Improving prostate cancer classification in H&E tissue micro arrays using Ki67 and P63 histopathology. (December 2020)

• Record Type: Journal Article
• Authors: Shao, Yanan; Nir, Guy; Fazli, Ladan; Goldenberg, Larry; Gleave, Martin; Black, Peter; Wang, Jane; Salcudean, Septimiu
• Is Part Of: Computers in biology and medicine. Volume 127(2020)
• Publication Date: 2020-12
• Subjects: Digital pathology -- H&E -- Ki67 -- Multi-stain -- p63 -- Prostate cancer classification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.104053
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Histopathology of Hematoxylin and Eosin (H&E)-stained tissue obtained from biopsy is commonly used in prostate cancer (PCa) diagnosis. Automatic PCa classification of digitized H&E slides has been developed before, but no attempts have been made to classify PCa using additional tissue stains registered to H&E. In this paper, we demonstrate that using H&E, Ki67 and p63-stained (3-stain) tissue improves PCa classification relative to H&E alone. We also show that we can infer PCa-relevant Ki67 and p63 information from the H&E slides alone, and use it to achieve H&E-based PCa classification that is comparable to the 3-stain classification. Reported improvements apply to classifying benign vs. malignant tissue, and low grade (Gleason group 2) vs. high grade (Gleason groups 3, 4, 5) cancer. Specifically, we conducted four classification tasks using 333 tissue samples extracted from 231 radical prostatectomy patients: regression tree-based classification using either (i) 3-stain features, with a benign vs malignant area under the curve (AUC = 92.9%), or (ii) real H&E features and H&E features learned from Ki67 and p63 stains (AUC = 92.4%), as well as deep learning classification using either (iii) real 3-stain tissue patches (AUC = 94.3%) and (iv) real H&E patches and generated Ki67 and p63 patches (AUC = 93.0%) using a deep convolutional generative adversarial network. Classification performance was assessed with Monte Carlo cross validation and quantified in terms of the Area Under the Curve, Brier score, sensitivity, and specificity. Our results are interpretable and indicate that the standard H&E classification could be improved by mimicking other stain types. Highlights: H&E, Ki67, and p63-based prostate cancer classification improves upon H&E alone. Ki67 and p63 histopathology stain information can be inferred from H&E stain. Modified U-Net provides new interpretable features for H&E histopathology.