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1. Longitudinal Association Between Muscle Loss and Mortality in Ever Smokers

Author

Crapo, James D., Silverman, Edwin K., Cummings, Sara, Madden, Kelley, Make, Barry J., Nabbosa, Juliet, Port, Emily, Rashdi, Serine, Regan, Elizabeth A., Stepp, Lori, Watts, Shandi, Weaver, Michael, Beaty, Terri, Bowler, Russell P., Curtis, Jeffrey L., Han, MeiLan K., Hokanson, John E., Lynch, David A., Strand, Matthew J., Anderson, Gary, Bleecker, Eugene R., Coxson, Harvey O., Crystal, Ronald G., Hogg, James C., Province, Michael A., Rennard, Stephen I., Croxton, Thomas, Gan, Weiniu, Postow, Lisa A., Viviano, Lisa M., Costa-Davis, Corinne, Malanga, Elisha, Prieto, Delia, Tal-Singer, Ruth, Farzadegan, Homayoon, Hadji, Akila, Sathe, Leena, Baraghoshi, David, Chen, Grace, Crooks, James, Knowles, Ruthie, Pratte, Katherine, Wilson, Carla, Zelarney, Pearlanne T., Kechris, Katerina J., Leach, Sonia, Austin, Erin E., Czizik, Annika, Kinney, Gregory, Li, Yisha, Lutz, Sharon M., Ragland, Margaret F., Richmond, Nicole, Young, Kendra A., Cho, Michael, Castaldi, Peter J., Glass, Kimberly, Hersh, Craig, Kim, Wonji, Liu, Yang-Yu, Hersh, Craig P., Bidinger, Jacqueline, Cho, Michael H., Conrad, Douglas, DeMeo, Dawn L., El-Boueiz, Adel R., Foreman, Marilyn G., Ghosh, Auyon, Hahn, Georg, Hansel, Nadia N., Hayden, Lystra P., Hobbs, Brian, Kim, Woori, Lange, Christoph, McDonald, Merry- Lynn, McGeachie, Michael, Moll, Matthew, Morris, Melody, Patsopoulos, Nikolaos A., Qiao, Dandi, Ruczinski, Ingo, Wan, Emily S., Dy, Jennifer G., Fain, Sean B., Ginsburg, Shoshana, Hoffman, Eric A., Humphries, Stephen, Judy, Philip F., Stefanie Mason, Alex Kluiber, Oh, Andrea, Poynton, Clare, Reinhardt, Joseph M., Ross, James, San Jose Estepar, Raul, Schroeder, Joyce D., Sitek, Arkadiusz, Steiner, Robert M., van Beek, Edwin, Ginneken, Bram van, van Rikxoort, Eva, Washko, George R., Jensen, Robert, John E. Hokanson, Co-Chair, Bhatt, Surya P., Casaburi, Richard, Kim, Victor, Putcha, Nirupama, Han, MeiLan, Bon, Jessica, Diaz, Alejandro A., Regan, Elizabeth, Anzueto, Antonio, Bailey, William C., Criner, Gerard J., Dransfield, Mark T., Kinney, Greg, Sprenger, Kim, Benos, Takis, Hanania, Nicola A., Hoth, Karin F., Lambert, Allison, Lowe, Katherine, Oates, Gabriela, Parekh, Trisha, Westney, Gloria, Young, Kendra, Balasubramanian, Aparna, Boriek, Aladin, Fawzy, Ashraf, Jacobson, Francine, LaFon, David C., MacIntyre, Neil, Maselli-Caceres, Diego, McCormack, Meredith C., McDonald, Merry-Lynn, Sciurba, Frank, Soler, Xavier, Tejwani, Vickram, van Beek, Edwin JR., Wade, Raymond C., Wells, Mike, Wendt, Chris H., Yun, Jeong H., Zhang, Jingzhou, Gillenwater, Lucas, Lowe, Katherine E., Pratte, Katherine A., Ragland, Margaret, Attaway, Amy, Mason, Stefanie, Rossiter, Harry B., Saha, Punam Kumar, Wilson, Ava, Amaza, Hannatu, Baldomero, Adrienne, Mamary, A. James, O’Brien, James, Wise, Robert A., Eakin, Michelle, Fiedorowicz, Jess G., Henkle, Ben, Holm, Kristen, Iyer, Anand, Kunisaki, Ken M., McEvoy, Charlene, Mkorombindo, Takudzwa, Shinozaki, Gen, Yohannes, Abebaw, Hobbs, Brian D., Miller, Bruce E., Retson, Tara, McCloskey, Lisa, Pernicano, Perry G., Atik, Mustafa, Bertrand, Laura, Monaco, Thomas, Narendra, Dharani, Lenge de Rosen, Veronica V., Badu-Danso, Kwame, Jacobson, Francine L., Kaufman, Laura, Maguire, Cherie, Struble, Sophie, Wilson, Seth, Barr, R. Graham, Almonte, Casandra, Austin, John H.M., Gomez Blum, Maria Lorena, D’Souza, Belinda M., Florez, Emilay, Martinez, Rodney, MacIntyre, Neil, Jr., Curry, Wendy, McAdams, H. Page, Reikofski, Charlotte V., Washington, Lacey, Brown, Robert, Clare, Cheryl, Daniel, Marie, Horton, Karen, Ting “Tony” Lin, Cheng, Mirza, Tahira, Scott, Meagan, Shade, Becky, Budoff, Matt, Calmelat, Robert, Cavanaugh, Deborah, Dailing, Chris, Diaz, Leticia, Fischer, Hans, Indelicato, Renee Love, Porszasz, Janos, Soriano, April, Stringer, William, Urrutia, Miriam, Baldomero, Arianne, Bell, Brian, Deconcini, Miranda, Loes, Linda, Phelan, Jonathan, Robichaux, Camille, Sasse, Cheryl, Tashjian, Joseph H., Flenaugh, Eric L., Abson, Kema, Gebrekristos, Hirut, Johnson, Priscilla, Jordan, Jessica, Ponce, Mario, Terpenning, Silanath, Wilson, Derrick, Broadhurst, Grace, Dyer, Debra, Engel, Elena, Finigan, Jay, Hill, Andrew, Jones, Alex, Jones, Ryan, Owen, Jordan, Rosiello, Richard, Andries, Nicole, Charpentier, Mary, Kirk, Diane, Pace, David, Ciccolella, David, Cordova, Francis, Dass, Chandra, D’Alonzo, Gilbert, Davis, Valena, Desai, Parag, Fehrle, Dee, Grabianowski, Carla, Jacobs, Michael, Jameson, Laurie, Jones, Gayle M., Kelsen, Steven, Marchetti, Nathaniel, McGonagle, Francine, Satti, Aditi, Shenoy, Kartik, Sheridan, Regina, Vega-Sanchez, Maria, Wallace, Samantha, Akinseye-kolapo, Samuel, Baker, Matthew, Goggins, Arnissa, McClain, Anny, Nath, Hrudaya, Singh, Satinder P., Sonavane, Sushil K., Westfall, Elizabeth, Gil, Marissa, El Hajjaoui, Tarek, Hsiao, Albert, Martineau, Amber, Mielke, Jenna, Perez, Karl, Querido, Gabriel, Reston, Tara, Yen, Andrew, Comellas, Alejandro, Fortis, Spyridon, Galizia, Mauricio, Garcia, Eric, Keating, Janet, Laroia, Archana, Lee, Changhyun, Meyer, Amber, Mullan, Brian, Nagpal, Prashant, Ofori, Oloigbe, Suiter, Sierra, Mason, Stefanie E., Moreta-Martinez, Rafael, Labaki, Wassim W., San Jose Estepar, Ruben, Make, Barry, and Stringer, Kathleen

Published
2022
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2. Evaluation of C-Reactive Protein and Computer-Aided Analysis of Chest X-rays as Tuberculosis Triage Tests at Health Facilities in Lesotho and South Africa.

Author

Bosman, Shannon, Ayakaka, Irene, Muhairwe, Josephine, Kamele, Mashaete, Heerden, Alastair van, Madonsela, Thandanani, Labhardt, Niklaus D, Sommer, Gregor, Bremerich, Jens, Zoller, Thomas, Murphy, Keelin, Ginneken, Bram van, Keter, Alfred K, Jacobs, Bart K M, Bresser, Moniek, Signorell, Aita, Glass, Tracy R, Lynen, Lutgarde, and Reither, Klaus

Subjects
TUBERCULOSIS diagnosis, COMPUTER software, RECEIVER operating characteristic curves, RESEARCH funding, ARTIFICIAL intelligence, CHEST X rays, RAPID diagnostic tests, DESCRIPTIVE statistics, LONGITUDINAL method, COMPUTER-aided diagnosis, HEALTH facilities, QUALITY assurance, DATA analysis software, CONFIDENCE intervals, C-reactive protein, MEDICAL triage, SENSITIVITY & specificity (Statistics), TELERADIOLOGY, EVALUATION
Abstract

Background To improve tuberculosis case-finding, rapid, non-sputum triage tests need to be developed according to the World Health Organization target product profile (TPP) (>90% sensitivity, >70% specificity). We prospectively evaluated and compared artificial intelligence–based, computer-aided detection software, CAD4TBv7, and C-reactive protein assay (CRP) as triage tests at health facilities in Lesotho and South Africa. Methods Adults (≥18 years) presenting with ≥1 of the 4 cardinal tuberculosis symptoms were consecutively recruited between February 2021 and April 2022. After informed consent, each participant underwent a digital chest X-ray for CAD4TBv7 and a CRP test. Participants provided 1 sputum sample for Xpert MTB/RIF Ultra and Xpert MTB/RIF and 1 for liquid culture. Additionally, an expert radiologist read the chest X-rays via teleradiology. For primary analysis, a composite microbiological reference standard (ie, positive culture or Xpert Ultra) was used. Results We enrolled 1392 participants, 48% were people with HIV and 24% had previously tuberculosis. The receiver operating characteristic curve for CAD4TBv7 and CRP showed an area under the curve of.87 (95% CI:.84–.91) and.80 (95% CI:.76–.84), respectively. At thresholds corresponding to 90% sensitivity, specificity was 68.2% (95% CI: 65.4–71.0%) and 38.2% (95% CI: 35.3–41.1%) for CAD4TBv7 and CRP, respectively. CAD4TBv7 detected tuberculosis as well as an expert radiologist. CAD4TBv7 almost met the TPP criteria for tuberculosis triage. Conclusions CAD4TBv7 is accurate as a triage test for patients with tuberculosis symptoms from areas with a high tuberculosis and HIV burden. The role of CRP in tuberculosis triage requires further research. Clinical Trials Registration Clinicaltrials.gov identifier: NCT04666311. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge

Author

Sogancioglu, Ecem, Ginneken, Bram van, Behrendt, Finn, Bengs, Marcel, Schlaefer, Alexander, Radu, Miron, Xu, Di, Sheng, Ke, Scalzo, Fabien, Marcus, Eric, Papa, Samuele, Teuwen, Jonas, Scholten, Ernst Th., Schalekamp, Steven, Hendrix, Nils, Jacobs, Colin, Hendrix, Ward, Sanchez, Clara I., and Murphy, Keelin

Abstract

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.

Published
2024
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5. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Author

Setio, Arnaud Arindra Adiyoso, Traverso, Alberto, de Bel, Thomas, Berens, Moira S.N., Bogaard, Cas van den, Cerello, Piergiorgio, Chen, Hao, Dou, Qi, Fantacci, Maria Evelina, Geurts, Bram, Gugten, Robbert van der, Heng, Pheng Ann, Jansen, Bart, de Kaste, Michael M.J., Kotov, Valentin, Lin, Jack Yu-Hung, Manders, Jeroen T.M.C., Sóñora-Mengana, Alexander, García-Naranjo, Juan Carlos, Papavasileiou, Evgenia, Prokop, Mathias, Saletta, Marco, Schaefer-Prokop, Cornelia M, Scholten, Ernst T., Scholten, Luuk, Snoeren, Miranda M., Torres, Ernesto Lopez, Vandemeulebroucke, Jef, Walasek, Nicole, Zuidhof, Guido C.A., Ginneken, Bram van, and Jacobs, Colin

Published
2017
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10. Understanding metric-related pitfalls in image analysis validation

Author

Reinke, Annika, Tizabi, Minu D., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Kavur, A. Emre, Rädsch, Tim, Sudre, Carole H., Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew, Büttner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, Ginneken, Bram van, Glocker, Ben, Godau, Patrick, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kenngott, Hannes, Kleesiek, Jens, Kofler, Florian, Kooi, Thijs, Kopp-Schneider, Annette, Kozubek, Michal, Kreshuk, Anna, Kurc, Tahsin, Landman, Bennett A., Litjens, Geert, Madani, Amin, Maier-Hein, Klaus, Martel, Anne L., Mattson, Peter, Meijering, Erik, Menze, Bjoern, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Petersen, Jens, Rafelski, Susanne M., Rajpoot, Nasir, Reyes, Mauricio, Riegler, Michael A., Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, Smeden, Maarten van, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Calster, Ben Van, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Jäger, Paul F., and Maier-Hein, Lena

Subjects
cs.CV
Abstract

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Published
2023
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11. Dataset of prostate MRI annotated for anatomical zones and cancer

Author

Adams, Lisa C., Makowski, Marcus R., Engel, Günther, Rattunde, Maximilian, Busch, Felix, Asbach, Patrick, Niehues, Stefan M., Vinayahalingam, Shankeeth, Ginneken, Bram Van, Litjens, Geert, and Bressem, Keno K.

Subjects
Prostate cancer, Apparent diffusion coefficient (ADC), Pixel-wise segmentation, T2-weighted imaging, Diffusion-weighted imaging, 3.0 Tesla MRI, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
Abstract

In the present work, we present a publicly available, expert-segmented representative dataset of 158 3.0 Tesla biparametric MRIs [1]. There is an increasing number of studies investigating prostate and prostate carcinoma segmentation using deep learning (DL) with 3D architectures [2], [3], [4], [5], [6], [7]. The development of robust and data-driven DL models for prostate segmentation and assessment is currently limited by the availability of openly available expert-annotated datasets [8], [9], [10]. The dataset contains 3.0 Tesla MRI images of the prostate of patients with suspected prostate cancer. Patients over 50 years of age who had a 3.0 Tesla MRI scan of the prostate that met PI-RADS version 2.1 technical standards were included. All patients received a subsequent biopsy or surgery so that the MRI diagnosis could be verified/matched with the histopathologic diagnosis. For patients who had undergone multiple MRIs, the last MRI, which was less than six months before biopsy/surgery, was included. All patients were examined at a German university hospital (Charité Universitätsmedizin Berlin) between 02/2016 and 01/2020. All MRI were acquired with two 3.0 Tesla MRI scanners (Siemens VIDA and Skyra, Siemens Healthineers, Erlangen, Germany). Axial T2W sequences and axial diffusion-weighted sequences (DWI) with apparent diffusion coefficient maps (ADC) were included in the data set. T2W sequences and ADC maps were annotated by two board-certified radiologists with 6 and 8 years of experience, respectively. For T2W sequences, the central gland (central zone and transitional zone) and peripheral zone were segmented. If areas of suspected prostate cancer (PIRADS score of ≥ 4) were identified on examination, they were segmented in both the T2W sequences and ADC maps. Because restricted diffusion is best seen in DWI images with high b-values, only these images were selected and all images with low b-values were discarded. Data were then anonymized and converted to NIfTI (Neuroimaging Informatics Technology Initiative) format.

Published
2022

12. Longitudinal Association Between Muscle Loss and Mortality in Ever Smokers

Author

Mason, Stefanie E., primary, Moreta-Martinez, Rafael, additional, Labaki, Wassim W., additional, Strand, Matthew J., additional, Regan, Elizabeth A., additional, Bon, Jessica, additional, San Jose Estepar, Ruben, additional, Casaburi, Richard, additional, McDonald, Merry-Lynn, additional, Rossiter, Harry B., additional, Make, Barry, additional, Dransfield, Mark T., additional, Han, MeiLan K., additional, Young, Kendra, additional, Curtis, Jeffrey L., additional, Stringer, Kathleen, additional, Kinney, Greg, additional, Hokanson, John E., additional, San Jose Estepar, Raul, additional, Washko, George R., additional, Crapo, James D., additional, Silverman, Edwin K., additional, Cummings, Sara, additional, Madden, Kelley, additional, Make, Barry J., additional, Nabbosa, Juliet, additional, Port, Emily, additional, Rashdi, Serine, additional, Stepp, Lori, additional, Watts, Shandi, additional, Weaver, Michael, additional, Beaty, Terri, additional, Bowler, Russell P., additional, Lynch, David A., additional, Anderson, Gary, additional, Bleecker, Eugene R., additional, Coxson, Harvey O., additional, Crystal, Ronald G., additional, Hogg, James C., additional, Province, Michael A., additional, Rennard, Stephen I., additional, Croxton, Thomas, additional, Gan, Weiniu, additional, Postow, Lisa A., additional, Viviano, Lisa M., additional, Costa-Davis, Corinne, additional, Malanga, Elisha, additional, Prieto, Delia, additional, Tal-Singer, Ruth, additional, Farzadegan, Homayoon, additional, Hadji, Akila, additional, Sathe, Leena, additional, Baraghoshi, David, additional, Chen, Grace, additional, Crooks, James, additional, Knowles, Ruthie, additional, Pratte, Katherine, additional, Wilson, Carla, additional, Zelarney, Pearlanne T., additional, Kechris, Katerina J., additional, Leach, Sonia, additional, Austin, Erin E., additional, Czizik, Annika, additional, Kinney, Gregory, additional, Li, Yisha, additional, Lutz, Sharon M., additional, Ragland, Margaret F., additional, Richmond, Nicole, additional, Young, Kendra A., additional, Cho, Michael, additional, Castaldi, Peter J., additional, Glass, Kimberly, additional, Hersh, Craig, additional, Kim, Wonji, additional, Liu, Yang-Yu, additional, Hersh, Craig P., additional, Bidinger, Jacqueline, additional, Cho, Michael H., additional, Conrad, Douglas, additional, DeMeo, Dawn L., additional, El-Boueiz, Adel R., additional, Foreman, Marilyn G., additional, Ghosh, Auyon, additional, Hahn, Georg, additional, Hansel, Nadia N., additional, Hayden, Lystra P., additional, Hobbs, Brian, additional, Kim, Woori, additional, Lange, Christoph, additional, McDonald, Merry- Lynn, additional, McGeachie, Michael, additional, Moll, Matthew, additional, Morris, Melody, additional, Patsopoulos, Nikolaos A., additional, Qiao, Dandi, additional, Ruczinski, Ingo, additional, Wan, Emily S., additional, Dy, Jennifer G., additional, Fain, Sean B., additional, Ginsburg, Shoshana, additional, Hoffman, Eric A., additional, Humphries, Stephen, additional, Judy, Philip F., additional, Stefanie Mason, Alex Kluiber, additional, Oh, Andrea, additional, Poynton, Clare, additional, Reinhardt, Joseph M., additional, Ross, James, additional, Schroeder, Joyce D., additional, Sitek, Arkadiusz, additional, Steiner, Robert M., additional, van Beek, Edwin, additional, Ginneken, Bram van, additional, van Rikxoort, Eva, additional, Jensen, Robert, additional, John E. Hokanson, Co-Chair:, additional, Bhatt, Surya P., additional, Kim, Victor, additional, Putcha, Nirupama, additional, Han, MeiLan, additional, Diaz, Alejandro A., additional, Regan, Elizabeth, additional, Anzueto, Antonio, additional, Bailey, William C., additional, Criner, Gerard J., additional, Sprenger, Kim, additional, Benos, Takis, additional, Hanania, Nicola A., additional, Hoth, Karin F., additional, Lambert, Allison, additional, Lowe, Katherine, additional, Oates, Gabriela, additional, Parekh, Trisha, additional, Westney, Gloria, additional, Balasubramanian, Aparna, additional, Boriek, Aladin, additional, Fawzy, Ashraf, additional, Jacobson, Francine, additional, LaFon, David C., additional, MacIntyre, Neil, additional, Maselli-Caceres, Diego, additional, McCormack, Meredith C., additional, Sciurba, Frank, additional, Soler, Xavier, additional, Tejwani, Vickram, additional, van Beek, Edwin JR., additional, Wade, Raymond C., additional, Wells, Mike, additional, Wendt, Chris H., additional, Yun, Jeong H., additional, Zhang, Jingzhou, additional, Gillenwater, Lucas, additional, Lowe, Katherine E., additional, Pratte, Katherine A., additional, Ragland, Margaret, additional, Attaway, Amy, additional, Mason, Stefanie, additional, Saha, Punam Kumar, additional, Wilson, Ava, additional, Amaza, Hannatu, additional, Baldomero, Adrienne, additional, Mamary, A. James, additional, O’Brien, James, additional, Wise, Robert A., additional, Eakin, Michelle, additional, Fiedorowicz, Jess G., additional, Henkle, Ben, additional, Holm, Kristen, additional, Iyer, Anand, additional, Kunisaki, Ken M., additional, McEvoy, Charlene, additional, Mkorombindo, Takudzwa, additional, Shinozaki, Gen, additional, Yohannes, Abebaw, additional, Hobbs, Brian D., additional, Miller, Bruce E., additional, Retson, Tara, additional, McCloskey, Lisa, additional, Pernicano, Perry G., additional, Atik, Mustafa, additional, Bertrand, Laura, additional, Monaco, Thomas, additional, Narendra, Dharani, additional, Lenge de Rosen, Veronica V., additional, Badu-Danso, Kwame, additional, Jacobson, Francine L., additional, Kaufman, Laura, additional, Maguire, Cherie, additional, Struble, Sophie, additional, Wilson, Seth, additional, Barr, R. Graham, additional, Almonte, Casandra, additional, Austin, John H.M., additional, Gomez Blum, Maria Lorena, additional, D’Souza, Belinda M., additional, Florez, Emilay, additional, Martinez, Rodney, additional, Curry, Wendy, additional, McAdams, H. Page, additional, Reikofski, Charlotte V., additional, Washington, Lacey, additional, Brown, Robert, additional, Clare, Cheryl, additional, Daniel, Marie, additional, Horton, Karen, additional, Ting “Tony” Lin, Cheng, additional, Mirza, Tahira, additional, Scott, Meagan, additional, Shade, Becky, additional, Budoff, Matt, additional, Calmelat, Robert, additional, Cavanaugh, Deborah, additional, Dailing, Chris, additional, Diaz, Leticia, additional, Fischer, Hans, additional, Indelicato, Renee Love, additional, Porszasz, Janos, additional, Soriano, April, additional, Stringer, William, additional, Urrutia, Miriam, additional, Baldomero, Arianne, additional, Bell, Brian, additional, Deconcini, Miranda, additional, Loes, Linda, additional, Phelan, Jonathan, additional, Robichaux, Camille, additional, Sasse, Cheryl, additional, Tashjian, Joseph H., additional, Flenaugh, Eric L., additional, Abson, Kema, additional, Gebrekristos, Hirut, additional, Johnson, Priscilla, additional, Jordan, Jessica, additional, Ponce, Mario, additional, Terpenning, Silanath, additional, Wilson, Derrick, additional, Broadhurst, Grace, additional, Dyer, Debra, additional, Engel, Elena, additional, Finigan, Jay, additional, Hill, Andrew, additional, Jones, Alex, additional, Jones, Ryan, additional, Owen, Jordan, additional, Rosiello, Richard, additional, Andries, Nicole, additional, Charpentier, Mary, additional, Kirk, Diane, additional, Pace, David, additional, Ciccolella, David, additional, Cordova, Francis, additional, Dass, Chandra, additional, D’Alonzo, Gilbert, additional, Davis, Valena, additional, Desai, Parag, additional, Fehrle, Dee, additional, Grabianowski, Carla, additional, Jacobs, Michael, additional, Jameson, Laurie, additional, Jones, Gayle M., additional, Kelsen, Steven, additional, Marchetti, Nathaniel, additional, McGonagle, Francine, additional, Satti, Aditi, additional, Shenoy, Kartik, additional, Sheridan, Regina, additional, Vega-Sanchez, Maria, additional, Wallace, Samantha, additional, Akinseye-kolapo, Samuel, additional, Baker, Matthew, additional, Goggins, Arnissa, additional, McClain, Anny, additional, Nath, Hrudaya, additional, Singh, Satinder P., additional, Sonavane, Sushil K., additional, Westfall, Elizabeth, additional, Gil, Marissa, additional, El Hajjaoui, Tarek, additional, Hsiao, Albert, additional, Martineau, Amber, additional, Mielke, Jenna, additional, Perez, Karl, additional, Querido, Gabriel, additional, Reston, Tara, additional, Yen, Andrew, additional, Comellas, Alejandro, additional, Fortis, Spyridon, additional, Galizia, Mauricio, additional, Garcia, Eric, additional, Keating, Janet, additional, Laroia, Archana, additional, Lee, Changhyun, additional, Meyer, Amber, additional, Mullan, Brian, additional, Nagpal, Prashant, additional, Ofori, Oloigbe, additional, and Suiter, Sierra, additional

Published
2022
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14. Common Limitations of Image Processing Metrics:A Picture Story

Author

Reinke, Annika, Tizabi, Minu D., Sudre, Carole H., Eisenmann, Matthias, Rädsch, Tim, Baumgartner, Michael, Acion, Laura, Antonelli, Michela, Bakas, Spyridon, Bankhead, Peter, Benis, Arriel, Cardoso, M. Jorge, Cheplygina, Veronika, Christodoulou, Evangelia, Cimini, Beth, Collins, Gary S., Farahani, Keyvan, Ginneken, Bram van, Glocker, Ben, Godau, Patrick, Hamprecht, Fred, Hashimoto, Daniel A., Heckmann-Nötzel, Doreen, Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Karargyris, Alexandros, Karthikesalingam, Alan, Kainz, Bernhard, Kavur, Emre, Kenngott, Hannes, Kleesiek, Jens, Kooi, Thijs, Kozubek, Michal, Kreshuk, Anna, Kurc, Tahsin, Landman, Bennett A., Litjens, Geert, Madani, Amin, Maier-Hein, Klaus, Martel, Anne L., Mattson, Peter, Meijering, Erik, Menze, Bjoern, Moher, David, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Noyan, M. Alican, Petersen, Jens, Polat, Gorkem, Rajpoot, Nasir, Reyes, Mauricio, Rieke, Nicola, Riegler, Michael, Rivaz, Hassan, Saez-Rodriguez, Julio, Gutierrez, Clarisa Sanchez, Schroeter, Julien, Saha, Anindo, Shetty, Shravya, Smeden, Maarten van, Stieltjes, Bram, Summers, Ronald M., Taha, Abdel A., Tsaftaris, Sotirios A., Calster, Ben Van, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Kopp-Schneider, Annette, Jäger, Paul, and Maier-Hein, Lena

Abstract

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.

Published
2021
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15. Visceral Adipose Tissue and Different Measures of Adiposity in Different Severities of Diffuse Idiopathic Skeletal Hyperostosis

Author

Harlianto, Netanja, Westerink, Jan, Foppen, Wouter, Hol, Marjolein, Wittenberg, Rianne, Veen, Pieternella van der, Ginneken, Bram van, Kuperus, Jonneke, Verlaan, Jorrit-Jan, Jong, Pim de, Hoesein, Firdaus Mohamed, and Group, on behalf of the UCC-SMART-Study

Subjects
medicine.medical_specialty, Intra-Abdominal Fat, Medicine (miscellaneous), Adipose tissue, Type 2 diabetes, Article, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, 0302 clinical medicine, Insulin resistance, Internal medicine, Diabetes mellitus, Medicine, risk factors, Diffuse Idiopathic Skeletal Hyperostosis, 030203 arthritis & rheumatology, adiposity, business.industry, medicine.disease, Obesity, Endocrinology, Blood pressure, intra-abdominal fat, business, diffuse idiopathic skeletal hyperostosis, 030217 neurology & neurosurgery, Rare cancers Radboud Institute for Health Sciences [Radboudumc 9]
Abstract

Background: Diffuse idiopathic skeletal hyperostosis (DISH) is associated with both obesity and type 2 diabetes. Our objective was to investigate the relation between DISH and visceral adipose tissue (VAT) in particular, as this would support a causal role of insulin resistance and low grade inflammation in the development of DISH. Methods: In 4334 patients with manifest vascular disease, the relation between different adiposity measures and the presence of DISH was compared using z-scores via standard deviation logistic regression analyses. Analyses were stratified by sex and adjusted for age, systolic blood pressure, diabetes, non-HDL cholesterol, smoking status, and renal function. Results: DISH was present in 391 (9%) subjects. The presence of DISH was associated with markers of adiposity and had a strong relation with VAT in males (OR: 1.35, 95%CI: 1.20–1.54) and females (OR: 1.43, 95%CI: 1.06–1.93). In males with the most severe DISH (extensive ossification of seven or more vertebral bodies) the association between DISH and VAT was stronger (OR: 1.61, 95%CI: 1.31–1.98), while increased subcutaneous fat was negatively associated with DISH (OR: 0.65, 95%CI: 0.49–0.95). In females, increased subcutaneous fat was associated with the presence of DISH (OR: 1.43, 95%CI: 1.14–1.80). Conclusion: Markers of adiposity, including VAT, are strongly associated with the presence of DISH. Subcutaneous adipose tissue thickness was negatively associated with more severe cases of DISH in males, while in females, increased subcutaneous adipose tissue was associated with the presence of DISH.

Published
2021

17. Automatic segmentation of pulmonary lobes robust against incomplete fissures

Author

Rikxoort, Eva M. van, Prokop, Mathias, de Hoop, Bartjan, Viergever, Max A., Pluim, Josien P.W., and Ginneken, Bram van

Subjects
CT imaging -- Usage, Lungs -- Medical examination, Segmentation (Morphology) -- Analysis, Business, Electronics, Electronics and electrical industries, Health care industry
Published
2010

18. Diffuse idiopathic skeletal hyperostosis is associated with incident stroke in patients with increased cardiovascular risk.

Author

Harlianto, Netanja I, Oosterhof, Nadine, Foppen, Wouter, Hol, Marjolein E, Wittenberg, Rianne, Veen, Pieternella H van der, Ginneken, Bram van, Hoesein, Firdaus A A Mohamed, Verlaan, Jorrit-Jan, Jong, Pim A de, Westerink, Jan, and UCC-SMART-Studygroup, for the

Subjects
SPINAL osteophytosis complications, CARDIOVASCULAR disease related mortality, CARDIOVASCULAR diseases risk factors, EXOSTOSIS, CONFIDENCE intervals, MAJOR adverse cardiovascular events, ISCHEMIC stroke, MYOCARDIAL infarction, DISEASE incidence, RISK assessment, DESCRIPTIVE statistics, STATISTICAL models, SPINAL osteophytosis, LONGITUDINAL method, PROPORTIONAL hazards models, DISEASE risk factors
Abstract

Objectives Earlier retrospective studies have suggested a relation between DISH and cardiovascular disease, including myocardial infarction. The present study assessed the association between DISH and incidence of cardiovascular events and mortality in patients with high cardiovascular risk. Methods In this prospective cohort study, we included 4624 patients (mean age 58.4 years, 69.6% male) from the Second Manifestations of ARTerial disease cohort. The main end point was major cardiovascular events (MACE: stroke, myocardial infarction and vascular death). Secondary endpoints included all-cause mortality and separate vascular events. Cause-specific proportional hazard models were used to evaluate the risk of DISH on all outcomes, and subdistribution hazard models were used to evaluate the effect of DISH on the cumulative incidence. All models were adjusted for age, sex, body mass index, blood pressure, diabetes, non-HDL cholesterol, packyears, renal function and C-reactive protein. Results DISH was present in 435 (9.4%) patients. After a median follow-up of 8.7 (IQR 5.0–12.0) years, 864 patients had died and 728 patients developed a MACE event. DISH was associated with an increased cumulative incidence of ischaemic stroke. After adjustment in cause-specific modelling, DISH remained significantly associated with ischaemic stroke (HR 1.55; 95% CI: 1.01, 2.38), but not with MACE (HR 0.99; 95% CI: 0.79, 1.24), myocardial infarction (HR 0.88; 95% CI: 0.59, 1.31), vascular death (HR 0.94; 95% CI: 0.68, 1.27) or all-cause mortality (HR 0.94; 95% CI: 0.77, 1.16). Conclusion The presence of DISH is independently associated with an increased incidence and risk for ischaemic stroke, but not with MACE, myocardial infarction, vascular death or all-cause mortality. [ABSTRACT FROM AUTHOR]

Published
2022
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19. Patients with diffuse idiopathic skeletal hyperostosis have an increased burden of thoracic aortic calcifications.

Author

Harlianto, Netanja I, Westerink, Jan, Hol, Marjolein E, Wittenberg, Rianne, Foppen, Wouter, Veen, Pieternella H van der, Ginneken, Bram van, Verlaan, Jorrit-Jan, Jong, Pim A de, Hoesein, Firdaus A A Mohamed, and Group, UCC-SMART Study

Subjects
EXOSTOSIS, CORONARY artery calcification, CARDIOVASCULAR diseases
Abstract

Objectives. DISH has been associated with increased coronary artery calcifications and incident ischaemic stroke. The formation of bone along the spine may share pathways with calcium deposition in the aorta. We hypothesized that patients with DISH have increased vascular calcifications. Therefore we aimed to investigate the presence and extent of DISH in relation to thoracic aortic calcification (TAC) severity. Methods. This cross-sectional study included 4703 patients from the Second Manifestation of ARTerial disease cohort, consisting of patients with cardiovascular events or risk factors for cardiovascular disease. Chest radiographs were scored for DISH using the Resnick criteria. Different severities of TAC were scored arbitrarily from no TAC to mild, moderate or severe TAC. Using multivariate logistic regression, the associations between DISH and TAC were analysed with adjustments for age, sex, BMI, diabetes, smoking status, non-high-density lipoprotein cholesterol, cholesterol lowering drug usage, renal function and blood pressure. Results. A total of 442 patients (9.4%) had evidence of DISH and 1789 (38%) patients had TAC. The prevalence of DISH increased from 6.6% in the no TAC group to 10.8% in the mild, 14.3% in the moderate and 17.1% in the severe TAC group. After adjustments, DISH was significantly associated with the presence of TAC [odds ratio (OR) 1.46 [95% CI 1.17, 1.82)]. In multinomial analyses, DISH was associated with moderate TAC [OR 1.43 (95% CI 1.06, 1.93)] and severe TAC [OR 1.67 (95% CI 1.19, 2.36)]. Conclusions. Subjects with DISH have increased TACs, providing further evidence that patients with DISH have an increased burden of vascular calcifications. [ABSTRACT FROM AUTHOR]

Published
2022
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21. Diffuse and specular reflectance from rough surfaces

Author

Ginneken, Bram van, Stavridi, Marigo, and Koenderink, Jan J.

Subjects
Reflection (Optics) -- Research, Optics, Geometrical -- Research, Astronomy, Physics
Abstract

We present a reflection model for isotropic rough surfaces that have both specular and diffuse components. The surface is assumed to have a normal distribution of heights. Parameters of the model are the surface roughness given by the rms slope, the albedo, and the balance between diffuse and specular reflection. The effect of roughness on diffuse reflection is taken into account, instead of our modeling this component as a constant Lambertian term. The model includes geometrical effects such as masking and shadowing. The model is compared with experimental data obtained from goniophotometric measurements on samples of tiles and bricks. The model fits well to samples with very different reflection properties. Measurements of the sample profiles performed with a laser profilometer to determine the rms slope show that the assumed surface model is realistic. The model could therefore be used in machine vision and computer graphics to approximate reflection characteristics of surfaces. It could also be used to predict the texture of surfaces as a function of illumination and viewing angles. OCIS codes: 120.5700, 240.5700, 240.0240.

Published
1998

22. Surface bidirectional reflection distribution function and the texture of bricks and tiles

Author

Stavridi, Marigo, Ginneken, Bram van, and Koenderink, Jan J.

Subjects
Reflection (Optics) -- Research, Bricks -- Research, Tiles -- Research, Astronomy, Physics
Abstract

We study and analyze properties underlying the visual appearance of materials such as the surface bidirectional reflection distribution function and texture. The spatial distribution of scattered light in relation to the incident light determines the surface appearance and can be partly specified by the bidirectional reflection distribution function, which is defined as the directionally dependent ratio of radiance to irradiance. We perform gonioradiometric measurements on samples of bricks and tiles. To describe the reflection mechanisms in the surfaces under study, we combine models of specular and diffuse reflectance from rough surfaces and fit them to the experimental data. We also collect images and determine the textural differences in the surface appearance, resulting from the variation in the illumination direction and the viewing directions. Key words: Bidirectional reflection distribution function, texture, surface roughness, specular and diffuse reflectance, radiance measurements, principal components.

Published
1997

24. Response : Optimum Management of Pulmonary Nodules

Author

Van Riel, Sarah J., Scholten, Ernst Th, Prokop, Mathias, Mali, Willem P., Ginneken, Bram van, and Schaefer-Prokop, Cornelia M.

Subjects
Radiology Nuclear Medicine and imaging, Journal Article
Published
2017

25. The Association Between Lung Hyperinflation and Coronary Artery Disease in Smokers

Author

Chandra, Divay, Gupta, Aman, Kinney, Gregory L., Fuhrman, Carl R., Leader, Joseph K., Diaz, Alejandro A., Bon, Jessica, Barr, R. Graham, Washko, George, Budoff, Matthew, Hokanson, John, Sciurba, Frank C., Crapo, James D., Silverman, Edwin K., Make, Barry J., Regan, Elizabeth A., Beaty, Terri, Begum, Ferdouse, Boueiz, Adel R., Castaldi, Peter J., Cho, Michael, DeMeo, Dawn L., Foreman, Marilyn G., Halper-Stromberg, Eitan, Hayden, Lystra P., Hersh, Craig P., Hetmanski, Jacqueline, Hobbs, Brian D., Hokanson, John E., Laird, Nan, Lange, Christoph, Lutz, Sharon M., McDonald, Merry-Lynn, Parker, Margaret M., Prokopenko, Dmitry, Qiao, Dandi, Regan, Elizabeth A., Sakornsakolpat, Phuwanat, Silverman, Edwin K., Wan, Emily S., Won, Sungho, Al Qaisi, Mustafa, Coxson, Harvey O., Gray, Teresa, Han, MeiLan K., Hoffman, Eric A., Humphries, Stephen, Jacobson, Francine L., Judy, Philip F., Kazerooni, Ella A., Kluiber, Alex, Lynch, David A., Newell, John D., Regan, Elizabeth A., Ross, James C., San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stinson, Douglas, Stoel, Berend C., Tschirren, Juerg, Van Beek, Edwin, Ginneken, Bram van, van Rikxoort, Eva, Washko, George, Wilson, Carla G., Jensen, Robert, Crooks, Jim, Everett, Douglas, Moore, Camille, Strand, Wilson, Carla G., Hokanson, John E., Hughes, John, Kinney, Gregory, Lutz, Sharon M., Pratte, Katherine, Young, Kendra A., Bhatt, Surya, Bon, Jessica, Han, MeiLan K., Make, Barry J., Martinez, Carlos, Murray, Susan, Regan, Elizabeth A., Soler, Xavier, Wilson, Carla G., Banaei-Kashani, Farnoush, Bowler, Russell P., Kechris, Katerina, Curtis, Jeffrey L., Pernicano, Perry G., Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Parulekar, Amit, DeMeo, Dawn L., Diaz, Alejandro A., Hayden, Lystra P., Hobbs, Brian D., Hersh, Craig, Jacobson, Francine L., Washko, George, Barr, R. Graham, Austin, John, D’Souza, Belinda, Thomashow, Byron, MacIntyre, Neil, McAdams, H. Page, Washington, Lacey, McEvoy, Charlene, Tashjian, Joseph, Wise, Robert, Brown, Robert, Hansel, Nadia N., Horton, Karen, Lambert, Allison, Putcha, Nirupama, Casaburi, Richard, Adami, Alessandra, Budoff, Matthew, Fischer, Hans, Porszasz, Janos, Rossiter, Harry, Stringer, William, DeBakey, Michael E., Sharafkhaneh, Amir, Lan, Charlie, Wendt, Christine, Bell, Brian, Kunisaki, Ken M., Foreman, Marilyn G., Berkowitz, Eugene, Westney, Gloria, Bowler, Russell, Lynch, David A., Rosiello, Richard, Pace, David, Criner, Gerard, Ciccolella, David, Cordova, Francis, Dass, Chandra, D’Alonzo, Gilbert, Desai, Parag, Jacobs, Michael, Kelsen, Steven, Kim, Victor, Mamary, A. James, Marchetti, Nathaniel, Satti, Aditi, Shenoy, Kartik, Steiner, Robert M., Swift, Alex, Swift, Irene, Vega-Sanchez, Maria Elena, Dransfield, Mark, Bailey, William, Bhatt, Surya P., Iyer, Anand, Nath, Hrudaya, Wells, J. Michael, Ramsdell, Joe, Friedman, Paul, Soler, Xavier, Yen, Andrew, Comellas, Alejandro P., Hoth, Karin F., Newell, John, Thompson, Brad, Han, MeiLan K., Kazerooni, Ella, Martinez, Carlos H., Billings, Joanne, Begnaud, Abbie, Allen, Tadashi, Sciurba, Frank, Bon, Jessica, Chandra, Divay, Fuhrman, Carl, Weissfeld, Joel, Anzueto, Antonio, Adams, Sandra, Maselli-Caceres, Diego, and Ruiz, Mario E.

Abstract

Smokers manifest varied phenotypes of pulmonary impairment.

Published
2021
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26. Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT

Author

Venkadesh, Kiran Vaidhya, Setio, Arnaud A. A., Schreuder, Anton, Scholten, Ernst T., Chung, Kaman, Wille, Mathilde M. W., Saghir, Zaigham, Ginneken, Bram van, Prokop, Mathias, and Jacobs, Colin

Abstract

A deep learning algorithm, developed for malignancy risk estimation of pulmonary nodules detected at screening CT, demonstrated good discriminative performance in a large independent validation set.

Published
2021
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28. Optimum Management of Pulmonary Nodules

Author

Arts-assistenten Radiologie, MS Radiologie, Brain, Circulatory Health, Cancer, Van Riel, Sarah J., Scholten, Ernst Th, Prokop, Mathias, Mali, Willem P., Ginneken, Bram van, Schaefer-Prokop, Cornelia M., Arts-assistenten Radiologie, MS Radiologie, Brain, Circulatory Health, Cancer, Van Riel, Sarah J., Scholten, Ernst Th, Prokop, Mathias, Mali, Willem P., Ginneken, Bram van, and Schaefer-Prokop, Cornelia M.

Published
2017

29. Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

Author

Lessmann, Nikolas, Sánchez, Clara I., Beenen, Ludo, Boulogne, Luuk H., Brink, Monique, Calli, Erdi, Charbonnier, Jean-Paul, Dofferhoff, Ton, Everdingen, Wouter M. van, Gerke, Paul K., Geurts, Bram, Gietema, Hester A., Groeneveld, Miriam, Harten, Louis van, Hendrix, Nils, Hendrix, Ward, Huisman, Henkjan J., Išgum, Ivana, Jacobs, Colin, Kluge, Ruben, Kok, Michel, Krdzalic, Jasenko, Lassen-Schmidt, Bianca, Leeuwen, Kicky van, Meakin, James, Overkamp, Mike, Vellinga, Tjalco van Rees, Rikxoort, Eva M. van, Samperna, Riccardo, Schaefer-Prokop, Cornelia, Schalekamp, Steven, Scholten, Ernst Th., Sital, Cheryl, Stöger, J. Lauran, Teuwen, Jonas, Venkadesh, Kiran Vaidhya, Vente, Coen de, Vermaat, Marieke, Xie, Weiyi, Wilde, Bram de, Prokop, Mathias, and Ginneken, Bram van

Abstract

The Coronavirus Disease 2019 (COVID-19) Reporting and Data System (CO-RADS) artificial intelligence system is a freely accessible deep learning algorithm that automatically assigns CO-RADS and CT severity scores to nonenhanced CT scans of patients suspected of having COVID-19 with high diagnostic performance.

Published
2021
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30. Genetic Association and Risk Scores in a Chronic Obstructive Pulmonary Disease Meta-analysis of 16,707 Subjects

Author

Busch, Robert, primary, Hobbs, Brian D., additional, Zhou, Jin, additional, Castaldi, Peter J., additional, McGeachie, Michael J., additional, Hardin, Megan E., additional, Hawrylkiewicz, Iwona, additional, Sliwinski, Pawel, additional, Yim, Jae-Joon, additional, Kim, Woo Jin, additional, Kim, Deog K., additional, Agusti, Alvar, additional, Make, Barry J., additional, Crapo, James D., additional, Calverley, Peter M., additional, Donner, Claudio F., additional, Lomas, David A., additional, Wouters, Emiel F., additional, Vestbo, Jørgen, additional, Tal-Singer, Ruth, additional, Bakke, Per, additional, Gulsvik, Amund, additional, Litonjua, Augusto A., additional, Sparrow, David, additional, Paré, Peter D., additional, Levy, Robert D., additional, Rennard, Stephen I., additional, Beaty, Terri H., additional, Hokanson, John, additional, Silverman, Edwin K., additional, Cho, Michael H., additional, Crapo, James, additional, Silverman, Edwin, additional, Make, Barry, additional, Regan, Elizabeth, additional, Beaty, Terri, additional, Laird, Nan, additional, Lange, Christoph, additional, Cho, Michael, additional, Santorico, Stephanie, additional, DeMeo, Dawn, additional, Hansel, Nadia, additional, Hersh, Craig, additional, Castaldi, Peter, additional, McDonald, Merry-Lynn, additional, Wan, Emily, additional, Hardin, Megan, additional, Hetmanski, Jacqueline, additional, Parker, Margaret, additional, Foreman, Marilyn, additional, Hobbs, Brian, additional, Busch, Robert, additional, El-Bouiez, Adel, additional, Qiao, Dandi, additional, Halper-Stromberg, Eitan, additional, Begum, Ferdouse, additional, Won, Sungho, additional, Lutz, Sharon, additional, Lynch, David A., additional, Coxson, Harvey O., additional, Han, MeiLan K., additional, Hoffman, Eric A., additional, Humphries, Stephen, additional, Jacobson, Francine L., additional, Judy, Philip F., additional, Kazerooni, Ella A., additional, Newell, John D., additional, Ross, James C., additional, San Jose Estepar, Raul, additional, Stoel, Berend C., additional, Tschirren, Juerg, additional, Rikxoort, Eva van, additional, Ginneken, Bram van, additional, Washko, George, additional, Wilson, Carla G., additional, Al Qaisi, Mustafa, additional, Gray, Teresa, additional, Kluiber, Alex, additional, Mann, Tanya, additional, Sieren, Jered, additional, Stinson, Douglas, additional, Schroeder, Joyce, additional, Van Beek, Edwin, additional, Jensen, Robert, additional, Everett, Douglas, additional, Faino, Anna, additional, Strand, Matt, additional, Wilson, Carla, additional, Hokanson, John E., additional, Kinney, Gregory, additional, Young, Kendra, additional, Pratte, Katherine, additional, Duca, Lindsey, additional, Curtis, Jeffrey L., additional, Martinez, Carlos H., additional, Pernicano, Perry G., additional, Hanania, Nicola, additional, Alapat, Philip, additional, Bandi, Venkata, additional, Atik, Mustafa, additional, Boriek, Aladin, additional, Guntupalli, Kalpatha, additional, Guy, Elizabeth, additional, Parulekar, Amit, additional, Nachiappan, Arun, additional, Jacobson, Francine, additional, Barr, R. Graham, additional, Thomashow, Byron, additional, Austin, John, additional, D'Souza, Belinda, additional, Pearson, Gregory D. N., additional, Rozenshtein, Anna, additional, MacIntyre, Neil, additional, Washington, Lacey, additional, McAdams, H. Page, additional, McEvoy, Charlene, additional, Tashjian, Joseph, additional, Wise, Robert, additional, Brown, Robert, additional, Horton, Karen, additional, Putcha, Nirupama, additional, Casaburi, Richard, additional, Adami, Alessandra, additional, Porszasz, Janos, additional, Fischer, Hans, additional, Budoff, Matthew, additional, Rossiter, Harry, additional, Sharafkhaneh, Amir, additional, Lan, Charlie, additional, Wendt, Christine, additional, Bell, Brian, additional, Westney, Gloria, additional, Berkowitz, Eugene, additional, Bowler, Russell, additional, Lynch, David, additional, Rosiello, Richard, additional, Pace, David, additional, Criner, Gerard, additional, Ciccolella, David, additional, Cordova, Francis, additional, Dass, Chandra, additional, D'Alonzo, Gilbert, additional, Desai, Parag, additional, Jacobs, Michael, additional, Kelsen, Steven, additional, Kim, Victor, additional, Mamary, A. James, additional, Marchetti, Nathaniel, additional, Satti, Aditi, additional, Shenoy, Kartik, additional, Steiner, Robert M., additional, Swift, Alex, additional, Swift, Irene, additional, Vega-Sanchez, Maria Elena, additional, Dransfield, Mark, additional, Bailey, William, additional, Wells, J. Michael, additional, Bhatt, Surya, additional, Nath, Hrudaya, additional, Ramsdell, Joe, additional, Friedman, Paul, additional, Soler, Xavier, additional, Yen, Andrew, additional, Comellas, Alejandro, additional, Newell, John, additional, Thompson, Brad, additional, Han, MeiLan, additional, Kazerooni, Ella, additional, Martinez, Carlos, additional, Billings, Joanne, additional, Allen, Tadashi, additional, Sciurba, Frank, additional, Chandra, Divay, additional, Weissfeld, Joel, additional, Fuhrman, Carl, additional, Bon, Jessica, additional, Anzueto, Antonio, additional, Adams, Sandra, additional, Maselli-Caceres, Diego, additional, Ruiz, Mario E., additional, Sauleda, Jaume, additional, Calverley, Peter M. A., additional, Rennard, Stephen, additional, Ivanov, Y., additional, Kostov, K., additional, Bourbeau, J., additional, Fitzgerald, M., additional, Hernandez, P., additional, Killian, K., additional, Levy, R., additional, Maltais, F., additional, O'Donnell, D., additional, Krepelka, J., additional, Vestbo, J., additional, Wouters, E., additional, Quinn, D., additional, Bakke, P., additional, Kosnik, M., additional, Agusti, A., additional, Sauleda, J., additional, Feschenko, Y., additional, Gavrisyuk, V., additional, Yashina, L., additional, Monogarova, N., additional, Calverley, P., additional, Lomas, D., additional, MacNee, W., additional, Singh, D., additional, Wedzicha, J., additional, Anzueto, A., additional, Braman, S., additional, Casaburi, R., additional, Celli, B., additional, Giessel, G., additional, Gotfried, M., additional, Greenwald, G., additional, Hanania, N., additional, Mahler, D., additional, Make, B., additional, Rennard, S., additional, Rochester, C., additional, Scanlon, P., additional, Schuller, D., additional, Sciurba, F., additional, Sharafkhaneh, A., additional, Siler, T., additional, Silverman, E., additional, Wanner, A., additional, Wise, R., additional, ZuWallack, R., additional, Coxson, H., additional, Crim, C., additional, Edwards, L., additional, Tal Singer, R., additional, Yates, J., additional, Miller, B., additional, Tal-Singer, R., additional, Benditt, J., additional, Criner, G., additional, DeCamp, M., additional, Diaz, P., additional, Ginsburg, M., additional, Kaiser, L., additional, Katz, M., additional, Krasna, M., additional, MacIntyre, N., additional, McKenna, R., additional, Martinez, F., additional, Mosenifar, Z., additional, Reilly, J., additional, Ries, A., additional, and Utz, J., additional

Published
2017
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31. Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin

Author

Ghafoorian, Mohsen, primary, Karssemeijer, Nico, additional, Heskes, Tom, additional, Bergkamp, Mayra, additional, Wissink, Joost, additional, Obels, Jiri, additional, Keizer, Karlijn, additional, Leeuw, Frank-Erik de, additional, Ginneken, Bram van, additional, Marchiori, Elena, additional, and Platel, Bram, additional

Published
2017
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32. COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System

Author

Murphy, Keelin, Smits, Henk, Knoops, Arnoud J. G., Korst, Michael B. J. M., Samson, Tijs, Scholten, Ernst T., Schalekamp, Steven, Schaefer-Prokop, Cornelia M., Philipsen, Rick H. H. M., Meijers, Annet, Melendez, Jaime, Ginneken, Bram van, and Rutten, Matthieu

Abstract

An artificial intelligence system (CAD4COVID-XRay) can identify characteristics of coronavirus disease 2019 on chest radiographs with performance comparable to that of six readers.

Published
2020
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33. CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19—Definition and Evaluation

Author

Prokop, Mathias, Everdingen, Wouter van, Vellinga, Tjalco van Rees, Quarles van Ufford, Henriëtte, Stöger, Lauran, Beenen, Ludo, Geurts, Bram, Gietema, Hester, Krdzalic, Jasenko, Schaefer-Prokop, Cornelia, Ginneken, Bram van, and Brink, Monique

Abstract

The Coronavirus disease 2019 (COVID-19) Reporting and Data System is a categorical assessment scheme for chest CT in patients suspected of having COVID-19, representing the level of suspicion for pulmonary involvement; the substantial agreement among observers and its discriminatory value make it well-suited for use in clinical practice.

Published
2020
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34. Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction

Author

Leeuwen, Kicky G. van, Schalekamp, Steven, Rutten, Matthieu J. C. M., Huisman, Merel, Schaefer-Prokop, Cornelia M., Rooij, Maarten de, Ginneken, Bram van, Maresch, Bas, Geurts, Bram H. J., Dijke, Cornelius F. van, Laupman-Koedam, Emmeline, Hulleman, Enzo V., Verhoeff, Eric L., Meys, Evelyne M. J., Mohamed Hoesein, Firdaus A. A., Brugge, Floor M. ter, Hoorn, Francois van, Wel, Frank van der, Berk, Inge A. H. van den, Luyendijk, Jacqueline M., Meakin, James, Habets, Jesse, Verbeke, Jonathan I. M. L., Nederend, Joost, Meys, Karlijn M. E., Deden, Laura N., Langezaal, Lucianne C. M., Nasrollah, Mahtab, Meij, Marleen, Boomsma, Martijn F., Vermeulen, Matthijs, Vestering, Myrthe M., Vijlbrief, Onno, Algra, Paul, Algra, Selma, Bollen, Stijn M., Samson, Tijs, and Fock, Yntor H. G. von Brucken

Abstract

In independent validation, nine artificial intelligence products for detecting lung nodules on chest radiographs or predicting bone age on hand radiographs showed improved or comparable performance to human readers.

Published
2024
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35. Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT

Author

Murphy, Keelin, Pluim, Josien P.W., Van Rikxoort, Eva M., De Jong, Pim A., De Hoop, Bartjan, Gietema, Hester A., Mets, Onno, De Bruijne, Marleen, Lo, Pechin, Prokop, Mathias, Ginneken, Bram Van, Radiology & Nuclear Medicine, and Medical Informatics

Abstract

Purpose : To analyze pulmonary function using a fully automatic technique which processes pairs of thoracic CT scans acquired at breath-hold inspiration and expiration, respectively. The following research objectives are identified to: (a) describe and systematically analyze the processing pipeline and its results; (b) verify that the quantitative, regional ventilation measurements acquired through CT are meaningful for pulmonary function analysis; (c) identify the most effective of the calculated measurements in predicting pulmonary function; and (d) demonstrate the potential of the system to deliver clinically important information not available through conventional spirometry. Methods : A pipeline of automatic segmentation and registration techniques is presented and demonstrated on a database of 216 subjects well distributed over the various stages of COPD (chronic obstructive pulmonary disorder). Lungs, fissures, airways, lobes, and vessels are automatically segmented in both scans and the expiration scan is registered with the inspiration scan using a fully automatic nonrigid registration algorithm. Segmentations and registrations are examined and scored by expert observers to analyze the accuracy of the automatic methods. Quantitative measures representing ventilation are computed at every image voxel and analyzed to provide information about pulmonary function, both globally and on a regional basis. These CT derived measurements are correlated with results from spirometry tests and used as features in a kNN classifier to assign COPD global initiative for obstructive lung disease (GOLD) stage. Results : The steps of anatomical segmentation (of lungs, lobes, and vessels) and registration in the workflow were shown to perform very well on an individual basis. All CT-derived measures were found to have good correlation with spirometry results, with several having correlation coefficients, r, in the range of 0.85-0.90. The best performing kNN classifier succeeded in classifying 67% of subjects into the correct COPD GOLD stage, with a further 29% assigned to a class neighboring the correct one. Conclusions : Pulmonary function information can be obtained from thoracic CT scans using the automatic pipeline described in this work. This preliminary demonstration of the system already highlights a number of points of clinical importance such as the fact that an inspiration scan alone is not optimal for predicting pulmonary function. It also permits measurement of ventilation on a per lobe basis which reveals, for example, that the condition of the lower lobes contributes most to the pulmonary function of the subject. It is expected that this type of regional analysis will be instrumental in advancing the understanding of multiple pulmonary diseases in the future.

Published
2012

37. Contribution of CT Quantified Emphysema, Air Trapping and Airway Wall Thickness on Pulmonary Function in Male Smokers With and Without COPD

Author

Mohamed Hoesein, Firdaus A.A., primary, de Jong, Pim A., additional, Lammers, Jan-Willem J., additional, Mali, Willem P. Th. M., additional, Mets, Onno M., additional, Schmidt, Michael, additional, de Koning, Harry J., additional, Aalst, Carlijn van der, additional, Oudkerk, Matthijs, additional, Vliegenthart, Rozemarijn, additional, Ginneken, Bram van, additional, van Rikxoort, Eva M., additional, and Zanen, Pieter, additional

Published
2014
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38. Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules

Author

Vaidhya Venkadesh, Kiran, Aleef, Tajwar Abrar, Scholten, Ernst T., Saghir, Zaigham, Silva, Mario, Sverzellati, Nicola, Pastorino, Ugo, Ginneken, Bram van, Prokop, Mathias, and Jacobs, Colin

Abstract

A deep learning algorithm trained to estimate 3-year malignancy risk of screening-detected pulmonary nodules using current and prior low-dose CT examinations outperformed validated models that used a single CT examination.

Published
2023
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39. Extraction of airways from CT (EXACT’09)

Author

Lo, Pechin Chien Pau, Ginneken, Bram van, Reinhardt, Joseph M., Yavarna, Tarunashree, Jong, Pim A. de, Irving, Benjamin, Fetita, Catalin, Ortner, Margarete, Pinho, Rômulo, Sijbers, Jan, Feuerstein, Marco, Fabijanska, Anna, Bauer, Christian, Beichel, Reinhard, Mendoza, Carlos S., Wiemker, Rafael, Lee, Jaesung, Reeves, Anthony P., Born, Silvia, Weinheimer, Oliver, Rikxoort, Eva M. van, Tschirren, Juerg, Mori, Ken, Odry, Benjamin, Naidich, David P., Hartmann, Ieneke, Hoffman, Eric A., Prokop, Mathias, Pedersen, Jesper H., de Bruijne, Marleen, Lo, Pechin Chien Pau, Ginneken, Bram van, Reinhardt, Joseph M., Yavarna, Tarunashree, Jong, Pim A. de, Irving, Benjamin, Fetita, Catalin, Ortner, Margarete, Pinho, Rômulo, Sijbers, Jan, Feuerstein, Marco, Fabijanska, Anna, Bauer, Christian, Beichel, Reinhard, Mendoza, Carlos S., Wiemker, Rafael, Lee, Jaesung, Reeves, Anthony P., Born, Silvia, Weinheimer, Oliver, Rikxoort, Eva M. van, Tschirren, Juerg, Mori, Ken, Odry, Benjamin, Naidich, David P., Hartmann, Ieneke, Hoffman, Eric A., Prokop, Mathias, Pedersen, Jesper H., and de Bruijne, Marleen

Abstract

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate 15 different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of 20 chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

Published
2012

40. Pixel level image fusion for medical imaging: An energy minimizing approach

Author

Ginneken, Bram van, Miles, Brandon, Law, Max W. K., Ben-Ayed, Ismail, Garvin, Greg, Fenster, Aaron, Li, Shuo, Ginneken, Bram van, Miles, Brandon, Law, Max W. K., Ben-Ayed, Ismail, Garvin, Greg, Fenster, Aaron, and Li, Shuo

Abstract

In an attempt to improve the visualisation techniques for diagnosis and treatment of musculoskeletal injuries, we present a novel method for a pixel-wise fusion of CT and MR images. We focus on the spine and its related diseases including osteophyte growth, degenerate disc disease and spinal stenosis. This will have benefit to the 50-75% of people who suffer from back pain, which is the reason for 1.8% of all hospital stays in the United States.1 Pre-registered CT and MR image pairs were used. Rigid registration was performed based on soft tissue correspondence. A pixel-wise image fusion algorithm was designed to combine CT and MR images into a single image. This was accomplished by minimizing an energy functional using a graph cut approach. The functional was formulated to balance the similarity between the resultant image and the CT image as well as between the resultant image and the MR image. Furthermore the variational smoothness of the resultant image was considered in the energy functional (to enforce natural transitions between pixels). The results have been validated based on the amount of significant detail preserved in the final fused image. Based on bone cortex and disc / spinal cord areas, 95% of the relevant MR detail and 85% of the relevant CT detail was preserved. This work has the potential to aid in patient diagnosis, surgery planning and execution along with post operative follow up.

Published
2012

42. Diagnosis of chronic obstructive pulmonary disease in lung cancer screening Computed Tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening.

Author

Mets, Onno M., Schmidt, Michael, Buckens, Constantinus F., Gondrie, Martijn J., Isgum, Ivana, Oudkerk, Matthijs, Vliegenthart, Rozemarijn, de Koning, Harry J., van der Aalst, Carlijn M., Prokop, Mathias, Lammers, Jan-Willem J., Zanen, Pieter, Hoesein, Firdaus A. Mohamed, Mali, Willem PThM, Ginneken, Bram van, Rikxoort, Eva M. van, and de Jong, Pim A.

Subjects
OBSTRUCTIVE lung diseases, LUNG cancer diagnosis, COMPUTED tomography, PULMONARY emphysema, HEALTH, SMOKING, BIOMARKERS, SPIROMETRY
Abstract

Background: Beyond lung cancer, screening CT contains additional information on other smoking related diseases (e.g. chronic obstructive pulmonary disease, COPD). Since pulmonary function testing is not regularly incorporated in lung cancer screening, imaging biomarkers for COPD are likely to provide important surrogate measures for disease evaluation. Therefore, this study aims to determine the independent diagnostic value of CT emphysema, CT air trapping and CT bronchial wall thickness for COPD in low-dose screening CT scans. Methods: Prebronchodilator spirometry and volumetric inspiratory and expiratory chest CT were obtained on the same day in 1140 male lung cancer screening participants. Emphysema, air trapping and bronchial wall thickness were automatically quantified in the CT scans. Logistic regression analysis was performed to derivate a model to diagnose COPD. The model was internally validated using bootstrapping techniques. Results: Each of the three CT biomarkers independently contributed diagnostic value for COPD, additional to age, body mass index, smoking history and smoking status. The diagnostic model that included all three CT biomarkers had a sensitivity and specificity of 73.2% and 88.%, respectively. The positive and negative predictive value were 80.2% and 84.2%, respectively. Of all participants, 82.8% was assigned the correct status. The C-statistic was 0.87, and the Net Reclassification Index compared to a model without any CT biomarkers was 44.4%. However, the added value of the expiratory CT data was limited, with an increase in Net Reclassification Index of 4.5% compared to a model with only inspiratory CT data. Conclusion: Quantitatively assessed CT emphysema, air trapping and bronchial wall thickness each contain independent diagnostic information for COPD, and these imaging biomarkers might prove useful in the absence of lung function testing and may influence lung cancer screening strategy. Inspiratory CT biomarkers alone may be sufficient to identify patients with COPD in lung cancer screening setting. [ABSTRACT FROM AUTHOR]

Published
2013
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43. Rate of progression of CT-quantified emphysema in male current and ex-smokers: a follow-up study.

Author

Hoesein, Firdaus A. A. Mohamed, Zanen, Pieter, de Jong, Pim A., Ginneken, Bram van, Boezen, H. Marike, Groen, Harry J. M., Oudkerk, Mathijs, de Koning, Harry J., Postma, Dirkje S., and Lammers, Jan-Willem J.

Subjects
OBSTRUCTIVE lung diseases, PULMONARY emphysema, PULMONARY function tests, EX-smokers, SMOKING cessation, COMPUTED tomography, HEALTH
Abstract

Background: Little is known about the factors associated with CT-quantified emphysema progression in heavy smokers. The objective of this study was to investigate the effect of length of smoking cessation and clinical / demographical factors on the rate of emphysema progression and FEV1-decline in male heavy smokers. Methods: 3,670 male smokers with mean (SD) 40.8 (17.9) packyears underwent chest CT scans and pulmonary function tests at baseline and after 1 and 3 years follow-up. Smoking status (quitted ⩾5, ⩾1-<5, <1 years or current smoker) was noted. Rate of progression of emphysema and FEV1-decline after follow-up were assessed by analysis of variance adjusting for age, height, baseline pulmonary function and emphysema severity, packyears, years in study and respiratory symptoms. The quitted ⩾5 group was used as reference. Results: Median (Q1-Q3) emphysema severity,<-950 HU, was 8.8 (5.1 - 14.1) and mean (SD) FEV1 was 3.4 (0.73) L or 98.5 (18.5) % of predicted. The group quitted '>5 years' showed significantly lower rates of progression of emphysema compared to current smokers, 1.07% and 1.12% per year, respectively (p<0.001). Current smokers had a yearly FEV1-decline of 69 ml, while subjects quit smoking >5 years had a yearly decline of 57.5 ml (p<0.001). Conclusion: Quit smoking >5 years significantly slows the rate of emphysema progression and lung function decline. [ABSTRACT FROM AUTHOR]

Published
2013
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