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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

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 co...

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Autores principales: Ryu, Han Suk, Jin, Min-Sun, Park, Jeong Hwan, Lee, Sanghun, Cho, Joonyoung, Oh, Sangjun, Kwak, Tae-Yeong, Woo, Junwoo Isaac, Mun, Yechan, Kim, Sun Woo, Hwang, Soohyun, Shin, Su-Jin, Chang, Hyeyoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966453/
https://www.ncbi.nlm.nih.gov/pubmed/31769420
http://dx.doi.org/10.3390/cancers11121860
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author Ryu, Han Suk
Jin, Min-Sun
Park, Jeong Hwan
Lee, Sanghun
Cho, Joonyoung
Oh, Sangjun
Kwak, Tae-Yeong
Woo, Junwoo Isaac
Mun, Yechan
Kim, Sun Woo
Hwang, Soohyun
Shin, Su-Jin
Chang, Hyeyoon
author_facet Ryu, Han Suk
Jin, Min-Sun
Park, Jeong Hwan
Lee, Sanghun
Cho, Joonyoung
Oh, Sangjun
Kwak, Tae-Yeong
Woo, Junwoo Isaac
Mun, Yechan
Kim, Sun Woo
Hwang, Soohyun
Shin, Su-Jin
Chang, Hyeyoon
author_sort Ryu, Han Suk
collection PubMed
description 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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spelling pubmed-69664532020-01-27 Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment Ryu, Han Suk Jin, Min-Sun Park, Jeong Hwan Lee, Sanghun Cho, Joonyoung Oh, Sangjun Kwak, Tae-Yeong Woo, Junwoo Isaac Mun, Yechan Kim, Sun Woo Hwang, Soohyun Shin, Su-Jin Chang, Hyeyoon Cancers (Basel) Article 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. MDPI 2019-11-25 /pmc/articles/PMC6966453/ /pubmed/31769420 http://dx.doi.org/10.3390/cancers11121860 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ryu, Han Suk
Jin, Min-Sun
Park, Jeong Hwan
Lee, Sanghun
Cho, Joonyoung
Oh, Sangjun
Kwak, Tae-Yeong
Woo, Junwoo Isaac
Mun, Yechan
Kim, Sun Woo
Hwang, Soohyun
Shin, Su-Jin
Chang, Hyeyoon
Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment
title Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment
title_full Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment
title_fullStr Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment
title_full_unstemmed Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment
title_short Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment
title_sort automated gleason scoring and tumor quantification in prostate core needle biopsy images using deep neural networks and its comparison with pathologist-based assessment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966453/
https://www.ncbi.nlm.nih.gov/pubmed/31769420
http://dx.doi.org/10.3390/cancers11121860
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