Automatic Cancer Sub-Grading On Digital Histopathology Images Of Radical Prostatectomy Specimens

Automatic cancer sub-grading of radical prostatectomy (RP) specimens can support clinical studies seeking the prognostic indications of the sub-grades, and potentially benefits patient risk management and treatment planning. We developed and validated an automatic system which classifies each of nine subgrades (i.e. 4 sub-grades of Gleason grade 3, 3 sub-grades of Gleason grade 4, benign intervening, and other cancerous tissue) on digital histopathology whole-slide images (WSIs). The system was cross-validated against expert-drawn contours on a 25-patient data set comprising 92 mid-gland WSIs of RP specimens. The system used a transfer learning technique by fine-tuning AlexNet to classify each cancerous region of interest (ROI) according to sub-grade. We used leave-one-WSI-out cross-validation to measure classifier performance. The system yielded an area under the receiver-operating characteristic curve (AUC) higher than 0.8 for sub-grades of small fused Gleason 4 (G4), intermediate G3, and other cancerous tissue (AUC of 0.976); and AUCs higher than 0.7 for sub-grades of sparse G3, large cribriform G4, and desmoplastic G3.