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2. Androgen receptor pathway inhibitors and taxanes in metastatic prostate cancer: an outcome-adaptive randomized platform trial

Author

De Laere, Bram, Crippa, Alessio, Discacciati, Andrea, Larsson, Berit, Persson, Maria, Johansson, Susanne, D’hondt, Sanne, Bergström, Rebecka, Chellappa, Venkatesh, Mayrhofer, Markus, Banijamali, Mahsan, Kotsalaynen, Anastasijia, Schelstraete, Céline, Vanwelkenhuyzen, Jan Pieter, Hjälm-Eriksson, Marie, Pettersson, Linn, Ullén, Anders, Lumen, Nicolaas, Enblad, Gunilla, Thellenberg Karlsson, Camilla, Jänes, Elin, Sandzén, Johan, Schatteman, Peter, Nyre Vigmostad, Maria, Olsson, Martha, Ghysel, Christophe, Sautois, Brieuc, De Roock, Wendy, Van Bruwaene, Siska, Anden, Mats, Verbiene, Ingrida, De Maeseneer, Daan, Everaert, Els, Darras, Jochen, Aksnessether, Bjørg Y., Luyten, Daisy, Strijbos, Michiel, Mortezavi, Ashkan, Oldenburg, Jan, Ost, Piet, Eklund, Martin, Grönberg, Henrik, and Lindberg, Johan

Published
2024
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3. On Undesired Emergent Behaviors in Compound Prostate Cancer Detection Systems

Author

Sortland Rolfsnes, Erlend, Thangngat, Philip, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, Fernandez-Quilez, Alvaro, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ali, Sharib, editor, van der Sommen, Fons, editor, Papież, Bartłomiej Władysław, editor, Ghatwary, Noha, editor, Jin, Yueming, editor, and Kolenbrander, Iris, editor

Published
2025
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6. Reconsidering evaluation practices in modular systems: On the propagation of errors in MRI prostate cancer detection

Author

Rolfsnes, Erlend Sortland, Thangngat, Philip, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, and Fernandez-Quilez, Alvaro

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
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

Published
2023

7. Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach

Author

Lindeijer, Tim Nikolass, Ytredal, Tord Martin, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, and Fernandez-Quilez, Alvaro

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
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

Published
2023

8. Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection

Author

Fernandez-Quilez, Alvaro, Nordström, Tobias, Jäderling, Fredrik, Kjosavik, Svein Reidar, and Eklund, Martin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

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

Published
2023

9. Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis

Author

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

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
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.

Published
2023

10. Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants.

Author

Wang, Anqi, Shen, Jiayi, Rodriguez, Alex, Saunders, Edward, Chen, Fei, Janivara, Rohini, Darst, Burcu, Sheng, Xin, Xu, Yili, Chou, Alisha, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark, Plym, Anna, Sahimi, Ali, Hoffman, Thomas, Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Laisk, Triin, Figuerêdo, Jéssica, Muir, Kenneth, Ito, Shuji, Liu, Xiaoxi, Uchio, Yuji, Kubo, Michiaki, Kamatani, Yoichiro, Lophatananon, Artitaya, Wan, Peggy, Andrews, Caroline, Lori, Adriana, Choudhury, Parichoy, Schleutker, Johanna, Tammela, Teuvo, Sipeky, Csilla, Auvinen, Anssi, Giles, Graham, Southey, Melissa, MacInnis, Robert, Cybulski, Cezary, Wokolorczyk, Dominika, Lubinski, Jan, Rentsch, Christopher, Cho, Kelly, Mcmahon, Benjamin, Neal, David, Donovan, Jenny, Hamdy, Freddie, Martin, Richard, Nordestgaard, Borge, Nielsen, Sune, Weischer, Maren, Bojesen, Stig, Røder, Andreas, Stroomberg, Hein, Batra, Jyotsna, Chambers, Suzanne, Horvath, Lisa, Clements, Judith, Tilly, Wayne, Risbridger, Gail, Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordstrom, Tobias, Pashayan, Nora, Dunning, Alison, Ghoussaini, Maya, Travis, Ruth, Key, Tim, Riboli, Elio, Park, Jong, Sellers, Thomas, Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie, Cook, Michael, Mucci, Lorelei, Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David, Penney, Kathryn, Turman, Constance, Tangen, Catherine, Goodman, Phyllis, Thompson, Ian, Hamilton, Robert, Fleshner, Neil, Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet, Ostrander, Elaine, Koutros, Stella, Beane Freeman, Laura, Stampfer, Meir, Wolk, Alicja, and Håkansson, Niclas

Subjects
Humans, Male, Black People, Genetic Predisposition to Disease, Genome-Wide Association Study, Hispanic or Latino, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Risk Factors, White People, Asian People
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.

Published
2023

12. Author Correction: Androgen receptor pathway inhibitors and taxanes in metastatic prostate cancer: an outcome-adaptive randomized platform trial

Author

De Laere, Bram, Crippa, Alessio, Discacciati, Andrea, Larsson, Berit, Persson, Maria, Johansson, Susanne, D’hondt, Sanne, Bergström, Rebecka, Chellappa, Venkatesh, Mayrhofer, Markus, Banijamali, Mahsan, Kotsalaynen, Anastasijia, Schelstraete, Céline, Vanwelkenhuyzen, Jan Pieter, Hjälm-Eriksson, Marie, Pettersson, Linn, Ullén, Anders, Lumen, Nicolaas, Enblad, Gunilla, Thellenberg Karlsson, Camilla, Jänes, Elin, Sandzén, Johan, Schatteman, Peter, Nyre Vigmostad, Maria, Olsson, Martha, Ghysel, Christophe, Sautois, Brieuc, De Roock, Wendy, Van Bruwaene, Siska, Anden, Mats, Verbiene, Ingrida, De Maeseneer, Daan, Everaert, Els, Darras, Jochen, Aksnessether, Bjørg Y., Luyten, Daisy, Strijbos, Michiel, Mortezavi, Ashkan, Oldenburg, Jan, Ost, Piet, Eklund, Martin, Grönberg, Henrik, and Lindberg, Johan

Published
2024
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13. Report of the first seven agents in the I-SPY COVID trial: a phase 2, open label, adaptive platform randomised controlled trial

Author

Consortium, The I-SPY COVID, Files, D Clark, Aggarwal, Neil, Albertson, Timothy, Auld, Sara, Beitler, Jeremy R, Berger, Paul, Burnham, Ellen L, Calfee, Carolyn S, Cobb, Nathan, Crippa, Alessio, Discacciati, Andrea, Eklund, Martin, Esserman, Laura, Friedman, Eliot, Gandotra, Sheetal, Khan, Kashif, Koff, Jonathan, Kumar, Santhi, Liu, Kathleen D, Martin, Thomas R, Matthay, Michael A, Meyer, Nuala J, Obermiller, Timothy, Robinson, Philip, Russell, Derek, Thomas, Karl, Wong, Fum, Wunderink, Richard G, Wurfel, Mark M, Yen, Albert, Youssef, Fady A, Darmanian, Anita, Dzierba, Amy L, Garcia, Ivan, Gosek, Katarzyna, Madahar, Purnema, Mittel, Aaron M, Muir, Justin, Rosen, Amanda, Schicchi, John, Serra, Alexis L, Wahab, Romina, Gibbs, Kevin W, Landreth, Leigha, LaRose, Mary, Parks, Lisa, Wynn, Adina, Ittner, Caroline AG, Mangalmurti, Nilman S, Reilly, John P, Harris, Donna, Methukupally, Abhishek, Patel, Siddharth, Boerger, Lindsie, Kazianis, John, Higgins, Carrie, McKeehan, Jeff, Daniel, Brian, Fields, Scott, Hurst-Hopf, James, Jauregui, Alejandra, Swigart, Lamorna Brown, Blevins, Daniel, Nguyen, Catherine, Suarez, Alexis, Tanios, Maged A, Sarafian, Farjad, Shah, Usman, Adelman, Max, Creel-Bulos, Christina, Detelich, Joshua, Harris, Gavin, Nugent, Katherine, Spainhour, Christina, Yang, Philip, Haczku, Angela, Hardy, Erin, Harper, Richart, Morrissey, Brian, Sandrock, Christian, Budinger, GR Scott, Donnelly, Helen K, Singer, Benjamin D, Moskowitz, Ari, Coleman, Melissa, Levitt, Joseph, Lu, Ruixiao, Henderson, Paul, Asare, Adam, Dunn, Imogene, and Barragan, Alejandro Botello

Subjects
Health Services, Clinical Trials and Supportive Activities, Clinical Research, Good Health and Well Being, I-SPY COVID Consortium, Acute lung injury, Clinical trial, Respiratory insufficiency, SARS-CoV-2
Abstract

BackgroundAn urgent need exists to rapidly screen potential therapeutics for severe COVID-19 or other emerging pathogens associated with high morbidity and mortality.MethodsUsing an adaptive platform design created to rapidly evaluate investigational agents, hospitalised patients with severe COVID-19 requiring ≥6 L/min oxygen were randomised to either a backbone regimen of dexamethasone and remdesivir alone (controls) or backbone plus one open-label investigational agent. Patients were enrolled to the arms described between July 30, 2020 and June 11, 2021 in 20 medical centres in the United States. The platform contained up to four potentially available investigational agents and controls available for randomisation during a single time-period. The two primary endpoints were time-to-recovery (

Published
2023

14. Stockholm3 in a Multiethnic Cohort for Prostate Cancer Detection (SEPTA): A Prospective Multicentered Trial

Author

Vigneswaran, Hari T., Eklund, Martin, Discacciati, Andrea, Nordström, Tobias, Hubbard, Rebecca A., Perlis, Nathan, Abern, Michael R., Moreira, Daniel M., Eggener, Scott, Yonover, Paul, Chow, Alexander K., Watts, Kara, Liss, Michael A., Thoreson, Gregory R., Abreu, Andre L., Sonn, Geoffrey A., Palsdottir, Thorgerdur, Plym, Anna, Wiklund, Fredrik, Grönberg, Henrik, and Murphy, Adam B.

Published
2024
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15. I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations

Author

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, Mittel, Aaron M, Yen, Albert F, Suarez, Alexis E, Serra, Alexis L, Amin, Alpesh N, Rosen, Amanda, Dzierba, Amy L, Haczku, Angela, Barker, Anna D, Weisman, Ariel R, Daniel, Brian M, Morrissey, Brian M, Jones, Chayse, Creel-Bulos, Christina, Angelucci, Christina M, Files, Daniel C, Ng, Diana, Youssef, Fady A, Chaparro-Rojas, Fredy, Harris, Gavin H, Barot, Harsh V, Su, Heny, Garcia, Ivan, Sutter, Jacqueline B, Dodin, Jamal, Lee, Jerry S, Kazianis, John, Reilly, John P, Detelich, Joshua F, Lang, Julie E, Muir, Justin, Gosek, Katarzyna, Nugent, Katherine L, Yee, Kimberly, Rodrigues, Laura G, Macias, Laura R, Orr, Lindsey A, Boerger, Lindsie L, Rosario-Remigio, Lissette, Kufa, Lucia, Huerta, Luis E, Tanios, Maged, Reyes, Maria B, Adelman, Max W, Juarez, Maya M, Jung, Michelle, Meyers, Michelle, Sternlieb, Mitchell P, Cobb, Nathan K, and Aggarwal, Neil

Subjects
Clinical Research, Clinical Trials and Supportive Activities, Good Health and Well Being, Bayes Theorem, COVID-19, Humans, Pandemics, Respiratory Distress Syndrome, Respiratory Insufficiency, SARS-CoV-2, Treatment Outcome, ISPY COVID Adaptive Platform Trial Network, undefined, adult intensive & critical care, clinical trials, Clinical Sciences, Public Health and Health Services, Other Medical and Health Sciences
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.

Published
2022

19. A case-case analysis of women with breast cancer: predictors of interval vs screen-detected cancer.

Author

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

Subjects
Humans, Breast Neoplasms, Mammography, Mass Screening, Odds Ratio, Adult, Aged, Middle Aged, Female, Early Detection of Cancer, Breast cancer, Breast density, Interval cancer, Screening, Supplemental screening, Cancer, Breast Cancer, Aging, Clinical Research, Prevention, 2.1 Biological and endogenous factors, Detection, screening and diagnosis, 4.4 Population screening, 4.2 Evaluation of markers and technologies, Aetiology, Clinical Sciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
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

Published
2022

20. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Author

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

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Urologic Diseases, Prostate Cancer, Algorithms, Biopsy, Cohort Studies, Humans, Male, Neoplasm Grading, Prostatic Neoplasms, Reproducibility of Results, PANDA challenge consortium, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

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 κ, 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.

Published
2022

21. Using deep learning to detect patients at risk for prostate cancer despite benign biopsies

Author

Liu, Bojing, Wang, Yinxi, Weitz, Philippe, Lindberg, Johan, Hartman, Johan, Egevad, Lars, Grönberg, Henrik, Eklund, Martin, and Rantalainen, Mattias

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Background: Transrectal ultrasound guided systematic biopsies of the prostate is a routine procedure to establish a prostate cancer diagnosis. However, the 10-12 prostate core biopsies only sample a relatively small volume of the prostate, and tumour lesions in regions between biopsy cores can be missed, leading to a well-known low sensitivity to detect clinically relevant cancer. As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer. Methods: This study included 14,354 hematoxylin and eosin stained whole slide images from benign prostate biopsies from 1,508 men in two groups: men without an established prostate cancer (PCa) diagnosis and men with at least one core biopsy diagnosed with PCa. 80% of the participants were assigned as training data and used for model optimization (1,211 men), and the remaining 20% (297 men) as a held-out test set used to evaluate model performance. An ensemble of 10 deep convolutional neural network models was optimized for classification of biopsies from men with and without established cancer. Hyperparameter optimization and model selection was performed by cross-validation in the training data . Results: Area under the receiver operating characteristic curve (ROC-AUC) was estimated as 0.727 (bootstrap 95% CI: 0.708-0.745) on biopsy level and 0.738 (bootstrap 95% CI: 0.682 - 0.796) on man level. At a specificity of 0.9 the model had an estimated sensitivity of 0.348. Conclusion: The developed model has the ability to detect men with risk of missed PCa due to under-sampling of the prostate. The proposed model has the potential to reduce the number of false negative cases in routine systematic prostate biopsies and to indicate men who could benefit from MRI-guided re-biopsy., Comment: 13 pages, 3 figures

Published
2021

22. Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks

Author

Weitz, Philippe, Wang, Yinxi, Kartasalo, Kimmo, Egevad, Lars, Lindberg, Johan, Grönberg, Henrik, Eklund, Martin, and Rantalainen, Mattias

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics. However, molecular profiling remains costly and resource intense to implement, and is just starting to be introduced into clinical diagnostics. Molecular changes, including genetic alterations and gene expression changes, occuring in tumors cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes directly from routine haematoxylin and eosin (H&E) stained whole slide images (WSIs) using deep convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach for disease specific modelling of relationships between morphology and gene expression, and we conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates from WSIs of H&E stained tissue. The work is based on the TCGA PRAD study and includes both WSIs and RNA-seq data for 370 patients. Out of 15586 protein coding and sufficiently frequently expressed transcripts, 6618 had predicted expression significantly associated with RNA-seq estimates (FDR-adjusted p-value < 1*10-4) in a cross-validation. 5419 (81.9%) of these were subsequently validated in a held-out test set. We also demonstrate the ability to predict a prostate cancer specific cell cycle progression score directly from WSIs. These findings suggest that contemporary computer vision models offer an inexpensive and scalable solution for prediction of gene expression phenotypes directly from WSIs, providing opportunity for cost-effective large-scale research studies and molecular diagnostics.

Published
2021
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24. Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks

Author

Ström, Peter, Kartasalo, Kimmo, Ruusuvuori, Pekka, Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Egevad, Lars, and Eklund, Martin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Background: The detection of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI is; however, labor intensive. In the study we aimed to develop an algorithm based on deep neural networks to aid pathologists in this task. Methods: We collected, digitized and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7,406 men who underwent biopsy in the prospective and diagnostic STHLM3 trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n=8,318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build deep neural networks, and the remaining 20% were used to evaluate the performance of the algorithm. Results: For the detection of PNI in prostate biopsy cores the network had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1,652 PNI negative cores in the independent test set. For the pre-specified operating point this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. For localizing the regions of PNI within a slide we estimated an average intersection over union of 0.50 (CI: 0.46-0.55). Conclusion: We have developed an algorithm based on deep neural networks for detecting PNI in prostate biopsies with apparently acceptable diagnostic properties. These algorithms have the potential to aid pathologists in the day-to-day work by drastically reducing the number of biopsy cores that need to be assessed for PNI and by highlighting regions of diagnostic interest., Comment: 20 pages, 5 figures

Published
2020

27. Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists.

Author

Bulten, Wouter, Balkenhol, Maschenka, Belinga, Jean-Joël, Brilhante, Américo, Çakır, Aslı, Egevad, Lars, Eklund, Martin, Farré, Xavier, Geronatsiou, Katerina, Molinié, Vincent, Pereira, Guilherme, Roy, Paromita, Saile, Günter, Salles, Paulo, Schaafsma, Ewout, Tschui, Joëlle, Vos, Anne-Marie, van Boven, Hester, Vink, Robert, van der Laak, Jeroen, Hulsbergen-van der Kaa, Christina, and Litjens, Geert

Subjects
Biopsy, Deep Learning, Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Male, Microscopy, Neoplasm Grading, Observer Variation, Pathologists, Predictive Value of Tests, Prostatic Neoplasms, Reproducibility of Results
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.

Published
2021

28. Personalized breast cancer screening in a population-based study: Women informed to screen depending on measures of risk (WISDOM)

Author

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, and Esserman, Laura

Subjects
Oncology and Carcinogenesis, Oncology & Carcinogenesis
Published
2021

29. Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence

Author

Ström, Peter, Kartasalo, Kimmo, Olsson, Henrik, Solorzano, Leslie, Delahunt, Brett, Berney, Daniel M., Bostwick, David G., Evans, Andrew J., Grignon, David J., Humphrey, Peter A., Iczkowski, Kenneth A., Kench, James G., Kristiansen, Glen, van der Kwast, Theodorus H., Leite, Katia R. M., McKenney, Jesse K., Oxley, Jon, Pan, Chin-Chen, Samaratunga, Hemamali, Srigley, John R., Takahashi, Hiroyuki, Tsuzuki, Toyonori, Varma, Murali, Zhou, Ming, Lindberg, Johan, Bergström, Cecilia, Ruusuvuori, Pekka, Wählby, Carolina, Grönberg, Henrik, Rantalainen, Mattias, Egevad, Lars, and Eklund, Martin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73)., Comment: 45 pages, 11 figures

Published
2019

30. 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

Author

Kwong, Jethro C C, Khondker, Adree, Meng, Eric, Taylor, Nicholas, Kuk, Cynthia, Perlis, Nathan, Kulkarni, Girish S, Hamilton, Robert J, Fleshner, Neil E, Finelli, Antonio, van der Kwast, Theodorus H, Ali, Amna, Jamal, Munir, Papanikolaou, Frank, Short, Thomas, Srigley, John R, Colinet, Valentin, Peltier, Alexandre, Diamand, Romain, Lefebvre, Yolene, Mandoorah, Qusay, Sanchez-Salas, Rafael, Macek, Petr, Cathelineau, Xavier, Eklund, Martin, Johnson, Alistair E W, Feifer, Andrew, and Zlotta, Alexandre R

Published
2023
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31. Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading.

Author

Egevad, Lars, Swanberg, Daniela, Delahunt, Brett, Ström, Peter, Kartasalo, Kimmo, Olsson, Henrik, Berney, Dan, Bostwick, David, Evans, Andrew, Humphrey, Peter, Iczkowski, Kenneth, Kench, James, Kristiansen, Glen, Leite, Katia, McKenney, Jesse, Oxley, Jon, Pan, Chin-Chen, Samaratunga, Hemamali, Srigley, John, Takahashi, Hiroyuki, Tsuzuki, Toyonori, van der Kwast, Theo, Varma, Murali, Zhou, Ming, Clements, Mark, and Eklund, Martin

Subjects
Artificial intelligence, Grading, Pathology, Prostate cancer, Reproducibility, Standardization, Artificial Intelligence, Databases, Factual, Humans, Image Interpretation, Computer-Assisted, Male, Neoplasm Grading, Observer Variation, Prostatic Neoplasms
Abstract

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68-0.84) and 0.50 (range 0.40-0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

Published
2020

32. Response to Carter et al.

Author

Eklund, Martin, Broglio, Kristine, Yau, Christina, Connor, Jason T, Fiscalini, Allison Stover, and Esserman, Laura J

Published
2020

34. Abstract OT3-03-02: Personalized breast cancer screening in a population-based study: Women informed to screen depending on measures of risk (WISDOM)

Author

Che, Mandy, Fiscallini, Allison Stover, Acerbi, Irene, Shieh, Yiweh, 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, Parker, Barbara, Borowsky, Alexander, Anton-Culver, Hoda, Naeim, Arash, Kaster, Andrea, Talley, Melinda, van't Veer, Laura, LaCroix, Andrea, Olopade, Olufunmilayo I, and Sheth, Deepa

Subjects
Health Services, Clinical Trials and Supportive Activities, Breast Cancer, Prevention, Clinical Research, Genetics, Cancer, Good Health and Well Being, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Abstract: Background: WISDOM is a 100,000 healthy women preference-tolerant, pragmatic study comparing traditional annual screening to personalized risk-based breast screening. The novelty of WISDOM personalized screening is the integration of previously validated genetic and clinical risk factors (age, family history, breast biopsy results, ethnicity, mammographic density) into a single risk assessment model that directs the starting age, timing, and frequency of screening. The goal of WISDOM is to determine if personalized screening, compared to annual screening, is as safe, less morbid, enables prevention, and is more accepted by women. The study is registered on ClinicalTrials.gov, NCT02620852. Methods: Women aged 40-74 years with no history of breast cancer or DCIS, and no previous double mastectomy can join the study online at wisdomstudy.org. Participants can either elect randomization or self-select a study arm. Then, they can provide electronic consent and sign the Release for Medical Information via DocuSign. For all participants, 5-year risk of developing breast cancer is calculated according to the Breast Cancer Screening Consortium (BCSC) model. Participants in the personalized arm undergo panel-based mutation testing (BRCA1, BRCA2, TP53, PTEN, STK11, CDH1, ATM, PALB2, and CHEK2), and their 5-year risk is calculated using the BCSC score combined with a Polygenic Risk Score (BCSC-PRS) that includes 75 single nucleotide polymorphisms (SNPs) known to increase breast cancer risk (will increase to 229). The SNPs and mutations are assessed by saliva-based testing through Color Genomics. 5-year risk level thresholds are used to stratify for low-, moderate- and high risk. Risk stratification determines age to start, stop, and frequency of screening. Accrual: As of July 2019, the WISDOM study is open to all eligible women in California, North Dakota, South Dakota, Minnesota, Iowa, Illinois, and New Jersey. To date, 30,392 eligible women have registered, and 21,392 women have consented to participate in the trial. The median age was 56 years. 85% of participants were Caucasian, 2% African-American, and 5% Asian. 6% self-reported Hispanic ethnicity. WISDOM is actively partnering with Blue Cross Blue Shield Association for national coverage, self-insured companies (Salesforce, Genentech, Qualcomm, CalPERS) and Medi-Cal (Inland Empire Health Plan) using a coverage with evidence progression approach. Accrual expansion and diversity: To strengthen generalizability, the WISDOM Study is enhancing the diversity of our potential participant population by expanding to other states (Alabama, Louisiana), and partnering with other health insurers and self-insured companies. Future expansion regions include Texas, Florida, South Carolina, Oklahoma, Montana, and New Mexico. Additionally, we have translated the whole study experience to Spanish to further reach Spanish-speaking communities. With the engagement of patient advocates and community partnerships, expanding diversity recruitment will strengthen our scientific knowledge of breast cancer risk and increase access to personalized breast cancer screening recommendations for all women. WISDOM enrollment will continue through 2020. Conclusions: Results at 5 years will enable us to demonstrate that personalized screening improves healthcare value by reducing screen volumes and costs without jeopardizing outcomes. Citation Format: Mandy Che, Allison Stover Fiscallini, Irene Acerbi, Yiweh Shieh, Lisa Madlensky, Jeffrey Tice, Elad Ziv, Martin Eklund, Amie Blanco, Barry Tong, Deborah Goodman, Lamees Nassereddine, Nancy Anderson, Heather Harvey, Steele Fors, Hannah L Park, Antonia Petruse, Skye Stewart, Janet Wernisch, Larissa Risty, Ian Hurley, Barbara Koenig, Celia Kaplan, Robert Hiatt, Neil Wenger, Vivian Lee, Diane Heditsian, Susie Brain, Leah Sabacan, Barbara Parker, Alexander Borowsky, Hoda Anton-Culver, Hoda Anton-Culver, Arash Naeim, Andrea Kaster, Melinda Talley, Laura van't Veer, Andrea LaCroix, Olufunmilayo I Olopade, Deepa Sheth, WISDOM Study and Athena Breast Health Network Investigators and Advocate Partners and Laura Esserman. Personalized breast cancer screening in a population-based study: Women informed to screen depending on measures of risk (WISDOM) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr OT3-03-02.

Published
2020

36. Report of the first seven agents in the I-SPY COVID trial: a phase 2, open label, adaptive platform randomised controlled trial

Author

Files, D. Clark, Aggarwal, Neil, Albertson, Timothy, Auld, Sara, Beitler, Jeremy R., Berger, Paul, Burnham, Ellen L., Calfee, Carolyn S., Cobb, Nathan, Crippa, Alessio, Discacciati, Andrea, Eklund, Martin, Esserman, Laura, Friedman, Eliot, Gandotra, Sheetal, Khan, Kashif, Koff, Jonathan, Kumar, Santhi, Liu, Kathleen D., Martin, Thomas R., Matthay, Michael A., Meyer, Nuala J., Obermiller, Timothy, Robinson, Philip, Russell, Derek, Thomas, Karl, Wong, Se Fum, Wunderink, Richard G., Wurfel, Mark M., Yen, Albert, Youssef, Fady A., Darmanian, Anita, Dzierba, Amy L., Garcia, Ivan, Gosek, Katarzyna, Madahar, Purnema, Mittel, Aaron M., Muir, Justin, Rosen, Amanda, Schicchi, John, Serra, Alexis L., Wahab, Romina, Gibbs, Kevin W., Landreth, Leigha, LaRose, Mary, Parks, Lisa, Wynn, Adina, Ittner, Caroline A.G., Mangalmurti, Nilman S., Reilly, John P., Harris, Donna, Methukupally, Abhishek, Patel, Siddharth, Boerger, Lindsie, Kazianis, John, Higgins, Carrie, McKeehan, Jeff, Daniel, Brian, Fields, Scott, Hurst-Hopf, James, Jauregui, Alejandra, Brown Swigart, Lamorna, Blevins, Daniel, Nguyen, Catherine, Suarez, Alexis, Tanios, Maged A., Sarafian, Farjad, Shah, Usman, Adelman, Max, Creel-Bulos, Christina, Detelich, Joshua, Harris, Gavin, Nugent, Katherine, Spainhour, Christina, Yang, Philip, Haczku, Angela, Hardy, Erin, Harper, Richart, Morrissey, Brian, Sandrock, Christian, Budinger, G. R. Scott, Donnelly, Helen K., Singer, Benjamin D., Moskowitz, Ari, Coleman, Melissa, Levitt, Joseph, Lu, Ruixiao, Henderson, Paul, Asare, Adam, Dunn, Imogene, and Botello Barragan, Alejandro

Published
2023
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38. A response to "Personalised medicine and population health: breast and ovarian cancer".

Author

Antoniou, Antonis, Anton-Culver, Hoda, Borowsky, Alexander, Broeders, Mireille, Brooks, Jennifer, Chiarelli, Anna, Chiquette, Jocelyne, Cuzick, Jack, Delaloge, Suzette, Devilee, Peter, Dorval, Michael, Easton, Douglas, Eisen, Andrea, Eklund, Martin, Eloy, Laurence, Esserman, Laura, Garcia-Closas, Montserrat, Goldgar, David, Hall, Per, Knoppers, Bartha Maria, Kraft, Peter, La Croix, Andrea, Madalensky, Lisa, Mavaddat, Nasim, Mittman, Nicole, Nabi, Hermann, Olopade, Olufunmilayo, Pashayan, Nora, Schmidt, Marjanka, Shieh, Yiwey, Simard, Jacques, Stover-Fiscallini, Allison, Tice, Jeffrey A, Van't Veer, Laura, Wenger, Neil, Wolfson, Michael, Yau, Christina, and Ziv, Elad

Subjects
Humans, Breast Neoplasms, Ovarian Neoplasms, Health Care Costs, Female, Early Detection of Cancer, Precision Medicine, Population Health, Genetics & Heredity, Genetics, Complementary and Alternative Medicine, Paediatrics and Reproductive Medicine
Published
2019

47. Trend in Clinical Trial Participation During COVID-19: A Secondary Analysis of the I-SPY COVID Clinical Trial

Author

Yang, Philip, Dickert, Neal W., Haczku, Angela, Spainhour, Christine, Auld, Sara C., Aggarwal, Neil R., Albertson, Timothy, Auld, Sara, Beitler, Jeremy R., Berger, Paul, Burnham, Ellen L., Calfee, Carolyn S., Cobb, Nathan, Crippa, Alessio, Discacciati, Andrea, Eklund, Martin, Esserman, Laura, Files, D. Clark, Friedman, Eliot, Gandotra, Sheetal, Khan, Kashif, Koff, Jonathan, Kumar, Santhi, Liu, Kathleen D., Martin, Thomas R., Matthay, Michael A., Meyer, Nuala J., Obermiller, Timothy, Robinson, Philip, Russell, Derek, Thomas, Karl, Wong, Se Fum, Wunderink, Richard G., Wurfel, Mark M., Yen, Albert, Youssef, Fady A., Darmanian, Anita, Dzierba, Amy L., Garcia, Ivan, Gosek, Katarzyna, Madahar, Purnema, Mittel, Aaron M., Muir, Justin, Roden, Amanda, Schicchi, John, Serra, Alexis L., Wahab, Romina, Gibbs, Kevin W., Landreth, Leigha, LaRose, Mary, Parks, Lisa, Wynn, Adina, Ittner, Caroline A. G., Mangalmurti, Nilam S., Reilly, John P., Harris, Donna, Methukupally, Abhishek, Patel, Siddharth, Boerger, Lindsie, Kazianis, John, Higgins, Carrie, McKeehan, Jeff, Daniel, Brian, Fields, Scott, Hurst-Hopf, James, Jauregui, Alejandra, Swigart, Lamorna Brown, Belvins, Daniel, Nguyen, Catherine, Suarez, Alexis, Tanios, Maged A., Sarafian, Farjad, Shah, Usman, Adelman, Max, Creel-Bulos, Christina, Detelich, Joshua, Harris, Gavin, Nugent, Katherine, Spainhour, Christine, Yang, Philip, Haczku, Angela, Hardy, Erin, Harper, Richart, Morrissey, Brian, Sandrock, Christian, Budinger, G. R. Scott, Donnelly, Helen K., Singer, Benjamin D., Moskowitz, Ari, Coleman, Melissa, Levitt, Joseph, Lu, Ruixiao, Henderson, Paul, Asare, Adam, Dunn, Imogene, and Barragan, Alejandro Botello

Published
2023
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49. The WISDOM Personalized Breast Cancer Screening Trial: Simulation Study to Assess Potential Bias and Analytic Approaches

Author

Eklund, Martin, Broglio, Kristine, Yau, Christina, Connor, Jason T, Fiscalini, Allison Stover, and Esserman, Laura J

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Human Genome, Cancer, Clinical Trials and Supportive Activities, Prevention, Breast Cancer, Clinical Research, Genetics, Good Health and Well Being, Oncology and carcinogenesis
Abstract

BackgroundWISDOM (Women Informed to Screen Depending on Measures of Risk) is a randomized trial to assess whether personalized breast cancer screening-where women are screened biannually, annually, biennially, or not at all depending on risk and age-can prevent as many advanced (stage IIB or higher) cancers as annual screening in women ages 40-74 years across 5 years of trial time. The short study time in combination with design choices of not requiring study entry and exit mammograms for all participants may introduce different sources of bias in favor of either the personalized or the annual arm.MethodsWe designed a simulation model and performed 5000 virtual WISDOM trials to assess potential biases. Each virtual trial simulated 65 000 randomly assigned participants who were each assigned a risk stratum and a time to stage of at least IIB cancer sampled from an exponential distribution with the hazard rate based on the risk stratum. Results from the virtual trials were used to evaluate two candidate analysis strategies with respect to susceptibility for introducing bias: 1) difference between arms in total number of events over total trial time, and 2) difference in number of events within complete screening cycles.ResultsBased on the simulations, about 86 stage IIB or higher cancers will be detected within the trial and the total exposure time will be about 74 000 years in each arm. Potential ascertainment bias is introduced at study entry and exit. Analysis strategy 1 works better for the nonscreened stratum, whereas method 2 is considerably more unbiased for the strata of women screened biennially or every 6 months.ConclusionCombining the two candidate analysis approaches gives a reasonably unbiased analysis based on the simulations and is the method we will use for the primary analysis in WISDOM. Publishing the WISDOM analysis approach provides transparency and can aid the design and analysis of other individualized screening trials.

Published
2018

50. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci

Author

Schumacher, Fredrick R, Al Olama, Ali Amin, Berndt, Sonja I, Benlloch, Sara, Ahmed, Mahbubl, Saunders, Edward J, Dadaev, Tokhir, Leongamornlert, Daniel, Anokian, Ezequiel, Cieza-Borrella, Clara, Goh, Chee, Brook, Mark N, Sheng, Xin, Fachal, Laura, Dennis, Joe, Tyrer, Jonathan, Muir, Kenneth, Lophatananon, Artitaya, Stevens, Victoria L, Gapstur, Susan M, Carter, Brian D, Tangen, Catherine M, Goodman, Phyllis J, Thompson, Ian M, Batra, Jyotsna, Chambers, Suzanne, Moya, Leire, Clements, Judith, Horvath, Lisa, Tilley, Wayne, Risbridger, Gail P, Gronberg, Henrik, Aly, Markus, Nordström, Tobias, Pharoah, Paul, Pashayan, Nora, Schleutker, Johanna, Tammela, Teuvo LJ, Sipeky, Csilla, Auvinen, Anssi, Albanes, Demetrius, Weinstein, Stephanie, Wolk, Alicja, Håkansson, Niclas, West, Catharine ML, Dunning, Alison M, Burnet, Neil, Mucci, Lorelei A, Giovannucci, Edward, Andriole, Gerald L, Cussenot, Olivier, Cancel-Tassin, Géraldine, Koutros, Stella, Beane Freeman, Laura E, Sorensen, Karina Dalsgaard, Orntoft, Torben Falck, Borre, Michael, Maehle, Lovise, Grindedal, Eli Marie, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Martin, Richard M, Travis, Ruth C, Key, Tim J, Hamilton, Robert J, Fleshner, Neil E, Finelli, Antonio, Ingles, Sue Ann, Stern, Mariana C, Rosenstein, Barry S, Kerns, Sarah L, Ostrer, Harry, Lu, Yong-Jie, Zhang, Hong-Wei, Feng, Ninghan, Mao, Xueying, Guo, Xin, Wang, Guomin, Sun, Zan, Giles, Graham G, Southey, Melissa C, MacInnis, Robert J, FitzGerald, Liesel M, Kibel, Adam S, Drake, Bettina F, Vega, Ana, Gómez-Caamaño, Antonio, Szulkin, Robert, Eklund, Martin, Kogevinas, Manolis, Llorca, Javier, Castaño-Vinyals, Gemma, Penney, Kathryn L, Stampfer, Meir, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Stanford, Janet L, and Cybulski, Cezary

Subjects
Cancer, Aging, Prostate Cancer, Genetics, Urologic Diseases, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Case-Control Studies, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Risk, Profile Study, Australian Prostate Cancer BioResource, IMPACT Study, Canary PASS Investigators, Breast and Prostate Cancer Cohort Consortium, PRACTICAL (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome) Consortium, Cancer of the Prostate in Sweden, Prostate Cancer Genome-wide Association Study of Uncommon Susceptibility Loci, Genetic Associations and Mechanisms in Oncology (GAME-ON)/Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (P C, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10-9; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa1.

Published
2018

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