  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 were significantly prognostic for time
to event endpoints (biochemical recurrence and cancer-specific survival) and
had simultaneously molecular biologic associations to relevant genomic
alterations and molecular subtypes using already trained DL models that were
not previously exposed to the datasets of the current study. Further, we
discussed 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.
