Biologic and Prognostic Feature Scores from Whole-Slide Histology Images Using Deep Learning
This work addresses the need for digital biomarkers in precision medicine for prostate cancer, though it is incremental as it applies existing deep learning models to new datasets.
The study introduced deep learning-derived feature scores from prostate histology images, which were significantly prognostic for biochemical recurrence and cancer-specific survival, with concrete associations to genomic alterations and molecular subtypes.
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.