Machine Learning and Computed Tomography Radiomics to Predict Disease Progression to Upfront Pembrolizumab Monotherapy in Advanced Non-Small-Cell Lung Cancer: A Pilot Study

Ian Janzen, Cheryl Ho, Barbara Melosky, Qian Ye, Jessica Li, Gang Wang, Stephen Lam, Calum MacAulay, Ren Yuan

Background/Objectives: Pembrolizumab monotherapy is approved in Canada for first-line treatment of advanced NSCLC with PD-L1 ≥ 50% and no EGFR/ALK aberrations. However, approximately 55% of these patients do not respond to pembrolizumab, underscoring the need for the early intervention of non-responders to optimize treatment strategies. Distinguishing the 55% sub-cohort prior to treatment is a real-world dilemma. Methods: In this retrospective study, we analyzed two patient cohorts treated with pembrolizumab monotherapy (training set: n = 97; test set: n = 17). The treatment response was assessed using baseline and follow-up CT scans via RECIST 1.1 criteria. Results: A logistic regression model, incorporating pre-treatment CT radiomic features of lung tumors and clinical variables, achieved high predictive accuracy (AUC: 0.85 in training; 0.81 in testing, 95% CI: 0.63–0.99). Notably, radiomic features from the peritumoral region were found to be independent predictors, complementing the standard CT evaluations and other clinical characteristics. Conclusions: This pragmatic model offers a valuable tool to guide first-line treatment decisions in NSCLC patients with high PD-L1 expression and has the potential to advance personalized oncology and improve timely disease management.