Abstract
Ovarian carcinoma is a leading driver of cancer-related mortality in women, but predicting long-term survival at diagnosis remains a challenge. We demonstrate that AI-powered pathology can classify cells and tissue regions in the high-grade serous carcinoma (HGSC) tumor microenvironment, and reveal nuclear morphology (NM) associated with patient outcomes, directly from digitized hematoxylin and eosin (H&E)-stained whole slide images (WSI).
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