The pathologic diagnosis and Gleason grading of prostate cancer are time-consuming, error-prone, and subject to interobserver variability. Machine learning offers opportunities to improve the diagnosis, risk stratification, and prognostication of prostate cancer.
To develop a state-of-the-art deep learning algorithm for the histopathologic diagnosis and Gleason grading of prostate biopsy specimens.
A total of 85 prostate core biopsy specimens from 25 patients were digitized at 20× magnification and annotated for Gleason 3, 4, and 5 prostate adenocarcinoma by a urologic pathologist. From these virtual slides, we sampled 14803 image patches of 256×256 pixels, approximately balanced for malignancy.
We trained and tested a deep residual convolutional neural network to classify each patch at two levels: (1) coarse (benign vs malignant) and (2) fine (benign vs Gleason 3 vs 4 vs 5). Model performance was evaluated using fivefold cross-validation. Randomization tests were used for hypothesis testing of model performance versus chance.
The model demonstrated 91.5% accuracy (p<0.001) at coarse-level classification of image patches as benign versus malignant (0.93 sensitivity, 0.90 specificity, and 0.95 average precision). The model demonstrated 85.4% accuracy (p<0.001) at fine-level classification of image patches as benign versus Gleason 3 versus Gleason 4 versus Gleason 5 (0.83 sensitivity, 0.94 specificity, and 0.83 average precision), with the greatest number of confusions in distinguishing between Gleason 3 and 4, and between Gleason 4 and 5. Limitations include the small sample size and the need for external validation.
In this study, a deep learning-based computer vision algorithm demonstrated excellent performance for the histopathologic diagnosis and Gleason grading of prostate cancer.
We developed a deep learning algorithm that demonstrated excellent performance for the diagnosis and grading of prostate cancer.
European urology focus. 2019 Nov 22 [Epub ahead of print]
Ohad Kott, Drew Linsley, Ali Amin, Andreas Karagounis, Carleen Jeffers, Dragan Golijanin, Thomas Serre, Boris Gershman
Minimally Invasive Urology Institute, The Miriam Hospital, Providence, RI, USA., Carney Institute for Brain Science, Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, RI, USA., Department of Pathology and Laboratory Medicine, The Miriam Hospital, Providence, RI, USA; Warren Alpert Medical School of Brown University, Providence, RI, USA., Minimally Invasive Urology Institute, The Miriam Hospital, Providence, RI, USA; Warren Alpert Medical School of Brown University, Providence, RI, USA; Division of Urology, Rhode Island Hospital and The Miriam Hospital, Providence, RI, USA., Division of Urologic Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA. Electronic address: .