肺癌 (LC) 是癌症相关死亡的主要原因，免疫疗法 (IO) 在治疗晚期 LC 方面已显示出前景。然而，识别可能受益于 IO 的患者并监测治疗反应仍然具有挑战性。本研究旨在根据临床特征和先进影像生物标志物开发 IO LC 患者无进展生存期 (PFS) 的预测模型。对 206 名接受 IO 治疗的 LC 患者进行回顾性分析。治疗前计算机断层扫描图像用于提取高级成像生物标志物，包括瘤内和瘤周脉管系统放射组学。还收集了临床特征，包括年龄、基因状态、血液学和分期。使用两步特征选择过程确定了预测 IO 结果的关键放射组学和临床特征，包括单变量 Cox 回归和卡方检验，然后进行顺序前向选择。 DeepSurv 模型的构建是为了根据临床和放射组学特征预测 PFS。使用时间依赖性受试者工作特征曲线 (AUC) 和一致性指数 (C-index) 下的面积来评估模型性能。将瘤内异质性和瘤周血管系统的放射组学与临床特征相结合，证明模型的显着增强 (p<0.001)预测 IO 响应。所提出的 DeepSurv 模型表现出预测性能，AUC 范围为 0.76 至 0.80，C 指数为 0.83。此外，预测的个性化 PFS 曲线显示，预后良好和不良的患者之间存在显着差异 (p<0.05)。将瘤内和瘤周血管放射组学与临床特征相结合，能够开发 IO 的 LC 患者的 PFS 预测模型。所提出的模型能够估计个性化 PFS 概率并区分预后状态，有望促进个性化医疗并改善 LC 患者的治疗结果。© 2024。作者。

Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers.A retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index).Combining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses.Integrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model's capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.© 2024. The Author(s).