目标 制定基于CT加权放射组学模型，预测接受PD-1/PD-ligand 1(PD-L1)免疫疗法的非小细胞肺癌患者的肿瘤反应。 方法 回顾性研究北京协和医院2015年6月至2022年2月期间接受PD-1/PD-L1免疫检查点抑制剂治疗的非小细胞肺癌患者，并对其进行分类处理，分类为反应者（部分或完全反应）和非反应者（稳定或进展性疾病）。从动态增强CT扫描的动脉期中提取多个肺内病灶的原始放射组学特征，然后通过基于注意力的多实例学习算法进行加权和求和。采用逻辑回归建立加权放射组学评分模型，然后计算放射组学评分。采用接受者操作特征曲线下的面积（AUC）比较加权放射组学评分模型、PD-L1模型、临床模型、加权放射组学评分+PD-L1模型和综合预测模型。 结果 共纳入237例患者，随机分为训练组(n=165)和测试组(n=72)，平均年龄分别为（64±9）岁和（62±8）岁。加权放射组学评分模型在训练组和测试组的AUC分别为0.85和0.80，高于PD-L1-1模型(Z=37.30，P<0.001和Z=5.69，P=0.017)，PD-L1-50模型(Z=38.36，P<0.001和Z=17.99，P<0.001)，以及临床模型(Z=11.40，P<0.001和Z=5.76，P=0.016)。加权评分模型的AUC与加权放射组学评分+PD-L1模型和综合预测模型的AUC无显著差异（P>0.05）。 结论 基于治疗前增强CT图像的加权放射组学评分能够预测非小细胞肺癌患者对免疫疗法的肿瘤反应。

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer. Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model. Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05). Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.