免疫检查点抑制剂（ICIs）彻底改变了非小细胞肺癌（NSCLC）的治疗。然而，免疫相关的不良事件仍然发生，其中检查点抑制剂肺炎（CIP）是最常见的。我们的目的是构建并验证基于对比增强计算机断层扫描的放射组学列线图，以预测 NSCLC ICI 治疗前发生 CIP 的概率。我们回顾性分析了 685 名最初接受 ICI 治疗的 NSCLC 患者。我们的研究总共纳入了 186 名患者，另外还考虑了来自另一家医院的另外 52 名患者进行外部验证。在放射组学特征提取和选择之后，我们应用支持向量机分类模型来区分 CIP 并使用概率作为放射组学特征。使用过滤后的临床参数和放射组学特征构建放射组学-临床逻辑回归模型。接受者操作特征、曲线下面积 (AUC)、校准曲线和决策曲线分析用于模型间比较。使用年龄、间质性肺疾病、基线肺气肿和放射组学特征构建的组合放射组学-临床模型显示出训练、验证和外部验证队列的 AUC 分别为 0.935、0.905 和 0.923。与仅临床模型（AUC分别为0.829、0.826和0.809）和仅放射组学模型（0.865、0.847和0.841）相比，放射组学-临床模型显示出更好的预测能力。这种组合的放射组学-临床模型预测了CIP的概率在 NSCLC 患者的 ICI 治疗期间具有良好的准确性，因此可以用作指导临床 ICI 决策的有效工具。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC). However, immune-related adverse events still occur, of which checkpoint inhibitor pneumonitis (CIP) is the most common. We aimed to construct and validate a contrast-enhanced computed tomography-based radiomic nomogram to predict the probability of CIP before ICIs treatment in NSCLC.We retrospectively analyzed 685 patients with NSCLC who were initially treated with ICIs. A total of 186 patients were included in our study, and an additional 52 patients from another hospital were considered for external validation. After radiomics feature extraction and selection, we applied a support vector machine classification model to distinguish CIP and used the probability as a radiomics signature. A radiomics-clinical logistic regression model was built using the filtered clinical parameters and a radiomic signature. Receiver operating characteristic, area under the curve (AUC), calibration curve, and decision curve analysis was used for inter-model comparison.The combined radiomics-clinical model constructed using age, interstitial lung disease, emphysema at baseline, and radiomics signature showed an AUC of 0.935, 0.905, and 0.923 for the training, validation, and external validation cohorts, respectively. Compared with the clinical-only (AUC of 0.829, 0.826, and 0.809) and radiomics-only models (0.865, 0.847, and 0.841), the radiomics-clinical displayed better predictive power.This combined radiomics-clinical model predicted the probability of CIP during ICIs treatment in patients with NSCLC with favorable accuracy and could therefore be used as an effective tool to guide clinical ICIs decisions.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.