Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade.

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 develop

SUBMITTER: Arbour KC 

PROVIDER: S-EPMC7981277 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature