A deep learning framework to iDentify prOgnostically releVant cancEr Regions (DOVER) within whole slide histopathology images

Abstract

The recent advancements in computational pathology focus on extracting valuable prognostic insights from whole-slide images (WSIs). These methods primarily involve deep learning-based or handcrafted feature representations of the disease's morphologic patterns associated with outcomes. However, determining the most prognostic regions within tumors remains challenging due to significant morphologic heterogeneity even within manually annotated tumor areas. In other words, the question is not simply what type of representation is appropriate to predict cancer outcomes, but specifically where to mine those representations. To address this issue, a deep learning framework to identify prognostically relevant (PR) cancer regions (DOVER) within WSIs is presented. DOVER leverages patterns mined from the tissue microarray (TMA) spots with the associated long-term clinical outcomes. The prognostic patterns learned from the individual spots of the TMA (morphologically consistent) are then mapped into larger WSIs to locate PR regions for subsequent feature representation and patient outcome prediction. DOVER improves prognostic prediction in terms of c-index over 20 % (p < 0.05) across 2041 patients (NSCLC: n = 1141; OPSCC: n = 900). Moreover, correlations with quantitative immunofluorescent (QIF) images reveal a diverse CD8+, CD20+, CD4+, and tumor cell distribution in DOVER-selected regions, reflecting a complex interplay between tumor and immune cells. DOVER identifies statistically significant differences between PR regions, both at the molecular and morphological levels. DOVER could help identify specific spatial locations on WSIs that could be used to mine prognostic feature representation for subsequent predictions of clinical outcomes. With additional validation, DOVER could also potentially help to guide AI-informed molecular profiling of tumors.