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

ABSTRACT: 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 syst

SUBMITTER: Ryu HS 

PROVIDER: S-EPMC6966453 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature