探讨基于计算机断层扫描(computed tomography, CT)肿瘤内部生境影像组学特征量化肿瘤内部异质性(intratumoral heterogeneity, ITH)对非小细胞肺癌(nonsmall cell lung cancer, NSCLC)患者免疫检查点抑制剂(immune checkpoint inhibitors, ICIs)治疗反应性的预测价值。

选择2022年6月至2025年6月我院拟行帕博利珠单抗联合铂类双药治疗NSCLC患者148例作为训练集，同期患者98例为验证集。治疗前采集CT图像并提取生境影像组学特征，采用LASSO回归筛选核心特征构建ITHscore模型。治疗结束后根据RECIST标准评估疗效，将训练集完全缓解和部分缓解分为缓解组79例，疾病稳定和疾病进展分为未缓解组69例。比较两组临床资料，采用单因素及多因素Logistic回归分析影响ICIs治疗反应性的独立因素，通过受试者特征曲线(receiver operating characteristic, ROC)和决策曲线(decision curve analysis, DCA)判断预测效能。

训练集中缓解组与未缓解组年龄、性别、临床分期及程序性死亡配体1(programmed deathligand 1, PD-L1)表达水平差异有统计学意义(P<0.05)。LASSO回归从1524个放射组学特征中筛选出8个非零系数核心特征构建ITHscore。多因素Logistic回归显示，年龄(OR＝1.145，95%CI：1.031~1.272，P＝0.011)、性别(OR＝2.447，95%CI：1.079~5.550，P＝0.032)、临床分期(OR＝3.555，95%CI：1.581~7.992，P＝0.002)、PDL1表达(OR＝0.887，95%CI：0.847~0.929，P＝0.007)及ITHscore(OR＝1.895，95%CI：1.261~2.848，P＝0.002)为影响ICIs治疗反应性的危险因素。ROC分析显示，ITHscore模型AUC为0.818(95%CI：0.737~0.882)，临床特征模型AUC为0.793(95%CI：0.710~0.862)(P>0.05)；联合模型AUC为0.896(95%CI：0.827~0.944)，敏感度75.61%，特异度91.23%，优于单一模型。验证集中联合模型AUC为0.879(95%CI：0.807~0.931)，敏感度70.73%，特异度92.41%。DCA显示，当阈值概率为0.25时，联合模型净获益0.27高于ITHscore模型0.23和临床特征模型0.11。

基于CT肿瘤内部异质性影像组学特征构建的ITHscore联合临床特征模型预测NSCLC患者ICIs治疗反应性区分能力和临床净获益良好，有助于早期识别免疫治疗获益人群。

To investigate the predictive value of intratumoral heterogeneity (ITH) quantified by computed tomography (CT)-based habitat radiomic features for the response to immune checkpoint inhibitors (ICIs) in patients with non-small cell lung cancer (NSCLC).

A total of 148 NSCLC patients scheduled to receive pembrolizumab plus platinum-based doublet chemotherapy at our hospital from June 2022 to June 2025 were enrolled as the training cohort, and another 98 patients treated during the same period served as the validation cohort. Pre-treatment CT images were acquired and habitat radiomic features were extracted. LASSO regression was used to select core features for constructing the ITHscore model. After treatment completion, treatment response was assessed according to RECIST criteria. In the training cohort, patients with complete response or partial response were classified as the response group (n=79), while those with stable disease or progressive disease were classified as the nonresponse group (n=69). Clinical data were compared between the two groups. Univariate and multivariate logistic regression analyses were performed to identify independent factors associated with ICI response. Predictive performance was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

In the training cohort, significant differences were observed between the response and nonresponse groups in age, sex, clinical stage, and programmed deathligand 1 (PDL1) expression level (all P<0.05). LASSO regression selected 8 core features with nonzero coefficients from 1, 524 radiomic features to construct the ITHscore. Multivariate logistic regression showed that age (OR=1.145, 95%CI: 1.031~1.272, P=0.011), sex (OR=2.447, 95%CI: 1.079~5.550, P=0.032), clinical stage (OR=3.555, 95%CI: 1.581~7.992, P=0.002), PDL1 expression (OR=0.887, 95%CI: 0.847~0.929, P=0.007), and ITHscore (OR=1.895, 95%CI: 1.261~2.848, P=0.002) were independent predictors of ICI response. ROC analysis showed that the AUC of the ITHscore model was 0.818 (95%CI: 0.737~0.882), and that of the clinical feature model was 0.793 (95%CI: 0.710~0.862) (P>0.05). The combined model achieved an AUC of 0.896 (95%CI: 0.827~0.944), with a sensitivity of 75.61% and specificity of 91.23%, outperforming either single model. In the validation cohort, the combined model yielded an AUC of 0.879(95%CI: 0.807~0.931), sensitivity of 70.73%, and specificity of 92.41%. DCA demonstrated that at a threshold probability of 0.25, the net benefit of the combined model (0.27) was higher than that of the ITHscore model (0.23) and the clinical feature model (0.11).

The ITHscore combined with clinical features, constructed from CTbased intratumoral heterogeneity radiomic features, demonstrates good discriminative ability and clinical net benefit for predicting ICI response in NSCLC patients, and may facilitate early identification of patients likely to benefit from immunotherapy.