TY - JOUR
T1 - PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images
AU - Armato, Samuel G.
AU - Huisman, Henkjan
AU - Drukker, Karen
AU - Hadjiiski, Lubomir
AU - Kirby, Justin S.
AU - Petrick, Nicholas
AU - Redmond, George
AU - Giger, Maryellen L.
AU - Cha, Kenny
AU - Mamonov, Artem
AU - Kalpathy-Cramer, Jayashree
AU - Farahani, Keyvan
N1 - Funding Information:
The authors would like to express their appreciation to Angela Keyser and Shayna Knazik from the AAPM and Diane Cline and Robbine Waters from SPIE for their assistance in making these challenges a reality. The PROSTATEx Challenges were supported by SPIE, NCI, and AAPM. The authors are grateful to the groups that participated in the PROSTATEx Challenges, including groups led by Bejoy Abraham and Madhu S. Nair (University of Kerala), Jason Adleberg (Drexel University College of Medicine), Ruhallah Amandi and Mohammad Farhadi (Amirkabir University of Technology), Yoganand Balagurunathan (H.L. Moffitt Cancer Center), Adrian Barbu (Florida State University), Lei Bi (University of Sydney), Vy Bui (The Catholic University of America), Quan Chen (University of Virginia), King Chung Ho and Karthik Sarma (University of California, Los Angeles), Andy Kitchen, Panagiotis Korfiatis (Mayo Clinic), Peter S. LaViolette (Medical College of Wisconsin), Nathan Lay (National Institutes of Health), Chao Li (Huazhong University of Science and Technology), Qing Liang (Temple University), Saifeng Liu (The MRI Institute for Biomedical Research), Sean D. McGarry (Medical College of Wisconsin), Alireza Mehrtash (Brigham and Women’s Hospital), Mira Park (The University of Newcastle), N. Andres Parra (Moffitt Cancer Center), Yue Miao (University of Electronic Science and Technology of China), Hung Le Minh (Huazhong University of Science and Technology), Jin Qi and Miao Le (University of Electronic Science and Technology of China), Jarrel Chen Yi Seah (Alfred Health), Piotr Sobecki (Warsaw University of Technology), Radka Stoyanova (University of Miami), Yu Sun (Peter MacCallum Cancer Centre), Ning Wen (Henry Ford Health System), Xinran Zhong (University of California, Los Angeles), and Delong Zhu (Robotic Geometry Research Group CUHK). These (and the other) participating groups deserve recognition for their work to develop their prostate lesion classification and grading methods and for their contributions to the success of the challenges. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. The challenge platform and JKC were funded in part by NIH/NCI U01CA154601, U24CA180927, and U24CA180918 and a contract (HHSN26120080001E) from Leidos Biomedical Research. This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.
Publisher Copyright:
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from -0.24 to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.
AB - Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from -0.24 to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.
KW - Gleason Grade Group
KW - grand challenge
KW - imaging biomarker
KW - lesion classification
KW - multiparametric magnetic resonance images
KW - prostate cancer
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U2 - 10.1117/1.JMI.5.4.044501
DO - 10.1117/1.JMI.5.4.044501
M3 - Article
AN - SCOPUS:85056752803
SN - 2329-4302
VL - 5
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 4
M1 - 044501
ER -