需要可靠的预测标记来识别晚期非小细胞肺癌肿瘤（NSCLC）患者哪些人能实现耐用临床益处（DCB）的免疫化疗，本回顾性研究中，我们从2018年1月1日至2022年5月31日，收集了94例接受抗PD-1/PD-L1联合化疗治疗的晚期NSCLC患者的放射荧光和临床记录。放射荧光变量是从预处理CT中提取的，并通过Spearman相关系数和Logistics回归分析选择了临床特征。我们执行有效的诊断算法主成分分析（PCA）和支持向量机（SVM），以在DCB和非持久获益（NDB）组之间开发早期分类模型。共选择了26个放射荧光特征和6个临床特征，然后使用主成分分析来获得6个主成分以用于SVM构建。RC-SVM在训练集中达到0.91的AUC预测准确性（95% CI 0.87-0.94），在交叉验证集中达到0.73的AUC（95% CI 0.61-0.85），在外部验证集中达到0.84的AUC（95% CI 0.80-0.89）。基于放射荧光-临床记录签名的RC-SVM模型提供了对NSCLC患者免疫化疗前响应预测的重要增值。©2023年作者。

The reliable predictive markers to identify which patients with advanced non-small cell lung cancer tumors (NSCLC) will achieve durable clinical benefit (DCB) for chemo-immunotherapy are needed. In this retrospective study, we collected radiomics and clinical signatures from 94 patients with advanced NSCLC treated with anti-PD-1/PD-L1 combined with chemotherapy from January 1, 2018 to May 31, 2022. Radiomics variables were extracted from pretreatment CT and selected by Spearman correlation coefficients and clinical features by Logistics regression analysis. We performed effective diagnostic algorithms principal components analysis (PCA) and support vector machine (SVM) to develop an early classification model among DCB and non-durable benefit (NDB) groups. A total of 26 radiomics features and 6 clinical features were selected, and then principal component analysis was used to obtain 6 principal components for SVM building. RC-SVM achieved prediction accuracy with AUC of 0.91 (95% CI 0.87-0.94) in the training set, 0.73 (95% CI 0.61-0.85) in the cross-validation set, 0.84 (95% CI 0.80-0.89) in the external validation set. The new method of RC-SVM model based on radiomics-clinical signatures provides a significant additive value on response prediction in patients with NSCLC preceding chemo-immunotherapy.© 2023. The Author(s).