Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade
Kathryn C. Arbour, Anh Tuan Luu, Jia Luo, Hira Rizvi, Andrew J. Plodkowski, Mustafa Sakhi, Kevin Huang, Subba R. Digumarthy, Michelle S. Ginsberg, Jeffrey Girshman, Mark G. Kris, Gregory J. Riely, Adam Yala, Justin F. Gainor, Regina Barzilay, Matthew D. Hellmann
Abstract
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 datasets. 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 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. Significance: We developed and validated a deep-learning model trained on radiology text reports to estimate gold-standard objective response categories used in clinical trial assessments. This tool may facilitate analysis of large real-world oncology datasets using objective outcome metrics determined more reliably and at greater scale than currently possible. This article is highlighted in the In This Issue feature, p. 1