ArticleScientific reports2026
Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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, C
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