Stable and discriminating features are predictive of cancer presence and Gleason grade in radical prostatectomy specimens: a multi-site study

Scientific Reports. 2018;8(1):1-13 DOI 10.1038/s41598-018-33026-5

 

Journal Homepage

Journal Title: Scientific Reports

ISSN: 2045-2322 (Online)

Publisher: Nature Publishing Group

LCC Subject Category: Medicine | Science

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Patrick Leo (Case Western Reserve University, Dept. of Biomedical Engineering)
Robin Elliott (Case Western Reserve University, Dept. of Pathology)
Natalie N. C. Shih (University of Pennsylvania, Dept. of Pathology)
Sanjay Gupta (Case Western Reserve University, Dept. of Urology)
Michael Feldman (University of Pennsylvania, Dept. of Pathology)
Anant Madabhushi (Case Western Reserve University, Dept. of Biomedical Engineering)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 20 weeks

 

Abstract | Full Text

Abstract Site variation in fixation, staining, and scanning can confound automated tissue based image classifiers for disease characterization. In this study we incorporated stability into four feature selection methods for identifying the most robust and discriminating features for two prostate histopathology classification tasks. We evaluated 242 morphology features from N = 212 prostatectomy specimens from four sites for automated cancer detection and grading. We quantified instability as the rate of significant cross-site feature differences. We mapped feature stability and discriminability using 188 non-cancerous and 210 cancerous regions via 3-fold cross validation, then held one site out, creating independent training and testing sets. In training, one feature set was selected only for discriminability, another for discriminability and stability. We trained a classifier with each feature set, testing on the hold out site. Experiments were repeated with 117 Gleason grade 3 and 112 grade 4 regions. Stability was calculated across non-cancerous regions. Gland shape features yielded the best stability and area under the receiver operating curve (AUC) trade-off while co-occurrence texture features were generally unstable. Our stability-informed method produced a cancer detection AUC of 0.98 ± 0.05 and increased average Gleason grading AUC by 4.38%. Color normalization of the images tended to exacerbate feature instability.