Presentation + Paper
16 March 2020 Automatic cancer sub-grading on digital histopathology images of radical prostatectomy specimens
Author Affiliations +
Abstract
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.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
W. Han, M. Downes, T. H. van der Kwast, J. L. Chin, S. E. Pautler, and A. D. Ward "Automatic cancer sub-grading on digital histopathology images of radical prostatectomy specimens", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200H (16 March 2020); https://doi.org/10.1117/12.2551529
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KEYWORDS
Tissues

Cancer

Pathology

Oncology

Error analysis

Prostate cancer

Medical research

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