Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials.

ABSTRACT: Understanding factors that impact prognosis for cancer patients have high clinical relevance for treatment decisions and monitoring of the disease outcome. Advances in artificial intelligence (AI) and digital pathology offer an exciting opportunity to capitalize on the use of whole slide images (WSIs) of hematoxylin and eosin (H&E) stained tumor tissue for objective prognosis and prediction of response to targeted therapies. AI models often require hand-delineated annotations for effective training which may not be readily available for larger data sets. In this study, we investigated whether AI models can be trained without region-level annotations and solely on patient-level survival data. We present a weakly supervised survival convolutional neural network (WSS-CNN) approach equipped wi

SUBMITTER: Qaiser T 

PROVIDER: S-EPMC9200764 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature