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Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment

 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 
 CANCERS, Vol.11(12) : 1860, 2019-11 
Journal Title
Issue Date
deep neural network ; gleason scoring system ; prostate cancer ; prostate core needle biopsy
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.
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1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
Yonsei Authors
Shin, Su Jin(신수진) ORCID logo https://orcid.org/0000-0001-9114-8438
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