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Article

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

1
Department of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea
2
Department of Pathology, Seoul National University Hospital, Seoul 03080, Korea
3
Department of Pathology, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Gyeonggi-do 14647, Korea
4
Department of Pathology, SMG-SNU Boramae Medical Center, Seoul 07061, Korea
5
Deep Bio Inc., 1201 HanWha BizMetro, 242, Digital-ro, Guro-gu, Seoul 08394, Korea
6
Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
7
Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this paper.
Cancers 2019, 11(12), 1860; https://doi.org/10.3390/cancers11121860
Received: 11 October 2019 / Revised: 13 November 2019 / Accepted: 21 November 2019 / Published: 25 November 2019
(This article belongs to the Special Issue Prostate Cancer: Past, Present, and Future)
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. View Full-Text
Keywords: gleason scoring system; deep neural network; prostate cancer; prostate core needle biopsy gleason scoring system; deep neural network; prostate cancer; prostate core needle biopsy
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MDPI and ACS Style

Ryu, H.S.; Jin, M.-S.; Park, J.H.; Lee, S.; Cho, J.; Oh, S.; Kwak, T.-Y.; Woo, J.I.; Mun, Y.; Kim, S.W.; Hwang, S.; Shin, S.-J.; Chang, H. Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment. Cancers 2019, 11, 1860. https://doi.org/10.3390/cancers11121860

AMA Style

Ryu HS, Jin M-S, Park JH, Lee S, Cho J, Oh S, Kwak T-Y, Woo JI, Mun Y, Kim SW, Hwang S, Shin S-J, Chang H. Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment. Cancers. 2019; 11(12):1860. https://doi.org/10.3390/cancers11121860

Chicago/Turabian Style

Ryu, Han S., Min-Sun Jin, Jeong H. Park, Sanghun Lee, Joonyoung Cho, Sangjun Oh, Tae-Yeong Kwak, Junwoo I. Woo, Yechan Mun, Sun W. Kim, Soohyun Hwang, Su-Jin Shin, and Hyeyoon Chang. 2019. "Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment" Cancers 11, no. 12: 1860. https://doi.org/10.3390/cancers11121860

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