., Steele, Linda, Ding, Yuan Chun, Isaacs, William B., Nemesure, Barbara, Hennis, Anselm J. M., Carpten, John, Pandha, Hardev, Michael, Agnieszka, De Ruyck, Kim, De Meerleer, Gert, Ost, Piet, Xu, Jianfeng, Razack, Azad, Lim, Jasmine, Teo, Soo-Hwang, Newcomb, Lisa F., Lin, Daniel W., Fowke, Jay H., Neslund-Dudas, Christine M., Rybicki, Benjamin A., Gamulin, Marija, Lessel, Davor, Kulis, Tomislav, Usmani, Nawaid, Abraham, Aswin, Singhal, Sandeep, Parliament, Matthew, Claessens, Frank, Joniau, Steven, Van den Broeck, Thomas, Gago-Dominguez, Manuela, Castelao, Jose Esteban, Martinez, Maria Elena, Larkin, Samantha, Townsend, Paul A., Aukim-Hastie, Claire, Bush, William S., Aldrich, Melinda C., Crawford, Dana C., Srivastava, Shiv, Cullen, Jennifer, Petrovics, Gyorgy, Casey, Graham, Wang, Ying, Tettey, Yao, Lachance, Joseph, Tang, Wei, Biritwum, Richard B., Adjei, Andrew A., Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Yamoah, Kosj, Govindasami, Koveela, Chokkalingam, Anand P., Keaton, Jacob M., Hellwege, Jacklyn N., Clark, Peter E., Jalloh, Mohamed, Gueye, Serigne M., Niang, Lamine, Ogunbiyi, Olufemi, Shittu, Olayiwola, Amodu, Olukemi, Adebiyi, Akindele O., Aisuodionoe-Shadrach, Oseremen I., Ajibola, Hafees O., Jamda, Mustapha A., Oluwole, Olabode P., Nwegbu, Maxwell, Adusei, Ben, Mante, Sunny, Darkwa-Abrahams, Afua, Diop, Halimatou, Gundell, Susan M., Roobol, Monique J., Jenster, Guido, van Schaik, Ron H. N., Hu, Jennifer J., Sanderson, Maureen, Kachuri, Linda, Varma, Rohit, McKean-Cowdin, Roberta, Torres, Mina, Preuss, Michael H., Loos, Ruth J. F., Zawistowski, Matthew, Zollner, Sebastian, Lu, Zeyun, Van Den Eeden, Stephen K., Easton, Douglas F., Ambs, Stefan, Edwards, Todd L., Magi, Reedik, Rebbeck, Timothy R., Fritsche, Lars, Chanock, Stephen J., Berndt, Sonja I., Wiklund, Fredrik, Nakagawa, Hidewaki, Witte, John S., Gaziano, J. Michael, Justice, Amy C., Mancuso, Nick, Terao, Chikashi, Eeles, Rosalind A., Kote-Jarai, Zsofia, Madduri, Ravi K., Conti, David V., and Haiman, Christopher A.
- Abstract
The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.
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- 2023
- Full Text
- View/download PDF
5. Biochemical Recurrence and Risk of Mortality Following Radiotherapy or Radical Prostatectomy
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Falagario, Ugo Giovanni, Abbadi, Ahmad, Remmers, Sebastiaan, Björnebo, Lars, Bogdanovic, Darko, Martini, Alberto, Valdman, Alexander, Carrieri, Giuseppe, Menon, Mani, Akre, Olof, Eklund, Martin, Nordström, Tobias, Grönberg, Henrik, Lantz, Anna, Wiklund, Peter, Falagario, Ugo Giovanni, Abbadi, Ahmad, Remmers, Sebastiaan, Björnebo, Lars, Bogdanovic, Darko, Martini, Alberto, Valdman, Alexander, Carrieri, Giuseppe, Menon, Mani, Akre, Olof, Eklund, Martin, Nordström, Tobias, Grönberg, Henrik, Lantz, Anna, and Wiklund, Peter
- Abstract
Importance: Stratifying patients with biochemical recurrence (BCR) after primary treatment for prostate cancer based on the risk of prostate cancer-specific mortality (PCSM) is essential for determining the need for further testing and treatments. Objective: To evaluate the association of BCR after radical prostatectomy or radiotherapy and its current risk stratification with PCSM. Design, Setting, and Participants: This population-based cohort study included a total of 16 311 male patients with 10 364 (64%) undergoing radical prostatectomy and 5947 (36%) undergoing radiotherapy with curative intent (cT1-3, cM0) and PSA follow-up in Stockholm, Sweden, between 2003 and 2019. Follow-up for all patients was until death, emigration, or end of the study (ie, December 31, 2018). Data were analyzed between September 2022 and March 2023. Main Outcomes and Measures: Primary outcomes of the study were the cumulative incidence of BCR and PCSM. Patients with BCR were stratified in low- and high-risk according to European Association of Urology (EAU) criteria. Exposures: Radical prostatectomy or radiotherapy. Results: A total of 16 311 patients were included. Median (IQR) age was 64 (59-68) years in the radical prostatectomy cohort (10 364 patients) and 69 (64-73) years in the radiotherapy cohort (5947 patients). Median (IQR) follow-up for survivors was 88 (55-138) months and 89 (53-134) months, respectively. Following radical prostatectomy, the 15-year cumulative incidences of BCR were 16% (95% CI, 15%-18%) for the 4024 patients in the low D'Amico risk group, 30% (95% CI, 27%-32%) for the 5239 patients in the intermediate D'Amico risk group, and 46% (95% CI, 42%-51%) for 1101 patients in the high D'Amico risk group. Following radiotherapy, the 15-year cumulative incidences of BCR were 18% (95% CI, 15%-21%) for the 1230 patients in the low-risk group, 24% (95% CI, 21%-26%) for the 2355 patients in the intermediate-risk group, and 36% (95% CI, 33%-39%) for the 2362 patients in
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- 2023
6. Interobserver reproducibility of cribriform cancer in prostate needle biopsies and validation of International Society of Urological Pathology criteria
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Egevad, Lars, Delahunt, Brett, Iczkowski, Kenneth A., van der Kwast, Theo, van Leenders, Geert J.L.H., Leite, Katia R.M., Pan, Chin Chen, Samaratunga, Hemamali, Tsuzuki, Toyonori, Mulliqi, Nita, Ji, Xiaoyi, Olsson, Henrik, Valkonen, Masi, Ruusuvuori, Pekka, Eklund, Martin, Kartasalo, Kimmo, Egevad, Lars, Delahunt, Brett, Iczkowski, Kenneth A., van der Kwast, Theo, van Leenders, Geert J.L.H., Leite, Katia R.M., Pan, Chin Chen, Samaratunga, Hemamali, Tsuzuki, Toyonori, Mulliqi, Nita, Ji, Xiaoyi, Olsson, Henrik, Valkonen, Masi, Ruusuvuori, Pekka, Eklund, Martin, and Kartasalo, Kimmo
- Abstract
Aims: There is strong evidence that cribriform morphology indicates a worse prognosis of prostatic adenocarcinoma. Our aim was to investigate its interobserver reproducibility in prostate needle biopsies. Methods and results: A panel of nine prostate pathology experts from five continents independently reviewed 304 digitised biopsies for cribriform cancer according to recent International Society of Urological Pathology criteria. The biopsies were collected from a series of 702 biopsies that were reviewed by one of the panellists for enrichment of high-grade cancer and potentially cribriform structures. A 2/3 consensus diagnosis of cribriform and noncribriform cancer was reached in 90% (272/304) of the biopsies with a mean kappa value of 0.56 (95% confidence interval 0.52–0.61). The prevalence of consensus cribriform cancers was estimated to 4%, 12%, 21%, and 20% of Gleason scores 7 (3 + 4), 7 (4 + 3), 8, and 9–10, respectively. More than two cribriform structures per level or a largest cribriform mass with ≥9 lumina or a diameter of ≥0.5 mm predicted a consensus diagnosis of cribriform cancer in 88% (70/80), 84% (87/103), and 90% (56/62), respectively, and noncribriform cancer in 3% (2/80), 5% (5/103), and 2% (1/62), respectively (all P < 0.01). Conclusion: Cribriform prostate cancer was seen in a minority of needle biopsies with high-grade cancer. Stringent diagnostic criteria enabled the identification of cribriform patterns and the generation of a large set of consensus cases for standardisation.
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- 2023
7. Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study
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Kwong, Jethro J.C.C., Khondker, Adree, Meng, Eric, Taylor, Nicholas, Kuk, Cynthia, Perlis, Nathan, Kulkarni, Girish G.S., Hamilton, Robert James, Fleshner, Neil Eric, Finelli, Antonio, van der Kwast, Theodorus H., Ali, Amna, Jamal, Munir, Papanikolaou, Frank, Short, Thomas, Srigley, John J.R., Colinet, Valentin, Peltier, Alexandre, Diamand, Romain, Lefebvre, Y., Mandoorah, Qusay, Sanchez-Salas, Rafael, Macek, Petr, Cathelineau, Xavier, Eklund, Martin, Johnson, Alistair Edward William, Feifer, Andrew, Zlotta, Alexandre, Kwong, Jethro J.C.C., Khondker, Adree, Meng, Eric, Taylor, Nicholas, Kuk, Cynthia, Perlis, Nathan, Kulkarni, Girish G.S., Hamilton, Robert James, Fleshner, Neil Eric, Finelli, Antonio, van der Kwast, Theodorus H., Ali, Amna, Jamal, Munir, Papanikolaou, Frank, Short, Thomas, Srigley, John J.R., Colinet, Valentin, Peltier, Alexandre, Diamand, Romain, Lefebvre, Y., Mandoorah, Qusay, Sanchez-Salas, Rafael, Macek, Petr, Cathelineau, Xavier, Eklund, Martin, Johnson, Alistair Edward William, Feifer, Andrew, and Zlotta, Alexandre
- Abstract
Background: Accurate prediction of side-specific extraprostatic extension (ssEPE) is essential for performing nerve-sparing surgery to mitigate treatment-related side-effects such as impotence and incontinence in patients with localised prostate cancer. Artificial intelligence (AI) might provide robust and personalised ssEPE predictions to better inform nerve-sparing strategy during radical prostatectomy. We aimed to develop, externally validate, and perform an algorithmic audit of an AI-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA). Methods: Each prostatic lobe was treated as an individual case such that each patient contributed two cases to the overall cohort. SEPERA was trained on 1022 cases from a community hospital network (Trillium Health Partners; Mississauga, ON, Canada) between 2010 and 2020. Subsequently, SEPERA was externally validated on 3914 cases across three academic centres: Princess Margaret Cancer Centre (Toronto, ON, Canada) from 2008 to 2020; L'Institut Mutualiste Montsouris (Paris, France) from 2010 to 2020; and Jules Bordet Institute (Brussels, Belgium) from 2015 to 2020. Model performance was characterised by area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), calibration, and net benefit. SEPERA was compared against contemporary nomograms (ie, Sayyid nomogram, Soeterik nomogram [non-MRI and MRI]), as well as a separate logistic regression model using the same variables included in SEPERA. An algorithmic audit was performed to assess model bias and identify common patient characteristics among predictive errors. Findings: Overall, 2468 patients comprising 4936 cases (ie, prostatic lobes) were included in this study. SEPERA was well calibrated and had the best performance across all validation cohorts (pooled AUROC of 0·77 [95% CI 0·75–0·78] and pooled AUPRC of 0·61 [0·58–0·63]). In patients with pathological ssEPE despite benign ipsilateral biopsies, SEPER, SCOPUS: ar.j, info:eu-repo/semantics/published
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- 2023
8. Reconsidering evaluation practices in modular systems: On the propagation of errors in MRI prostate cancer detection
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Rolfsnes, Erlend Sortland, Thangngat, Philip, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, Fernandez-Quilez, Alvaro, Rolfsnes, Erlend Sortland, Thangngat, Philip, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, and Fernandez-Quilez, Alvaro
- Abstract
Magnetic resonance imaging has evolved as a key component for prostate cancer (PCa) detection, substantially increasing the radiologist workload. Artificial intelligence (AI) systems can support radiological assessment by segmenting and classifying lesions in clinically significant (csPCa) and non-clinically significant (ncsPCa). Commonly, AI systems for PCa detection involve an automatic prostate segmentation followed by the lesion detection using the extracted prostate. However, evaluation reports are typically presented in terms of detection under the assumption of the availability of a highly accurate segmentation and an idealistic scenario, omitting the propagation of errors between modules. For that purpose, we evaluate the effect of two different segmentation networks (s1 and s2) with heterogeneous performances in the detection stage and compare it with an idealistic setting (s1:89.90+-2.23 vs 88.97+-3.06 ncsPCa, P<.001, 89.30+-4.07 and 88.12+-2.71 csPCa, P<.001). Our results depict the relevance of a holistic evaluation, accounting for all the sub-modules involved in the system., Comment: Under review
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- 2023
9. Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
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Lindeijer, Tim Nikolass, Ytredal, Tord Martin, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, Fernandez-Quilez, Alvaro, Lindeijer, Tim Nikolass, Ytredal, Tord Martin, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, and Fernandez-Quilez, Alvaro
- Abstract
An accurate prostate delineation and volume characterization can support the clinical assessment of prostate cancer. A large amount of automatic prostate segmentation tools consider exclusively the axial MRI direction in spite of the availability as per acquisition protocols of multi-view data. Further, when multi-view data is exploited, manual annotations and availability at test time for all the views is commonly assumed. In this work, we explore a contrastive approach at training time to leverage multi-view data without annotations and provide flexibility at deployment time in the event of missing views. We propose a triplet encoder and single decoder network based on U-Net, tU-Net (triplet U-Net). Our proposed architecture is able to exploit non-annotated sagittal and coronal views via contrastive learning to improve the segmentation from a volumetric perspective. For that purpose, we introduce the concept of inter-view similarity in the latent space. To guide the training, we combine a dice score loss calculated with respect to the axial view and its manual annotations together with a multi-view contrastive loss. tU-Net shows statistical improvement in dice score coefficient (DSC) with respect to only axial view (91.25+-0.52% compared to 86.40+-1.50%,P<.001). Sensitivity analysis reveals the volumetric positive impact of the contrastive loss when paired with tU-Net (2.85+-1.34% compared to 3.81+-1.88%,P<.001). Further, our approach shows good external volumetric generalization in an in-house dataset when tested with multi-view data (2.76+-1.89% compared to 3.92+-3.31%,P=.002), showing the feasibility of exploiting non-annotated multi-view data through contrastive learning whilst providing flexibility at deployment in the event of missing views., Comment: Under review
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- 2023
10. Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection
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Fernandez-Quilez, Alvaro, Nordström, Tobias, Jäderling, Fredrik, Kjosavik, Svein Reidar, Eklund, Martin, Fernandez-Quilez, Alvaro, Nordström, Tobias, Jäderling, Fredrik, Kjosavik, Svein Reidar, and Eklund, Martin
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Background: Prostate cancer (PC) MRI-based risk calculators are commonly based on biological (e.g. PSA), MRI markers (e.g. volume), and patient age. Whilst patient age measures the amount of years an individual has existed, biological age (BA) might better reflect the physiology of an individual. However, surrogates from prostate MRI and linkage with clinically significant PC (csPC) remain to be explored. Purpose: To obtain and evaluate Prostate Age Gap (PAG) as an MRI marker tool for csPC risk. Study type: Retrospective. Population: A total of 7243 prostate MRI slices from 468 participants who had undergone prostate biopsies. A deep learning model was trained on 3223 MRI slices cropped around the gland from 81 low-grade PC (ncsPC, Gleason score <=6) and 131 negative cases and tested on the remaining 256 participants. Assessment: Chronological age was defined as the age of the participant at the time of the visit and used to train the deep learning model to predict the age of the patient. Following, we obtained PAG, defined as the model predicted age minus the patient's chronological age. Multivariate logistic regression models were used to estimate the association through odds ratio (OR) and predictive value of PAG and compared against PSA levels and PI-RADS>=3. Statistical tests: T-test, Mann-Whitney U test, Permutation test and ROC curve analysis. Results: The multivariate adjusted model showed a significant difference in the odds of clinically significant PC (csPC, Gleason score >=7) (OR =3.78, 95% confidence interval (CI):2.32-6.16, P <.001). PAG showed a better predictive ability when compared to PI-RADS>=3 and adjusted by other risk factors, including PSA levels: AUC =0.981 vs AUC =0.704, p<.001. Conclusion: PAG was significantly associated with the risk of clinically significant PC and outperformed other well-established PC risk factors., Comment: Under review
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- 2023
11. Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
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Ji, Xiaoyi, Salmon, Richard, Mulliqi, Nita, Khan, Umair, Wang, Yinxi, Blilie, Anders, Olsson, Henrik, Pedersen, Bodil Ginnerup, Sørensen, Karina Dalsgaard, Ulhøi, Benedicte Parm, Kjosavik, Svein R, Janssen, Emilius AM, Rantalainen, Mattias, Egevad, Lars, Ruusuvuori, Pekka, Eklund, Martin, Kartasalo, Kimmo, Ji, Xiaoyi, Salmon, Richard, Mulliqi, Nita, Khan, Umair, Wang, Yinxi, Blilie, Anders, Olsson, Henrik, Pedersen, Bodil Ginnerup, Sørensen, Karina Dalsgaard, Ulhøi, Benedicte Parm, Kjosavik, Svein R, Janssen, Emilius AM, Rantalainen, Mattias, Egevad, Lars, Ruusuvuori, Pekka, Eklund, Martin, and Kartasalo, Kimmo
- Abstract
The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical. Changes in the imaging workflow can also lead to compromised diagnoses and patient safety risks. We evaluated whether physical color calibration of scanners can standardize WSI appearance and enable robust AI performance. We employed a color calibration slide in four different laboratories and evaluated its impact on the performance of an AI system for prostate cancer diagnosis on 1,161 WSIs. Color standardization resulted in consistently improved AI model calibration and significant improvements in Gleason grading performance. The study demonstrates that physical color calibration provides a potential solution to the variation introduced by different scanners, making AI-based cancer diagnostics more reliable and applicable in clinical settings.
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- 2023
12. Response to Carter et al.
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Eklund, Martin, Eklund, Martin, Broglio, Kristine, Yau, Christina, Connor, Jason T, Fiscalini, Allison Stover, Esserman, Laura J, Eklund, Martin, Eklund, Martin, Broglio, Kristine, Yau, Christina, Connor, Jason T, Fiscalini, Allison Stover, and Esserman, Laura J
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- 2020
13. Response to Carter et al.
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Eklund, Martin, Eklund, Martin, Broglio, Kristine, Yau, Christina, Connor, Jason T, Fiscalini, Allison Stover, Esserman, Laura J, Eklund, Martin, Eklund, Martin, Broglio, Kristine, Yau, Christina, Connor, Jason T, Fiscalini, Allison Stover, and Esserman, Laura J
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- 2020
14. Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge
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Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Strom, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Gronberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Hakkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, Eklund, Martin, Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Strom, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Gronberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Hakkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin
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Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]
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- 2022
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15. A Head-to-head Comparison of Prostate Cancer Diagnostic Strategies Using the Stockholm3 Test, Magnetic Resonance Imaging, and Swedish National Guidelines : Results from a Prospective Population-based Screening Study
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Waldén, Mauritz, Aldrimer, Mattias, Heydorn Lagerlöf, Jakob, Eklund, Martin, Grönberg, Henrik, Nordström, Tobias, Palsdottir, Thorgerdur, Waldén, Mauritz, Aldrimer, Mattias, Heydorn Lagerlöf, Jakob, Eklund, Martin, Grönberg, Henrik, Nordström, Tobias, and Palsdottir, Thorgerdur
- Abstract
Background: Strategies for early detection of prostate cancer aim to detect clinically significant prostate cancer (csPCa) and avoid detection of insignificant cancers and unnecessary biopsies. Swedish national guidelines (SNGs), years 2019 and 2020, involve prostate-specific antigen (PSA) testing, clinical variables, and magnetic resonance imaging (MRI). The Stockholm3 test and MRI have been suggested to improve selection of men for prostate biopsy. Performance of SNGs compared with the Stockholm3 test or MRI in a screening setting is unclear. Objective: To compare strategies based on previous and current national guidelines, Stockholm3, and MRI to select patients for biopsy in a screening-by-invitation setting. Design setting and participants: All participants underwent PSA test, and men with PSA ≥3 ng/ml underwent Stockholm3 testing and MRI. Men with Stockholm3 ≥11%, Prostate Imaging Reporting and Data System score ≥3 on MRI, or indication according to SNG-2019 or SNG-2020 were referred to biopsy. Outcome measurements and statistical analysis: The primary outcome was the detection of csPCa at prostate biopsy, defined as an International Society of Urological Pathology (ISUP) grade of ≥2. Results and limitations: We invited 8764 men from the general population, 272 of whom had PSA ≥3 ng/ml. The median PSA was 4.1 (interquartile range: 3.4-5.8), and 136 of 270 (50%) who underwent MRI lacked any pathological lesions. In total, 37 csPCa cases were diagnosed. Using SNG-2019, 36 csPCa cases with a high biopsy rate (179 of 272) were detected and 49 were diagnosed with ISUP 1 cancers. The Stockholm3 strategy diagnosed 32 csPCa cases, with 89 biopsied and 27 ISUP 1 cancers. SNG-2020 detected 32 csPCa and 33 ISUP 1 cancer patients, with 99 men biopsied, and the MRI strategy detected 30 csPCa and 35 ISUP 1 cancer cases by biopsying 123 men. The latter two strategies generated more MRI scans than the Stockholm3 strategy (n = 270 vs 33). Conclusions: Previous guidelines provi
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- 2022
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16. I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations.
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Files, Daniel Clark, Files, Daniel Clark, Matthay, Michael A, Calfee, Carolyn S, Aggarwal, Neil R, Asare, Adam L, Beitler, Jeremy R, Berger, Paul A, Burnham, Ellen L, Cimino, George, Coleman, Melissa H, Crippa, Alessio, Discacciati, Andrea, Gandotra, Sheetal, Gibbs, Kevin W, Henderson, Paul T, Ittner, Caroline AG, Jauregui, Alejandra, Khan, Kashif T, Koff, Jonathan L, Lang, Julie, LaRose, Mary, Levitt, Joe, Lu, Ruixiao, McKeehan, Jeffrey D, Meyer, Nuala J, Russell, Derek W, Thomas, Karl W, Eklund, Martin, Esserman, Laura J, Liu, Kathleen D, ISPY COVID Adaptive Platform Trial Network, undefined, Files, Daniel Clark, Files, Daniel Clark, Matthay, Michael A, Calfee, Carolyn S, Aggarwal, Neil R, Asare, Adam L, Beitler, Jeremy R, Berger, Paul A, Burnham, Ellen L, Cimino, George, Coleman, Melissa H, Crippa, Alessio, Discacciati, Andrea, Gandotra, Sheetal, Gibbs, Kevin W, Henderson, Paul T, Ittner, Caroline AG, Jauregui, Alejandra, Khan, Kashif T, Koff, Jonathan L, Lang, Julie, LaRose, Mary, Levitt, Joe, Lu, Ruixiao, McKeehan, Jeffrey D, Meyer, Nuala J, Russell, Derek W, Thomas, Karl W, Eklund, Martin, Esserman, Laura J, Liu, Kathleen D, ISPY COVID Adaptive Platform Trial Network, and undefined
- Abstract
IntroductionThe COVID-19 pandemic brought an urgent need to discover novel effective therapeutics for patients hospitalised with severe COVID-19. The Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis (ISPY COVID-19 trial) was designed and implemented in early 2020 to evaluate investigational agents rapidly and simultaneously on a phase 2 adaptive platform. This manuscript outlines the design, rationale, implementation and challenges of the ISPY COVID-19 trial during the first phase of trial activity from April 2020 until December 2021.Methods and analysisThe ISPY COVID-19 Trial is a multicentre open-label phase 2 platform trial in the USA designed to evaluate therapeutics that may have a large effect on improving outcomes from severe COVID-19. The ISPY COVID-19 Trial network includes academic and community hospitals with significant geographical diversity across the country. Enrolled patients are randomised to receive one of up to four investigational agents or a control and are evaluated for a family of two primary outcomes-time to recovery and mortality. The statistical design uses a Bayesian model with 'stopping' and 'graduation' criteria designed to efficiently discard ineffective therapies and graduate promising agents for definitive efficacy trials. Each investigational agent arm enrols to a maximum of 125 patients per arm and is compared with concurrent controls. As of December 2021, 11 investigational agent arms had been activated, and 8 arms were complete. Enrolment and adaptation of the trial design are ongoing.Ethics and disseminationISPY COVID-19 operates under a central institutional review board via Wake Forest School of Medicine IRB00066805. Data generated from this trial will be reported in peer-reviewed medical journals.Trial registration numberNCT04488081.
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- 2022
17. Time to castration-resistant prostate cancer and prostate cancer death according to PSA response in men with non-metastatic prostate cancer treated with gonadotropin releasing hormone agonists
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Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, Robinson, David, Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, and Robinson, David
- Abstract
Objectives: To predict castration-resistant prostate cancer (CRPC) and prostate cancer (Pca) death by use of clinical variables at Pca diagnosis and PSA levels after start of gonadotropin-releasing hormone agonists (GnRH) in men with non-metastatic castration sensitive prostate cancer (nmCSPC). Materials and Methods: PSA values for 1603 men with nmCSPC in the National Prostate Cancer Register of Sweden who received GnRH as primary treatment were retrieved from Uppsala-Örebro PSA Cohort and Stockholm PSA and Biopsy Register. All men had measured PSA before (pre-GnRH PSA) and 3–6 months after (post-GnRH PSA) date of start of GnRH. Unadjusted and adjusted Cox models were used to predict CRPC by PSA levels. PSA levels and ISUP grade were used to construct a risk score to stratify men by tertiles according to risk of CRPC and Pca death. Results: 788 (49%) men reached CRPC and 456 (28%) died of Pca during follow-up. Post-GnRH PSA predicted CRPC regardless of pre-GnRH PSA. CRPC risk increased with higher post-GnRH PSA, HR 4.7 (95% CI: 3.4–6.7) for PSA > 16 ng/mL vs 0–0.25 ng/mL and with ISUP grade, HR 3.7 (95%: 2.5–5.4) for ISUP 5 vs ISUP 1. Risk of Pca death in men above top vs bellow bottom tertile of post-GnRH PSA and ISUP grade was HR 4.1 (95% CI: 3.0–5.5). Conclusion: A risk score based on post-GnRH PSA and ISUP grade could be used for early identification of a target group for future clinical trials on additional therapy to GnRH.
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- 2022
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18. Modeling Disease Trajectories for Castration-resistant Prostate Cancer Using Nationwide Population-based Data
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Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, Garmo, Hans, Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, and Garmo, Hans
- Abstract
Background: Little is known about disease trajectories for men with castration -resistant prostate cancer (CRPC).Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes.Design, setting, and participants: The model was generated using population -based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state.Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category.Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data.Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category.Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
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- 2022
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19. Time to castration-resistant prostate cancer and prostate cancer death according to PSA response in men with non-metastatic prostate cancer treated with gonadotropin releasing hormone agonists
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Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, Robinson, David, Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, and Robinson, David
- Abstract
Objectives: To predict castration-resistant prostate cancer (CRPC) and prostate cancer (Pca) death by use of clinical variables at Pca diagnosis and PSA levels after start of gonadotropin-releasing hormone agonists (GnRH) in men with non-metastatic castration sensitive prostate cancer (nmCSPC). Materials and Methods: PSA values for 1603 men with nmCSPC in the National Prostate Cancer Register of Sweden who received GnRH as primary treatment were retrieved from Uppsala-Örebro PSA Cohort and Stockholm PSA and Biopsy Register. All men had measured PSA before (pre-GnRH PSA) and 3–6 months after (post-GnRH PSA) date of start of GnRH. Unadjusted and adjusted Cox models were used to predict CRPC by PSA levels. PSA levels and ISUP grade were used to construct a risk score to stratify men by tertiles according to risk of CRPC and Pca death. Results: 788 (49%) men reached CRPC and 456 (28%) died of Pca during follow-up. Post-GnRH PSA predicted CRPC regardless of pre-GnRH PSA. CRPC risk increased with higher post-GnRH PSA, HR 4.7 (95% CI: 3.4–6.7) for PSA > 16 ng/mL vs 0–0.25 ng/mL and with ISUP grade, HR 3.7 (95%: 2.5–5.4) for ISUP 5 vs ISUP 1. Risk of Pca death in men above top vs bellow bottom tertile of post-GnRH PSA and ISUP grade was HR 4.1 (95% CI: 3.0–5.5). Conclusion: A risk score based on post-GnRH PSA and ISUP grade could be used for early identification of a target group for future clinical trials on additional therapy to GnRH.
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- 2022
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20. Mortality in men with castration‐resistant prostate cancer—A long‐term follow‐up of a population‐based real‐world cohort
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Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, Aly, Markus, Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, and Aly, Markus
- Abstract
Objectives The objective of this study is to find clinical variables that predict the prognosis for men with castration-resistant prostate cancer (CRPC) in a Swedish real-life CRPC cohort, including a risk group classification to clarify the risk of succumbing to prostate cancer. This is a natural history cohort representing the premodern drug era before the introduction of novel hormonal drug therapies. Methods PSA tests from the clinical chemistry laboratories serving health care in six regions of Sweden were retrieved and cross-linked to the National Prostate Cancer Registry (NPCR) to identify men with a prostate cancer diagnosis. Through further cross-linking with data sources at the Swedish Board of Health and Welfare, we retrieved other relevant information such as prescribed drugs, hospitalizations, and cause of death. Men entered the CRPC cohort at the first date of doubling of their PSA nadir value with the last value being >2 ng/ml, or an absolute increase of >5 ng/ml or more, whilst on 3 months of medical castration or if they had been surgically castrated (n = 4098). By combining the two variables with the largest C-statistics, “PSA at time of CRPC” and “PSA doubling time,” a risk group classification was created. Rsults PSA-DT and PSA at date of CRPC are the strongest variables associated with PC specific survival. At the end of follow-up, the proportion of men who died due to PC was 57%, 71%, 81%, 86%, and 89% for risk categories one through five, respectively. The median overall survival in our cohort of men with CRPC was 1.86 years (95% CI: 1.79–1.97). Conclusion For a man with castration-resistant prostate cancer, there is a high probability that this will be the main cause contributing to his death. However, there is a significant difference in mortality that varies in relation to tumor burden assessed as PSA doubling time and PSA at time of CRCP. This information could be used in a clinical setting when deciding when to treat more or less ag
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- 2022
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21. Modeling Disease Trajectories for Castration-resistant Prostate Cancer Using Nationwide Population-based Data
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Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, Garmo, Hans, Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, and Garmo, Hans
- Abstract
Background: Little is known about disease trajectories for men with castration -resistant prostate cancer (CRPC).Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes.Design, setting, and participants: The model was generated using population -based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state.Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category.Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data.Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category.Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
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- 2022
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22. Mortality in men with castration‐resistant prostate cancer—A long‐term follow‐up of a population‐based real‐world cohort
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Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, Aly, Markus, Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, and Aly, Markus
- Abstract
Objectives The objective of this study is to find clinical variables that predict the prognosis for men with castration-resistant prostate cancer (CRPC) in a Swedish real-life CRPC cohort, including a risk group classification to clarify the risk of succumbing to prostate cancer. This is a natural history cohort representing the premodern drug era before the introduction of novel hormonal drug therapies. Methods PSA tests from the clinical chemistry laboratories serving health care in six regions of Sweden were retrieved and cross-linked to the National Prostate Cancer Registry (NPCR) to identify men with a prostate cancer diagnosis. Through further cross-linking with data sources at the Swedish Board of Health and Welfare, we retrieved other relevant information such as prescribed drugs, hospitalizations, and cause of death. Men entered the CRPC cohort at the first date of doubling of their PSA nadir value with the last value being >2 ng/ml, or an absolute increase of >5 ng/ml or more, whilst on 3 months of medical castration or if they had been surgically castrated (n = 4098). By combining the two variables with the largest C-statistics, “PSA at time of CRPC” and “PSA doubling time,” a risk group classification was created. Rsults PSA-DT and PSA at date of CRPC are the strongest variables associated with PC specific survival. At the end of follow-up, the proportion of men who died due to PC was 57%, 71%, 81%, 86%, and 89% for risk categories one through five, respectively. The median overall survival in our cohort of men with CRPC was 1.86 years (95% CI: 1.79–1.97). Conclusion For a man with castration-resistant prostate cancer, there is a high probability that this will be the main cause contributing to his death. However, there is a significant difference in mortality that varies in relation to tumor burden assessed as PSA doubling time and PSA at time of CRCP. This information could be used in a clinical setting when deciding when to treat more or less ag
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- 2022
- Full Text
- View/download PDF
23. Modeling Disease Trajectories for Castration-resistant Prostate Cancer Using Nationwide Population-based Data
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Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, Garmo, Hans, Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, and Garmo, Hans
- Abstract
Background: Little is known about disease trajectories for men with castration -resistant prostate cancer (CRPC).Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes.Design, setting, and participants: The model was generated using population -based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state.Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category.Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data.Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category.Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
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- 2022
- Full Text
- View/download PDF
24. Time to castration-resistant prostate cancer and prostate cancer death according to PSA response in men with non-metastatic prostate cancer treated with gonadotropin releasing hormone agonists
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Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, Robinson, David, Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, and Robinson, David
- Abstract
Objectives: To predict castration-resistant prostate cancer (CRPC) and prostate cancer (Pca) death by use of clinical variables at Pca diagnosis and PSA levels after start of gonadotropin-releasing hormone agonists (GnRH) in men with non-metastatic castration sensitive prostate cancer (nmCSPC). Materials and Methods: PSA values for 1603 men with nmCSPC in the National Prostate Cancer Register of Sweden who received GnRH as primary treatment were retrieved from Uppsala-Örebro PSA Cohort and Stockholm PSA and Biopsy Register. All men had measured PSA before (pre-GnRH PSA) and 3–6 months after (post-GnRH PSA) date of start of GnRH. Unadjusted and adjusted Cox models were used to predict CRPC by PSA levels. PSA levels and ISUP grade were used to construct a risk score to stratify men by tertiles according to risk of CRPC and Pca death. Results: 788 (49%) men reached CRPC and 456 (28%) died of Pca during follow-up. Post-GnRH PSA predicted CRPC regardless of pre-GnRH PSA. CRPC risk increased with higher post-GnRH PSA, HR 4.7 (95% CI: 3.4–6.7) for PSA > 16 ng/mL vs 0–0.25 ng/mL and with ISUP grade, HR 3.7 (95%: 2.5–5.4) for ISUP 5 vs ISUP 1. Risk of Pca death in men above top vs bellow bottom tertile of post-GnRH PSA and ISUP grade was HR 4.1 (95% CI: 3.0–5.5). Conclusion: A risk score based on post-GnRH PSA and ISUP grade could be used for early identification of a target group for future clinical trials on additional therapy to GnRH.
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- 2022
- Full Text
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25. Mortality in men with castration‐resistant prostate cancer—A long‐term follow‐up of a population‐based real‐world cohort
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Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, Aly, Markus, Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, and Aly, Markus
- Abstract
Objectives The objective of this study is to find clinical variables that predict the prognosis for men with castration-resistant prostate cancer (CRPC) in a Swedish real-life CRPC cohort, including a risk group classification to clarify the risk of succumbing to prostate cancer. This is a natural history cohort representing the premodern drug era before the introduction of novel hormonal drug therapies. Methods PSA tests from the clinical chemistry laboratories serving health care in six regions of Sweden were retrieved and cross-linked to the National Prostate Cancer Registry (NPCR) to identify men with a prostate cancer diagnosis. Through further cross-linking with data sources at the Swedish Board of Health and Welfare, we retrieved other relevant information such as prescribed drugs, hospitalizations, and cause of death. Men entered the CRPC cohort at the first date of doubling of their PSA nadir value with the last value being >2 ng/ml, or an absolute increase of >5 ng/ml or more, whilst on 3 months of medical castration or if they had been surgically castrated (n = 4098). By combining the two variables with the largest C-statistics, “PSA at time of CRPC” and “PSA doubling time,” a risk group classification was created. Rsults PSA-DT and PSA at date of CRPC are the strongest variables associated with PC specific survival. At the end of follow-up, the proportion of men who died due to PC was 57%, 71%, 81%, 86%, and 89% for risk categories one through five, respectively. The median overall survival in our cohort of men with CRPC was 1.86 years (95% CI: 1.79–1.97). Conclusion For a man with castration-resistant prostate cancer, there is a high probability that this will be the main cause contributing to his death. However, there is a significant difference in mortality that varies in relation to tumor burden assessed as PSA doubling time and PSA at time of CRCP. This information could be used in a clinical setting when deciding when to treat more or less ag
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- 2022
- Full Text
- View/download PDF
26. Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge
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Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, Eklund, Martin, Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin
- Abstract
Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]
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- 2022
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27. Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge
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Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, Eklund, Martin, Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin
- Abstract
Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]
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- 2022
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28. I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations.
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Files, Daniel Clark, Files, Daniel Clark, Matthay, Michael A, Calfee, Carolyn S, Aggarwal, Neil R, Asare, Adam L, Beitler, Jeremy R, Berger, Paul A, Burnham, Ellen L, Cimino, George, Coleman, Melissa H, Crippa, Alessio, Discacciati, Andrea, Gandotra, Sheetal, Gibbs, Kevin W, Henderson, Paul T, Ittner, Caroline AG, Jauregui, Alejandra, Khan, Kashif T, Koff, Jonathan L, Lang, Julie, LaRose, Mary, Levitt, Joe, Lu, Ruixiao, McKeehan, Jeffrey D, Meyer, Nuala J, Russell, Derek W, Thomas, Karl W, Eklund, Martin, Esserman, Laura J, Liu, Kathleen D, ISPY COVID Adaptive Platform Trial Network, undefined, Files, Daniel Clark, Files, Daniel Clark, Matthay, Michael A, Calfee, Carolyn S, Aggarwal, Neil R, Asare, Adam L, Beitler, Jeremy R, Berger, Paul A, Burnham, Ellen L, Cimino, George, Coleman, Melissa H, Crippa, Alessio, Discacciati, Andrea, Gandotra, Sheetal, Gibbs, Kevin W, Henderson, Paul T, Ittner, Caroline AG, Jauregui, Alejandra, Khan, Kashif T, Koff, Jonathan L, Lang, Julie, LaRose, Mary, Levitt, Joe, Lu, Ruixiao, McKeehan, Jeffrey D, Meyer, Nuala J, Russell, Derek W, Thomas, Karl W, Eklund, Martin, Esserman, Laura J, Liu, Kathleen D, ISPY COVID Adaptive Platform Trial Network, and undefined
- Abstract
IntroductionThe COVID-19 pandemic brought an urgent need to discover novel effective therapeutics for patients hospitalised with severe COVID-19. The Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis (ISPY COVID-19 trial) was designed and implemented in early 2020 to evaluate investigational agents rapidly and simultaneously on a phase 2 adaptive platform. This manuscript outlines the design, rationale, implementation and challenges of the ISPY COVID-19 trial during the first phase of trial activity from April 2020 until December 2021.Methods and analysisThe ISPY COVID-19 Trial is a multicentre open-label phase 2 platform trial in the USA designed to evaluate therapeutics that may have a large effect on improving outcomes from severe COVID-19. The ISPY COVID-19 Trial network includes academic and community hospitals with significant geographical diversity across the country. Enrolled patients are randomised to receive one of up to four investigational agents or a control and are evaluated for a family of two primary outcomes-time to recovery and mortality. The statistical design uses a Bayesian model with 'stopping' and 'graduation' criteria designed to efficiently discard ineffective therapies and graduate promising agents for definitive efficacy trials. Each investigational agent arm enrols to a maximum of 125 patients per arm and is compared with concurrent controls. As of December 2021, 11 investigational agent arms had been activated, and 8 arms were complete. Enrolment and adaptation of the trial design are ongoing.Ethics and disseminationISPY COVID-19 operates under a central institutional review board via Wake Forest School of Medicine IRB00066805. Data generated from this trial will be reported in peer-reviewed medical journals.Trial registration numberNCT04488081.
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- 2022
29. Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
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Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, Eklund, Martin, Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, and Eklund, Martin
- Abstract
Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.
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- 2022
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30. Modeling Disease Trajectories for Castration-resistant Prostate Cancer Using Nationwide Population-based Data
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Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, Garmo, Hans, Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, and Garmo, Hans
- Abstract
Background: Little is known about disease trajectories for men with castration -resistant prostate cancer (CRPC).Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes.Design, setting, and participants: The model was generated using population -based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state.Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category.Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data.Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category.Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
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- 2022
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31. Mortality in men with castration‐resistant prostate cancer—A long‐term follow‐up of a population‐based real‐world cohort
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Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, Aly, Markus, Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, and Aly, Markus
- Abstract
Objectives The objective of this study is to find clinical variables that predict the prognosis for men with castration-resistant prostate cancer (CRPC) in a Swedish real-life CRPC cohort, including a risk group classification to clarify the risk of succumbing to prostate cancer. This is a natural history cohort representing the premodern drug era before the introduction of novel hormonal drug therapies. Methods PSA tests from the clinical chemistry laboratories serving health care in six regions of Sweden were retrieved and cross-linked to the National Prostate Cancer Registry (NPCR) to identify men with a prostate cancer diagnosis. Through further cross-linking with data sources at the Swedish Board of Health and Welfare, we retrieved other relevant information such as prescribed drugs, hospitalizations, and cause of death. Men entered the CRPC cohort at the first date of doubling of their PSA nadir value with the last value being >2 ng/ml, or an absolute increase of >5 ng/ml or more, whilst on 3 months of medical castration or if they had been surgically castrated (n = 4098). By combining the two variables with the largest C-statistics, “PSA at time of CRPC” and “PSA doubling time,” a risk group classification was created. Rsults PSA-DT and PSA at date of CRPC are the strongest variables associated with PC specific survival. At the end of follow-up, the proportion of men who died due to PC was 57%, 71%, 81%, 86%, and 89% for risk categories one through five, respectively. The median overall survival in our cohort of men with CRPC was 1.86 years (95% CI: 1.79–1.97). Conclusion For a man with castration-resistant prostate cancer, there is a high probability that this will be the main cause contributing to his death. However, there is a significant difference in mortality that varies in relation to tumor burden assessed as PSA doubling time and PSA at time of CRCP. This information could be used in a clinical setting when deciding when to treat more or less ag
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- 2022
- Full Text
- View/download PDF
32. Time to castration-resistant prostate cancer and prostate cancer death according to PSA response in men with non-metastatic prostate cancer treated with gonadotropin releasing hormone agonists
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Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, Robinson, David, Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, and Robinson, David
- Abstract
Objectives: To predict castration-resistant prostate cancer (CRPC) and prostate cancer (Pca) death by use of clinical variables at Pca diagnosis and PSA levels after start of gonadotropin-releasing hormone agonists (GnRH) in men with non-metastatic castration sensitive prostate cancer (nmCSPC). Materials and Methods: PSA values for 1603 men with nmCSPC in the National Prostate Cancer Register of Sweden who received GnRH as primary treatment were retrieved from Uppsala-Örebro PSA Cohort and Stockholm PSA and Biopsy Register. All men had measured PSA before (pre-GnRH PSA) and 3–6 months after (post-GnRH PSA) date of start of GnRH. Unadjusted and adjusted Cox models were used to predict CRPC by PSA levels. PSA levels and ISUP grade were used to construct a risk score to stratify men by tertiles according to risk of CRPC and Pca death. Results: 788 (49%) men reached CRPC and 456 (28%) died of Pca during follow-up. Post-GnRH PSA predicted CRPC regardless of pre-GnRH PSA. CRPC risk increased with higher post-GnRH PSA, HR 4.7 (95% CI: 3.4–6.7) for PSA > 16 ng/mL vs 0–0.25 ng/mL and with ISUP grade, HR 3.7 (95%: 2.5–5.4) for ISUP 5 vs ISUP 1. Risk of Pca death in men above top vs bellow bottom tertile of post-GnRH PSA and ISUP grade was HR 4.1 (95% CI: 3.0–5.5). Conclusion: A risk score based on post-GnRH PSA and ISUP grade could be used for early identification of a target group for future clinical trials on additional therapy to GnRH.
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- 2022
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33. Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
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Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, Eklund, Martin, Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, and Eklund, Martin
- Abstract
Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.
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- 2022
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34. Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
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Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, Eklund, Martin, Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, and Eklund, Martin
- Abstract
Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.
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- 2022
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35. A case-case analysis of women with breast cancer: predictors of interval vs screen-detected cancer.
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Dreher, Nickolas, Dreher, Nickolas, Matthys, Madeline, Hadeler, Edward, Shieh, Yiwey, Acerbi, Irene, McAuley, Fiona M, Melisko, Michelle, Eklund, Martin, Tice, Jeffrey A, Esserman, Laura J, Veer, Laura J Van't, Dreher, Nickolas, Dreher, Nickolas, Matthys, Madeline, Hadeler, Edward, Shieh, Yiwey, Acerbi, Irene, McAuley, Fiona M, Melisko, Michelle, Eklund, Martin, Tice, Jeffrey A, Esserman, Laura J, and Veer, Laura J Van't
- Abstract
PurposeThe Breast Cancer Surveillance Consortium (BCSC) model is a widely used risk model that predicts 5- and 10-year risk of developing invasive breast cancer for healthy women aged 35-74 years. Women with high BCSC risk may also be at elevated risk to develop interval cancers, which present symptomatically in the year following a normal screening mammogram. We examined the association between high BCSC risk (defined as the top 2.5% by age) and breast cancers presenting as interval cancers.MethodsWe conducted a case-case analysis among women with breast cancer in which we compared the mode of detection and tumor characteristics of patients in the top 2.5% BCSC risk by age with age-matched (1:2) patients in the lower 97.5% risk. We constructed logistic regression models to estimate the odds ratio (OR) of presenting with interval cancers, and poor prognosis tumor features, between women from the top 2.5% and bottom 97.5% of BCSC risk.ResultsOur analysis included 113 breast cancer patients in the top 2.5% of risk for their age and 226 breast cancer patients in the lower 97.5% of risk. High-risk patients were more likely to have presented with an interval cancer within one year of a normal screening, OR 6.62 (95% CI 3.28-13.4, p < 0.001). These interval cancers were also more likely to be larger, node positive, and higher stage than the screen-detected cancers.ConclusionBreast cancer patients in the top 2.5% of BCSC risk for their age were more likely to present with interval cancers. The BCSC model could be used to identify healthy women who may benefit from intensified screening.
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- 2022
36. Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
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Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, Eklund, Martin, Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, and Eklund, Martin
- Abstract
Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.
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- 2022
- Full Text
- View/download PDF
37. Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge
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Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, Eklund, Martin, Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin
- Abstract
Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]
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- 2022
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38. Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
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Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, Eklund, Martin, Olsson, Henrik, Kartasalo, Kimmo, Mulliqi, Nita, Capuccini, Marco, Ruusuvuori, Pekka, Samaratunga, Hemamali, Delahunt, Brett, Lindskog, Cecilia, Janssen, Emiel A. M., Blilie, Anders, Egevad, Lars, Spjuth, Ola, and Eklund, Martin
- Abstract
Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.
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- 2022
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39. A Head-to-head Comparison of Prostate Cancer Diagnostic Strategies Using the Stockholm3 Test, Magnetic Resonance Imaging, and Swedish National Guidelines : Results from a Prospective Population-based Screening Study
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Waldén, Mauritz, Aldrimer, Mattias, Heydorn Lagerlöf, Jakob, Eklund, Martin, Grönberg, Henrik, Nordström, Tobias, Palsdottir, Thorgerdur, Waldén, Mauritz, Aldrimer, Mattias, Heydorn Lagerlöf, Jakob, Eklund, Martin, Grönberg, Henrik, Nordström, Tobias, and Palsdottir, Thorgerdur
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Background: Strategies for early detection of prostate cancer aim to detect clinically significant prostate cancer (csPCa) and avoid detection of insignificant cancers and unnecessary biopsies. Swedish national guidelines (SNGs), years 2019 and 2020, involve prostate-specific antigen (PSA) testing, clinical variables, and magnetic resonance imaging (MRI). The Stockholm3 test and MRI have been suggested to improve selection of men for prostate biopsy. Performance of SNGs compared with the Stockholm3 test or MRI in a screening setting is unclear. Objective: To compare strategies based on previous and current national guidelines, Stockholm3, and MRI to select patients for biopsy in a screening-by-invitation setting. Design setting and participants: All participants underwent PSA test, and men with PSA ≥3 ng/ml underwent Stockholm3 testing and MRI. Men with Stockholm3 ≥11%, Prostate Imaging Reporting and Data System score ≥3 on MRI, or indication according to SNG-2019 or SNG-2020 were referred to biopsy. Outcome measurements and statistical analysis: The primary outcome was the detection of csPCa at prostate biopsy, defined as an International Society of Urological Pathology (ISUP) grade of ≥2. Results and limitations: We invited 8764 men from the general population, 272 of whom had PSA ≥3 ng/ml. The median PSA was 4.1 (interquartile range: 3.4-5.8), and 136 of 270 (50%) who underwent MRI lacked any pathological lesions. In total, 37 csPCa cases were diagnosed. Using SNG-2019, 36 csPCa cases with a high biopsy rate (179 of 272) were detected and 49 were diagnosed with ISUP 1 cancers. The Stockholm3 strategy diagnosed 32 csPCa cases, with 89 biopsied and 27 ISUP 1 cancers. SNG-2020 detected 32 csPCa and 33 ISUP 1 cancer patients, with 99 men biopsied, and the MRI strategy detected 30 csPCa and 35 ISUP 1 cancer cases by biopsying 123 men. The latter two strategies generated more MRI scans than the Stockholm3 strategy (n = 270 vs 33). Conclusions: Previous guidelines provi
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- 2022
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40. Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge
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Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, Eklund, Martin, Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin
- Abstract
Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]
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- 2022
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41. External Validation of the Prostate Biopsy Collaborative Group Risk Calculator and the Rotterdam Prostate Cancer Risk Calculator in a Swedish Population-based Screening Cohort
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Chandra Engel, Jan, Palsdottir, Thorgerdur, Ankerst, Donna, Remmers, Sebastiaan, Mortezavi, Ashkan, Chellappa, Venkatesh, Egevad, Lars, Grönberg, Henrik, Eklund, Martin, Nordström, Tobias, Chandra Engel, Jan, Palsdottir, Thorgerdur, Ankerst, Donna, Remmers, Sebastiaan, Mortezavi, Ashkan, Chellappa, Venkatesh, Egevad, Lars, Grönberg, Henrik, Eklund, Martin, and Nordström, Tobias
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Background: External validation of risk calculators (RCs) is necessary to determine their clinical applicability beyond the setting in which these were developed. Objective: To assess the performance of the Rotterdam Prostate Cancer RC (RPCRC) and the Prostate Biopsy Collaborative Group RC (PBCG-RC). Design, setting, and participants: We used data from the prospective, population-based STHLM3 screening study, performed in 2012–2015. Participants with prostate-specific antigen ≥3 ng/ml who underwent systematic prostate biopsies were included. Outcome measurements and statistical analysis: Probabilities for clinically significant prostate cancer (csPCa), defined as International Society of Urological Pathology grade ≥2, were calculated for each participant. External validity was assessed by calibration, discrimination, and clinical usefulness for both original and recalibrated models. Results and limitations: Out of 5841 men, 1054 (18%) had csPCa. Distribution of risk predictions differed between RCs; median risks for csPCa using the RPCRC and PBCG-RC were 3.3% (interquartile range [IQR] 2.1–7.1%) and 20% (IQR 15–28%), respectively. The correlation between RC risk estimates on individual level was moderate (Spearman's r = 0.55). Using the RPCRC's recommended risk threshold of ≥4% for finding csPCa, 36% of participants would get concordant biopsy recommendations. At 10% risk cut-off, RCs agreed in 23% of cases. Both RCs showed good discrimination, with areas under the curves for the RPCRC of 0.74 (95% confidence interval [CI] 0.72–0.76) and the PBCG-RC of 0.70 (95% CI 0.68–0.72). Calibration was adequate using the PBCG-RC (calibration slope: 1.13 [95% CI 1.03–1.23]), but the RPCRC underestimated the risk of csPCa (calibration slope: 0.73 [0.68–0.79]). The PBCG-RC showed a net benefit in a decision curve analysis, whereas the RPCRC showed no net benefit at clinically relevant risk threshold levels. Recalibration improved clinical benefit, and differences between RCs
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- 2022
42. Time to castration-resistant prostate cancer and prostate cancer death according to PSA response in men with non-metastatic prostate cancer treated with gonadotropin releasing hormone agonists
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Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, Robinson, David, Bonde, Tiago M., Westerberg, Marcus, Aly, Markus, Eklund, Martin, Adolfsson, Jan, Bill-Axelson, Anna, Garmo, Hans, Stattin, Pär, and Robinson, David
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Objectives: To predict castration-resistant prostate cancer (CRPC) and prostate cancer (Pca) death by use of clinical variables at Pca diagnosis and PSA levels after start of gonadotropin-releasing hormone agonists (GnRH) in men with non-metastatic castration sensitive prostate cancer (nmCSPC). Materials and Methods: PSA values for 1603 men with nmCSPC in the National Prostate Cancer Register of Sweden who received GnRH as primary treatment were retrieved from Uppsala-Örebro PSA Cohort and Stockholm PSA and Biopsy Register. All men had measured PSA before (pre-GnRH PSA) and 3–6 months after (post-GnRH PSA) date of start of GnRH. Unadjusted and adjusted Cox models were used to predict CRPC by PSA levels. PSA levels and ISUP grade were used to construct a risk score to stratify men by tertiles according to risk of CRPC and Pca death. Results: 788 (49%) men reached CRPC and 456 (28%) died of Pca during follow-up. Post-GnRH PSA predicted CRPC regardless of pre-GnRH PSA. CRPC risk increased with higher post-GnRH PSA, HR 4.7 (95% CI: 3.4–6.7) for PSA > 16 ng/mL vs 0–0.25 ng/mL and with ISUP grade, HR 3.7 (95%: 2.5–5.4) for ISUP 5 vs ISUP 1. Risk of Pca death in men above top vs bellow bottom tertile of post-GnRH PSA and ISUP grade was HR 4.1 (95% CI: 3.0–5.5). Conclusion: A risk score based on post-GnRH PSA and ISUP grade could be used for early identification of a target group for future clinical trials on additional therapy to GnRH.
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- 2022
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43. Mortality in men with castration‐resistant prostate cancer—A long‐term follow‐up of a population‐based real‐world cohort
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Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, Aly, Markus, Khoshkar, Yashar, Westerberg, Marcus, Adolfsson, Jan, Bill-Axelson, Anna, Olsson, Henrik, Eklund, Martin, Akre, Olof, Garmo, Hans, and Aly, Markus
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Objectives The objective of this study is to find clinical variables that predict the prognosis for men with castration-resistant prostate cancer (CRPC) in a Swedish real-life CRPC cohort, including a risk group classification to clarify the risk of succumbing to prostate cancer. This is a natural history cohort representing the premodern drug era before the introduction of novel hormonal drug therapies. Methods PSA tests from the clinical chemistry laboratories serving health care in six regions of Sweden were retrieved and cross-linked to the National Prostate Cancer Registry (NPCR) to identify men with a prostate cancer diagnosis. Through further cross-linking with data sources at the Swedish Board of Health and Welfare, we retrieved other relevant information such as prescribed drugs, hospitalizations, and cause of death. Men entered the CRPC cohort at the first date of doubling of their PSA nadir value with the last value being >2 ng/ml, or an absolute increase of >5 ng/ml or more, whilst on 3 months of medical castration or if they had been surgically castrated (n = 4098). By combining the two variables with the largest C-statistics, “PSA at time of CRPC” and “PSA doubling time,” a risk group classification was created. Rsults PSA-DT and PSA at date of CRPC are the strongest variables associated with PC specific survival. At the end of follow-up, the proportion of men who died due to PC was 57%, 71%, 81%, 86%, and 89% for risk categories one through five, respectively. The median overall survival in our cohort of men with CRPC was 1.86 years (95% CI: 1.79–1.97). Conclusion For a man with castration-resistant prostate cancer, there is a high probability that this will be the main cause contributing to his death. However, there is a significant difference in mortality that varies in relation to tumor burden assessed as PSA doubling time and PSA at time of CRCP. This information could be used in a clinical setting when deciding when to treat more or less ag
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- 2022
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44. Modeling Disease Trajectories for Castration-resistant Prostate Cancer Using Nationwide Population-based Data
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Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, Garmo, Hans, Ventimiglia, Eugenio, Bill-Axelson, Anna, Adolfsson, Jan, Aly, Markus, Eklund, Martin, Westerberg, Marcus, Stattin, Pär, and Garmo, Hans
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Background: Little is known about disease trajectories for men with castration -resistant prostate cancer (CRPC).Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes.Design, setting, and participants: The model was generated using population -based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state.Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category.Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data.Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category.Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
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- 2022
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45. Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge
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Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, Eklund, Martin, Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin
- Abstract
Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]
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- 2022
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46. Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
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Bulten, Wouter, Balkenhol, Maschenka, Belinga, Jean-Joel Awoumou, Brilhante, Americo, Cakir, Asli, Egevad, Lars, Eklund, Martin, Farre, Xavier, Geronatsiou, Katerina, Molinie, Vincent, Pereira, Guilherme, Roy, Paromita, Saile, Gunter, Salles, Paulo, Schaafsma, Ewout, Tschui, Joelle, Vos, Anne-Marie, van Boven, Hester, Vink, Robert, van der Laak, Jeroen, Hulsbergen-van der Kaa, Christina, Litjens, Geert, Bulten, Wouter, Balkenhol, Maschenka, Belinga, Jean-Joel Awoumou, Brilhante, Americo, Cakir, Asli, Egevad, Lars, Eklund, Martin, Farre, Xavier, Geronatsiou, Katerina, Molinie, Vincent, Pereira, Guilherme, Roy, Paromita, Saile, Gunter, Salles, Paulo, Schaafsma, Ewout, Tschui, Joelle, Vos, Anne-Marie, van Boven, Hester, Vink, Robert, van der Laak, Jeroen, Hulsbergen-van der Kaa, Christina, and Litjens, Geert
- Abstract
The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohens kappa, 0.799 vs. 0.872;p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohens kappa, 0.733 vs. 0.786;p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy., Funding Agencies|Dutch Cancer Society (KWF)KWF Kankerbestrijding [KUN 2015-7970]
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- 2021
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47. Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
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Bulten, Wouter, Balkenhol, Maschenka, Belinga, Jean-Joel Awoumou, Brilhante, Americo, Cakir, Asli, Egevad, Lars, Eklund, Martin, Farre, Xavier, Geronatsiou, Katerina, Molinie, Vincent, Pereira, Guilherme, Roy, Paromita, Saile, Gunter, Salles, Paulo, Schaafsma, Ewout, Tschui, Joelle, Vos, Anne-Marie, van Boven, Hester, Vink, Robert, van der Laak, Jeroen, Hulsbergen-van der Kaa, Christina, Litjens, Geert, Bulten, Wouter, Balkenhol, Maschenka, Belinga, Jean-Joel Awoumou, Brilhante, Americo, Cakir, Asli, Egevad, Lars, Eklund, Martin, Farre, Xavier, Geronatsiou, Katerina, Molinie, Vincent, Pereira, Guilherme, Roy, Paromita, Saile, Gunter, Salles, Paulo, Schaafsma, Ewout, Tschui, Joelle, Vos, Anne-Marie, van Boven, Hester, Vink, Robert, van der Laak, Jeroen, Hulsbergen-van der Kaa, Christina, and Litjens, Geert
- Abstract
The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohens kappa, 0.799 vs. 0.872;p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohens kappa, 0.733 vs. 0.786;p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy., Funding Agencies|Dutch Cancer Society (KWF)KWF Kankerbestrijding [KUN 2015-7970]
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- 2021
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48. Personalized breast cancer screening in a population-based study: Women informed to screen depending on measures of risk (WISDOM)
- Author
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Acerbi, Irene, Acerbi, Irene, Fiscalini, Allison Stover, Che, Mandy, Shieh, Yiwey, Madlensky, Lisa, Tice, Jeffrey, Ziv, Elad, Eklund, Martin, Blanco, Amie, Tong, Barry, Goodman, Deborah, Nassereddine, Lamees, Anderson, Nancy, Harvey, Heather, Fors, Steele, Park, Hannah L, Petruse, Antonia, Stewart, Skye, Wernisch, Janet, Risty, Larissa, Hurley, Ian, Koenig, Barbara, Kaplan, Celia, Hiatt, Robert, Wenger, Neil, Lee, Vivian, Heditsian, Diane, Brain, Susie, Sabacan, Leah, Wang, Tianyi, Parker, Barbara A, Borowsky, Alexander, Anton-Culver, Hoda, Naeim, Arash, Kaster, Andrea, Talley, Melinda, van 't Veer, Laura, LaCroix, Andrea Z, Olopade, Olufunmilayo I, Sheth, Deepa, Garcia, Augustin, Lancaster, Rachel, Esserman, Laura, Hlth, Wisdom Study Athena Breast, Acerbi, Irene, Acerbi, Irene, Fiscalini, Allison Stover, Che, Mandy, Shieh, Yiwey, Madlensky, Lisa, Tice, Jeffrey, Ziv, Elad, Eklund, Martin, Blanco, Amie, Tong, Barry, Goodman, Deborah, Nassereddine, Lamees, Anderson, Nancy, Harvey, Heather, Fors, Steele, Park, Hannah L, Petruse, Antonia, Stewart, Skye, Wernisch, Janet, Risty, Larissa, Hurley, Ian, Koenig, Barbara, Kaplan, Celia, Hiatt, Robert, Wenger, Neil, Lee, Vivian, Heditsian, Diane, Brain, Susie, Sabacan, Leah, Wang, Tianyi, Parker, Barbara A, Borowsky, Alexander, Anton-Culver, Hoda, Naeim, Arash, Kaster, Andrea, Talley, Melinda, van 't Veer, Laura, LaCroix, Andrea Z, Olopade, Olufunmilayo I, Sheth, Deepa, Garcia, Augustin, Lancaster, Rachel, Esserman, Laura, and Hlth, Wisdom Study Athena Breast
- Published
- 2021
49. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction
- Author
-
Conti, David V., Darst, Burcu F., Moss, Lilit C., Saunders, Edward J., Sheng, Xin, Chou, Alisha, Schumacher, Fredrick R., Olama, Ali Amin Al, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N., Sahimi, Ali, Hoffmann, Thomas J., Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Muir, Kenneth, Lophatananon, Artitaya, Wan, Peggy, Le Marchand, Loic, Wilkens, Lynne R., Stevens, Victoria L., Gapstur, Susan M., Carter, Brian D., Schleutker, Johanna, Tammela, Teuvo L.J., Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G., Southey, Melissa C., MacInnis, Robert J., Cybulski, Cezary, Wokołorczyk, Dominika, Lubiński, Jan, Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Martin, Richard M., Nordestgaard, Børge G., Nielsen, Sune F., Weischer, Maren, Bojesen, Stig E., Røder, Martin Andreas, Iversen, Peter, Batra, Jyotsna, Chambers, Suzanne, Moya, Leire, Horvath, Lisa, Clements, Judith A., Tilley, Wayne, Risbridger, Gail P., Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordström, Tobias, Pashayan, Nora, Dunning, Alison M., Ghoussaini, Maya, Travis, Ruth C., Key, Tim J., Riboli, Elio, Park, Jong Y., Sellers, Thomas A., Lin, Hui Yi, Albanes, Demetrius, Weinstein, Stephanie J., Mucci, Lorelei A., Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J., Penney, Kathryn L., Turman, Constance, Tangen, Catherine M., Goodman, Phyllis J., Thompson, Ian M., Hamilton, Robert J., Fleshner, Neil E., Finelli, Antonio, Parent, Marie Élise, Stanford, Janet L., Ostrander, Elaine A., Geybels, Milan S., Koutros, Stella, Freeman, Laura E.Beane, Stampfer, Meir, Wolk, Alicja, Håkansson, Niclas, Andriole, Gerald L., Hoover, Robert N., Machiela, Mitchell J., Sørensen, Karina Dalsgaard, Borre, Michael, Blot, William J., Zheng, Wei, Yeboah, Edward D., Mensah, James E., Lu, Yong Jie, Zhang, Hong Wei, Feng, Ninghan, Mao, Xueying, Wu, Yudong, Zhao, Shan Chao, Sun, Zan, Thibodeau, Stephen N., McDonnell, Shannon K., Schaid, Daniel J., West, Catharine M.L., Burnet, Neil, Barnett, Gill, Maier, Christiane, Schnoeller, Thomas, Luedeke, Manuel, Kibel, Adam S., Drake, Bettina F., Cussenot, Olivier, Cancel-Tassin, Géraldine, Menegaux, Florence, Truong, Thérèse, Koudou, Yves Akoli, John, Esther M., Grindedal, Eli Marie, Maehle, Lovise, Khaw, Kay Tee, Ingles, Sue A., Stern, Mariana C., Vega, Ana, Gómez-Caamaño, Antonio, Fachal, Laura, Rosenstein, Barry S., Kerns, Sarah L., Ostrer, Harry, Teixeira, Manuel R., Paulo, Paula, Brandão, Andreia, Watya, Stephen, Lubwama, Alexander, Bensen, Jeannette T., Fontham, Elizabeth T.H., Mohler, James, Taylor, Jack A., Kogevinas, Manolis, Llorca, Javier, Castaño-Vinyals, Gemma, Cannon-Albright, Lisa, Teerlink, Craig C., Huff, Chad D., Strom, Sara S., Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Kaneva, Radka, Slavov, Chavdar, Mitev, Vanio, Leach, Robin J., Weaver, Brandi, Brenner, Hermann, Cuk, Katarina, Holleczek, Bernd, Saum, Kai Uwe, Klein, Eric A., Hsing, Ann W., Kittles, Rick A., Murphy, Adam B., Logothetis, Christopher J., Kim, Jeri, Neuhausen, Susan L., Steele, Linda, Ding, Yuan Chun, Isaacs, William B., Nemesure, Barbara, Hennis, Anselm J.M., Carpten, John, Pandha, Hardev, Michael, Agnieszka, De Ruyck, Kim, De Meerleer, Gert, Ost, Piet, Xu, Jianfeng, Razack, Azad, Lim, Jasmine, Teo, Soo Hwang, Newcomb, Lisa F., Lin, Daniel W., Fowke, Jay H., Neslund-Dudas, Christine, Rybicki, Benjamin A., Gamulin, Marija, Lessel, Davor, Kulis, Tomislav, Usmani, Nawaid, Singhal, Sandeep, Parliament, Matthew, Claessens, Frank, Joniau, Steven, Van den Broeck, Thomas, Gago-Dominguez, Manuela, Castelao, Jose Esteban, Martinez, Maria Elena, Larkin, Samantha, Townsend, Paul A., Aukim-Hastie, Claire, Bush, William S., Aldrich, Melinda C., Crawford, Dana C., Srivastava, Shiv, Cullen, Jennifer C., Petrovics, Gyorgy, Casey, Graham, Roobol, Monique J., Jenster, Guido, van Schaik, Ron H.N., Hu, Jennifer J., Sanderson, Maureen, Varma, Rohit, McKean-Cowdin, Roberta, Torres, Mina, Mancuso, Nicholas, Berndt, Sonja I., Van Den Eeden, Stephen K., Easton, Douglas F., Chanock, Stephen J., Cook, Michael B., Wiklund, Fredrik, Nakagawa, Hidewaki, Witte, John S., Eeles, Rosalind A., Kote-Jarai, Zsofia, Haiman, Christopher A., Conti, David V., Darst, Burcu F., Moss, Lilit C., Saunders, Edward J., Sheng, Xin, Chou, Alisha, Schumacher, Fredrick R., Olama, Ali Amin Al, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N., Sahimi, Ali, Hoffmann, Thomas J., Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Muir, Kenneth, Lophatananon, Artitaya, Wan, Peggy, Le Marchand, Loic, Wilkens, Lynne R., Stevens, Victoria L., Gapstur, Susan M., Carter, Brian D., Schleutker, Johanna, Tammela, Teuvo L.J., Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G., Southey, Melissa C., MacInnis, Robert J., Cybulski, Cezary, Wokołorczyk, Dominika, Lubiński, Jan, Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Martin, Richard M., Nordestgaard, Børge G., Nielsen, Sune F., Weischer, Maren, Bojesen, Stig E., Røder, Martin Andreas, Iversen, Peter, Batra, Jyotsna, Chambers, Suzanne, Moya, Leire, Horvath, Lisa, Clements, Judith A., Tilley, Wayne, Risbridger, Gail P., Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordström, Tobias, Pashayan, Nora, Dunning, Alison M., Ghoussaini, Maya, Travis, Ruth C., Key, Tim J., Riboli, Elio, Park, Jong Y., Sellers, Thomas A., Lin, Hui Yi, Albanes, Demetrius, Weinstein, Stephanie J., Mucci, Lorelei A., Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J., Penney, Kathryn L., Turman, Constance, Tangen, Catherine M., Goodman, Phyllis J., Thompson, Ian M., Hamilton, Robert J., Fleshner, Neil E., Finelli, Antonio, Parent, Marie Élise, Stanford, Janet L., Ostrander, Elaine A., Geybels, Milan S., Koutros, Stella, Freeman, Laura E.Beane, Stampfer, Meir, Wolk, Alicja, Håkansson, Niclas, Andriole, Gerald L., Hoover, Robert N., Machiela, Mitchell J., Sørensen, Karina Dalsgaard, Borre, Michael, Blot, William J., Zheng, Wei, Yeboah, Edward D., Mensah, James E., Lu, Yong Jie, Zhang, Hong Wei, Feng, Ninghan, Mao, Xueying, Wu, Yudong, Zhao, Shan Chao, Sun, Zan, Thibodeau, Stephen N., McDonnell, Shannon K., Schaid, Daniel J., West, Catharine M.L., Burnet, Neil, Barnett, Gill, Maier, Christiane, Schnoeller, Thomas, Luedeke, Manuel, Kibel, Adam S., Drake, Bettina F., Cussenot, Olivier, Cancel-Tassin, Géraldine, Menegaux, Florence, Truong, Thérèse, Koudou, Yves Akoli, John, Esther M., Grindedal, Eli Marie, Maehle, Lovise, Khaw, Kay Tee, Ingles, Sue A., Stern, Mariana C., Vega, Ana, Gómez-Caamaño, Antonio, Fachal, Laura, Rosenstein, Barry S., Kerns, Sarah L., Ostrer, Harry, Teixeira, Manuel R., Paulo, Paula, Brandão, Andreia, Watya, Stephen, Lubwama, Alexander, Bensen, Jeannette T., Fontham, Elizabeth T.H., Mohler, James, Taylor, Jack A., Kogevinas, Manolis, Llorca, Javier, Castaño-Vinyals, Gemma, Cannon-Albright, Lisa, Teerlink, Craig C., Huff, Chad D., Strom, Sara S., Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Kaneva, Radka, Slavov, Chavdar, Mitev, Vanio, Leach, Robin J., Weaver, Brandi, Brenner, Hermann, Cuk, Katarina, Holleczek, Bernd, Saum, Kai Uwe, Klein, Eric A., Hsing, Ann W., Kittles, Rick A., Murphy, Adam B., Logothetis, Christopher J., Kim, Jeri, Neuhausen, Susan L., Steele, Linda, Ding, Yuan Chun, Isaacs, William B., Nemesure, Barbara, Hennis, Anselm J.M., Carpten, John, Pandha, Hardev, Michael, Agnieszka, De Ruyck, Kim, De Meerleer, Gert, Ost, Piet, Xu, Jianfeng, Razack, Azad, Lim, Jasmine, Teo, Soo Hwang, Newcomb, Lisa F., Lin, Daniel W., Fowke, Jay H., Neslund-Dudas, Christine, Rybicki, Benjamin A., Gamulin, Marija, Lessel, Davor, Kulis, Tomislav, Usmani, Nawaid, Singhal, Sandeep, Parliament, Matthew, Claessens, Frank, Joniau, Steven, Van den Broeck, Thomas, Gago-Dominguez, Manuela, Castelao, Jose Esteban, Martinez, Maria Elena, Larkin, Samantha, Townsend, Paul A., Aukim-Hastie, Claire, Bush, William S., Aldrich, Melinda C., Crawford, Dana C., Srivastava, Shiv, Cullen, Jennifer C., Petrovics, Gyorgy, Casey, Graham, Roobol, Monique J., Jenster, Guido, van Schaik, Ron H.N., Hu, Jennifer J., Sanderson, Maureen, Varma, Rohit, McKean-Cowdin, Roberta, Torres, Mina, Mancuso, Nicholas, Berndt, Sonja I., Van Den Eeden, Stephen K., Easton, Douglas F., Chanock, Stephen J., Cook, Michael B., Wiklund, Fredrik, Nakagawa, Hidewaki, Witte, John S., Eeles, Rosalind A., Kote-Jarai, Zsofia, and Haiman, Christopher A.
- Abstract
Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84–5.29) for men of European ancestry to 3.74 (95% CI, 3.36–4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14–2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71–0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.
- Published
- 2021
50. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.
- Author
-
Conti, David V, Conti, David V, Darst, Burcu F, Moss, Lilit C, Saunders, Edward J, Sheng, Xin, Chou, Alisha, Schumacher, Fredrick R, Olama, Ali Amin Al, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N, Sahimi, Ali, Hoffmann, Thomas J, Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Muir, Kenneth, Lophatananon, Artitaya, Wan, Peggy, Le Marchand, Loic, Wilkens, Lynne R, Stevens, Victoria L, Gapstur, Susan M, Carter, Brian D, Schleutker, Johanna, Tammela, Teuvo LJ, Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G, Southey, Melissa C, MacInnis, Robert J, Cybulski, Cezary, Wokołorczyk, Dominika, Lubiński, Jan, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Martin, Richard M, Nordestgaard, Børge G, Nielsen, Sune F, Weischer, Maren, Bojesen, Stig E, Røder, Martin Andreas, Iversen, Peter, Batra, Jyotsna, Chambers, Suzanne, Moya, Leire, Horvath, Lisa, Clements, Judith A, Tilley, Wayne, Risbridger, Gail P, Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordström, Tobias, Pashayan, Nora, Dunning, Alison M, Ghoussaini, Maya, Travis, Ruth C, Key, Tim J, Riboli, Elio, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie J, Mucci, Lorelei A, Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J, Penney, Kathryn L, Turman, Constance, Tangen, Catherine M, Goodman, Phyllis J, Thompson, Ian M, Hamilton, Robert J, Fleshner, Neil E, Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet L, Ostrander, Elaine A, Geybels, Milan S, Koutros, Stella, Freeman, Laura E Beane, Stampfer, Meir, Wolk, Alicja, Håkansson, Niclas, Andriole, Gerald L, Hoover, Robert N, Machiela, Mitchell J, Sørensen, Karina Dalsgaard, Borre, Michael, Blot, William J, Zheng, Wei, Yeboah, Edward D, Mensah, James E, Lu, Yong-Jie, Conti, David V, Conti, David V, Darst, Burcu F, Moss, Lilit C, Saunders, Edward J, Sheng, Xin, Chou, Alisha, Schumacher, Fredrick R, Olama, Ali Amin Al, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N, Sahimi, Ali, Hoffmann, Thomas J, Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Muir, Kenneth, Lophatananon, Artitaya, Wan, Peggy, Le Marchand, Loic, Wilkens, Lynne R, Stevens, Victoria L, Gapstur, Susan M, Carter, Brian D, Schleutker, Johanna, Tammela, Teuvo LJ, Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G, Southey, Melissa C, MacInnis, Robert J, Cybulski, Cezary, Wokołorczyk, Dominika, Lubiński, Jan, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Martin, Richard M, Nordestgaard, Børge G, Nielsen, Sune F, Weischer, Maren, Bojesen, Stig E, Røder, Martin Andreas, Iversen, Peter, Batra, Jyotsna, Chambers, Suzanne, Moya, Leire, Horvath, Lisa, Clements, Judith A, Tilley, Wayne, Risbridger, Gail P, Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordström, Tobias, Pashayan, Nora, Dunning, Alison M, Ghoussaini, Maya, Travis, Ruth C, Key, Tim J, Riboli, Elio, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie J, Mucci, Lorelei A, Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J, Penney, Kathryn L, Turman, Constance, Tangen, Catherine M, Goodman, Phyllis J, Thompson, Ian M, Hamilton, Robert J, Fleshner, Neil E, Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet L, Ostrander, Elaine A, Geybels, Milan S, Koutros, Stella, Freeman, Laura E Beane, Stampfer, Meir, Wolk, Alicja, Håkansson, Niclas, Andriole, Gerald L, Hoover, Robert N, Machiela, Mitchell J, Sørensen, Karina Dalsgaard, Borre, Michael, Blot, William J, Zheng, Wei, Yeboah, Edward D, Mensah, James E, and Lu, Yong-Jie
- Abstract
Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.
- Published
- 2021
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