Identifying Survival Phenotypes in High-Grade Serous Ovarian Cancer by Application of AI to Digital Pathology
Our preliminary models, trained on an internal cohort of approximately 90 patients with high-grade serous ovarian cancer, attain an AUROC between 0.65-0.73 in predicting the extremes of survival classification (Short-term survival - STS: < 2 years; LTS: > 7 years). Initial technical modifications have thus far yielded incremental improvements of 0.05-0.15 in the AUROC per iteration on our internal test set. With each improvement in performance, we have noted that model attention narrows; we plan additional pathologist-led analyses to identify trends in these regions of attention.
Provided we are successful in predicting survival from H&E WSIs and able to effectively validate on external datasets such as PLCO, we plan to develop a web-based interface to establish our models as a tool in clinical practice, providing patients with real-time prognostic estimations.
1. To determine the optimal machine learning architecture to classify H&E WSIs of ovarian cancer into survival phenotypes
2. To identify and characterize the regions with H&E WSIs of ovarian cancer that are most predictive of survival
Ernst Lengyel, MD PhD, University of Chicago
Leonhard Donle, PhD, University of Chicago
Hilary Kenny, PhD, University of Chicago
Alex Pearson, MD, University of Chicago
Emma Rose Carpenter, University of Chicago
Elena Nevins, University of Chicago