ArticleTechnology in cancer research & treatment
Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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6 authors.
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Abstract
ObjectiveLung cancer is primarily categorized into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), each characterized by distinct therapeutic approaches and prognostic outcomes, particularly in stage III peripheral cases. This study aimed to develop predictive models utilizing clinical and radiomic data to preoperatively differentiate stage III peripheral SCLC from NSCLC.MethodWe conducted a retrospective analysis of 33 stage III peripheral SCLC cases and 99 stage III peripheral NSCLC cases treated at our hospital between January 2016 and July 2024. A total of 1037 radiomic features were extracted from contrast-enhanced CT scans. The cohort was divided into a training set (n = 92) and a test set (n = 40). Radiomic feature selection was performed using the LASSO algorithm, and nine machine learning models were evaluated. The optimal model was employed to compute the radiomics score (Rad-score) and construct a clinical model. A combined model, integrating clinical factors and radiomic features, was assessed for clinical utility through receiver operating characteristic (ROC) curve analysis (area under the curve, AUC), KS statistics and decision curve analysis (DCA). We externally validated the combined model in a group of 84 patients from another hospital.ResultsThe logistic regression-based combined model exhibited superior performance, achieving AUC values of 0.956, 0.775, and 0.841 for the combined, clinical, and radiomics models, respectively, within the training cohort, and 0.905, 0.864, and 0.732 in the test cohort. AUC for the combined model was 0.843 in the external validation cohort. The KS statistics and DCA indicated the clinical utility of the combined model, as evidenced by a Brier score of 0.115.ConclusionThe integration of clinical parameters and radiomics features within the combined model may hold significant potential for the preoperative differentiation of stage III peripheral SCLC from NSCLC.
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