This article was originally published here Cancer Discov. ABSTRACT Real-world evidence (RWE) – conclusions derived from analysis of patients not treated in clinical trials – is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE may be facilitated through machine learning techniques to integrate and interrogate large and otherwise underutilized data sets. In cancer research, an ongoing challenge for RWE is the lack of reliable, reproducible, scalable assessment of treatment-specific outcomes. We hypothesized a deep learning model could be trained to use radiology text reports to estimate gold-standard Response Evaluation Criteria in Solid Tumors (RECIST)-defined outcomes.