Frontiers in immunology · 2026
Pretreatment CT radiomics for prediction of pathological complete response after neoadjuvant chemoimmunotherapy in NSCLC: a multicenter study incorporating habitat analysis.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zheng Sunyi, Huang Shuyuan, Zhang Jiahui, Yang Xiaomeng, Ye PengChun, Wang Weiping, Li Wenhua, Liu Houpu, Zhang Chunlin, Liao Guoqing, Zhou Haiyu, Wang Jing, Cui Xiaonan, Yue Dongsheng
Аннотация
BACKGROUND: Pathological complete response (pCR) after neoadjuvant chemoimmunotherapy (NACI) varies among patients with non-small cell lung cancer (NSCLC). This study aimed to quantify intratumoral heterogeneity using habitat analysis and to develop a pretreatment model for predicting pCR.
METHODS: In this retrospective multicenter study, 287 patients with NSCLC who received NACI followed by surgery were included. Patients were randomly divided into a training cohort (n = 147) and an internal validation cohort (n = 63), with an independent external test cohort (n = 77). K-means clustering was used to partition tumors into three habitat subregions based on entropy. Radiomics features were extracted from pretreatment CT images at the whole-tumor and habitat levels. Whole-tumor, habitat-based, and combined models were developed to predict pCR. Model performance was evaluated using area under the curve (AUC) analysis, and model interpretability was assessed using Shapley Additive exPlanations (SHAP).
RESULTS: The habitat-based model showed acceptable predictive performance across the training, internal validation, and external test cohorts, with AUCs of 0.74, 0.71, and 0.67, respectively, and showed higher AUCs compared with the whole-tumor radiomics model, which achieved AUCs of 0.72, 0.68, and 0.65. The combined model achieved performance comparable to the habitat-based model. SHAP analysis suggested that, among the habitat-derived features, the most influential predictive features were primarily associated with the high-entropy habitat subregion.
CONCLUSION: The habitat-based model shows potential performance for predicting pCR, and features from a high-entropy habitat subregion may provide complementary imaging information for preoperative assessment of treatment response.
METHODS: In this retrospective multicenter study, 287 patients with NSCLC who received NACI followed by surgery were included. Patients were randomly divided into a training cohort (n = 147) and an internal validation cohort (n = 63), with an independent external test cohort (n = 77). K-means clustering was used to partition tumors into three habitat subregions based on entropy. Radiomics features were extracted from pretreatment CT images at the whole-tumor and habitat levels. Whole-tumor, habitat-based, and combined models were developed to predict pCR. Model performance was evaluated using area under the curve (AUC) analysis, and model interpretability was assessed using Shapley Additive exPlanations (SHAP).
RESULTS: The habitat-based model showed acceptable predictive performance across the training, internal validation, and external test cohorts, with AUCs of 0.74, 0.71, and 0.67, respectively, and showed higher AUCs compared with the whole-tumor radiomics model, which achieved AUCs of 0.72, 0.68, and 0.65. The combined model achieved performance comparable to the habitat-based model. SHAP analysis suggested that, among the habitat-derived features, the most influential predictive features were primarily associated with the high-entropy habitat subregion.
CONCLUSION: The habitat-based model shows potential performance for predicting pCR, and features from a high-entropy habitat subregion may provide complementary imaging information for preoperative assessment of treatment response.