Abstract
Early assessment of treatment response is essential for optimizing cancer management, as it allows timely interventions during the course of therapy, potentially improving cancer control and clinical outcomes. In this study, we aimed to develop and validate a machine learning model integrating radiomics, dosiomics, and clinical characteristics to predict response to radiotherapy in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC). A total of 222 patients across multiple centers who received radiotherapy for NSCLC were enrolled and divided into training (n = 110), internal validation (n = 28), and external validation (n = 84) cohorts. Objective response rate (ORR) and progression-free survival (PFS) were predicted using models based on radiomics, dosiomics, and clinical characteristics. Model performance was evaluated using receiver operating characteristic (ROC) curves, DeLong test, decision curve analysis (DCA), Kaplan-Meier survival analysis, and Integrated Brier Score (IBS). The clinical models, CORR and CPFS (both based on planning target volume [PTV] and lymphocyte count) were compared with combined radiomics-dosiomics-clinical models (RDCORR and RDCPFS). For ORR prediction, RDCORR achieved AUCs of 0.901, 0.894, and 0.869 in the training, internal validation, and external validation cohorts, outperforming CORR (AUCs of 0.723, 0.606, and 0.723), with p ORR outperformed CORR, providing a higher overall net benefit. For PFS prediction, RDCPFS yielded higher concordance indices (0.805, 0.730, and 0.743 in the training, internal validation, and external validation cohorts) than CPFS (0.679, 0.699, and 0.640, respectively). RDCPFS showed the lowest IBS across all cohorts (0.092, 0.107, and 0.093, respectively) compared with CPFS and the reference model, indicating better predictive accuracy. The combined model integrating radiomics, dosiomics, and clinical characteristics enhances the prediction of radiotherapy response in locally advanced, unresectable NSCLC, facilitating improved patient monitoring and more effective adjuvant clinical trial design.