European journal of radiology open · 2026
Contrast-enhanced CT based radiomics for prediction of 3-year disease progression in NSCLC patients after anti-PD-1-targeted therapy.
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Huang Wen, Lin Min-Jia, He Yi-Ting, Zhou Hong, Chen Yi, Zong Min
Аннотация
PURPOSE: This study aimed to develop and validate a radiomics-based model derived from contrast-enhanced computed tomography (CE-CT) to predict 3-year disease progression in patients with non-small-cell lung cancer (NSCLC) receiving anti-PD1 immunotherapy.
METHODS: A total of 173 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled. We developed a integrated model based on Radscore by selecting radiomics features from target lesions (TL) and clinical features derived from pretreatment CE-CT images. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of different models. Model interpretability was enhanced via Shapley Additive Explanations (SHAP).
RESULTS: Chronic obstructive pulmonary disease (COPD) and tumor stage were identified as significant clinical predictors of 3-year disease progression. The radiomics model achieved area under the curve (AUC) values of 0.758 (95% CI: 0.663-0.852) and 0.815 (95% CI: 0.618-1.000) in training and testing cohorts, respectively. The integrated model showed improved performance, with AUCs of 0.802 (95% CI: 0.721-0.884) and 0.836 (95% CI: 0.663-1.000), respectively. The nomogram exhibited superior net clinical benefit compared to radiomics- or clinical-only models. SHAP analysis identified shape Sphericity, gldm Small Dependence High Gray Level Emphasis, glszm Gray Level Variance, glszm Small Area High Gray Level Emphasis as key imaging features associated with 3-year disease progression.
CONCLUSIONS: We developed an integrated clinical-radiomics model that effectively identifies NSCLC patients most likely to benefit from anti-PD-1 therapy. Using SHAP-based explainability, we clarified the contribution of imaging features, enabling more personalized treatment strategies.
METHODS: A total of 173 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled. We developed a integrated model based on Radscore by selecting radiomics features from target lesions (TL) and clinical features derived from pretreatment CE-CT images. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of different models. Model interpretability was enhanced via Shapley Additive Explanations (SHAP).
RESULTS: Chronic obstructive pulmonary disease (COPD) and tumor stage were identified as significant clinical predictors of 3-year disease progression. The radiomics model achieved area under the curve (AUC) values of 0.758 (95% CI: 0.663-0.852) and 0.815 (95% CI: 0.618-1.000) in training and testing cohorts, respectively. The integrated model showed improved performance, with AUCs of 0.802 (95% CI: 0.721-0.884) and 0.836 (95% CI: 0.663-1.000), respectively. The nomogram exhibited superior net clinical benefit compared to radiomics- or clinical-only models. SHAP analysis identified shape Sphericity, gldm Small Dependence High Gray Level Emphasis, glszm Gray Level Variance, glszm Small Area High Gray Level Emphasis as key imaging features associated with 3-year disease progression.
CONCLUSIONS: We developed an integrated clinical-radiomics model that effectively identifies NSCLC patients most likely to benefit from anti-PD-1 therapy. Using SHAP-based explainability, we clarified the contribution of imaging features, enabling more personalized treatment strategies.