Harnessing Big Data with Machine Learning in Precision Oncology.

Abstract

While multi-level molecular "omic" analyses have undoubtedly increased the sophistication and depth with which we can understand cancer biology, the challenge is to make this overwhelming wealth of data relevant to the clinician and the individual patient. Bridging this gap serves as the cornerstone of precision medicine, yet the expense and difficulty of executing and interpreting these molecular studies make it impractical to routinely implement them in the clinical setting. Herein, we propose that machine learning may hold the key to guiding the future of precision oncology accurately and efficiently. Training deep learning models to interpret the histopathologic or radiographic appearance of tumors and their microenvironment-a phenotypic microcosm of their inherent molecular biology-has the potential to output relevant diagnostic, prognostic, and therapeutic patient-level data. This type of artificial intelligence framework may effectively shape the future of precision oncology by fostering multidisciplinary collaboration.