ArticleLa Radiologia medica2026
CT radiomics machine learning for predicting treatment response in unresectable stage III-IV NSCLC: insights from dual chemo-immunotherapy and immunotherapy cohorts.
Article in La Radiologia medica, 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
purposeThe purpose of this study was to develop a machine learning model based on radiomic features extracted from baseline contrast-enhanced CT scans for predicting early treatment responses in two cohorts of patients with stage III-IV NSCLC treated with distinct therapeutic regimens (first-line chemo-immunotherapy or immunotherapy alone).
methodsIn this retrospective bicentric study including two cohorts, patients with a confirmed diagnosis of advanced NSCLC (stage III-IV) were retrospectively collected. At Center 1, after application of the exclusion criteria, 90 eligible patients (52 treated with CHT/IT and 38 with IT) were included for model development and internal validation. An independent external cohort from Center 2 comprised 40 additional patients, including 20 treated with CHT/IT and 20 with IT, and was used for external validation. Radiomic features were extracted from segmented tumor volumes using the Trace4Research™ platform, and multiple machine learning models, including random forest, support vector machine, K-nearest neighbors, multilayer perceptron, and logistic regression, were trained and compared.
resultsIn the CHT/IT cohort, seven radiomic features were retained after outlier removal. Among the tested models, the support vector machine (SVM) classifier showed the best performance, with a ROC-AUC of 0.90 (95% CI: 0.82-0.97), accuracy of 82%, sensitivity of 82%, specificity of 83%, positive predictive value of 67%, and negative predictive value of 94% (p < 0.005). In the IT cohort, 29 radiomic features were retained, and the SVM again achieved the best performance, with a ROC-AUC of 0.84 (95% CI: 0.73-0.95), accuracy of 77%, sensitivity of 82%, specificity of 70%, positive predictive value of 80%, and negative predictive value of 83% (p < 0.05). External validation supported the robustness and generalizability of both models.
conclusionsMachine learning models based on CT-derived radiomic features may enable accurate and noninvasive early prediction of treatment response in patients with advanced NSCLC receiving chemo-immunotherapy or immunotherapy alone, supporting the potential role of radiomics as an imaging biomarker for treatment stratification and personalized therapeutic planning.
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