The Gleason grading system, currently the most powerful prognostic predictor of prostate cancer, is based solely on the tumor's histological architecture and has high inter-observer variability. We propose an automated Gleason scoring system based on deep neural networks for diagnosis of prostate core needle biopsy samples. To verify its efficacy, the system was trained using 1133 cases of prostate core needle biopsy samples and validated on 700 cases. Further, system-based diagnosis results were compared with reference standards derived from three certified pathologists. In addition, the system's ability to quantify cancer in terms of tumor length was also evaluated via comparison with pathologist-based measurements. The results showed a substantial diagnostic concordance between the system-grade group classification and the reference standard (0.907 quadratic-weighted Cohen's kappa coefficient). The system tumor length measurements were also notably closer to the reference standard (correlation coefficient, R = 0.97) than the original hospital diagnoses (R = 0.90). We expect this system to assist pathologists to reduce the probability of over- or under-diagnosis by providing pathologist-level second opinions on the Gleason score when diagnosing prostate biopsy, and to support research on prostate cancer treatment and prognosis by providing reproducible diagnosis based on the consistent standards.
Cancers. 2019 Nov 25*** epublish ***
Han Suk Ryu, Min-Sun Jin, Jeong Hwan Park, Sanghun Lee, Joonyoung Cho, Sangjun Oh, Tae-Yeong Kwak, Junwoo Isaac Woo, Yechan Mun, Sun Woo Kim, Soohyun Hwang, Su-Jin Shin, Hyeyoon Chang
Department of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea., Department of Pathology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Gyeonggi-do 14647, Korea., Deep Bio Inc., 1201 HanWha BizMetro, 242, Digital-ro, Guro-gu, Seoul 08394, Korea., Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea., Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea.