Biologic and Prognostic Feature scores from Whole-Slide Histology Images Using Deep Learning
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Histopathology is a reflection of the molecular changes and provides prognostic phenotypes representing the disease progression. In this study, we introduced feature scores generated from hematoxylin and eosin histology images based on deep learning (DL) models developed for prostate pathology. We demonstrated that these feature scores are significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and have simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that are not exposed to the datasets of the current study. Further, we will discuss the potential of such feature scores to improve the current tumor grading system and the challenges that are associated with tumor heterogeneity and the development of prognostic models from histology images. Our findings uncover the potential of feature scores from histology images as digital biomarkers in precision medicine and as an expanding utility for digital pathology.
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