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In Cancer discovery ; h5-index 105.0

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. Using text reports from patients with non-small cell lung cancer treated with PD-1 blockade in a training cohort and two test cohorts, we developed a deep learning model to accurately estimate best overall response and progression-free survival. Our model may be a tool to determine outcomes at scale, enabling analyses of large clinical databases.

Arbour Kathryn C, Luu Anh Tuan, Luo Jia, Rizvi Hira, Plodkowski Andrew J, Sakhi Mustafa, Huang Kevin B, Digumarthy Subba R, Ginsberg Michelle S, Girshman Jeffrey, Kris Mark G, Riely Gregory J, Yala Adam, Gainor Justin F, Barzilay Regina, Hellmann Matthew D