我们旨在探究胸部CT放射学在预测一线EGFR酪氨酸激酶抑制剂（EGFR-TKI）失败后晚期非小细胞肺癌（NSCLC）患者表皮生长因子受体（EGFR）-T790M耐药突变价值。共纳入211名依赖于瘤组织检测（Cohort-1）和135名依赖于循环肿瘤DNA（ctDNA）检测（Cohort-2）的晚期NSCLC患者。Cohort-1用于建模，Cohort-2用于验证模型。从胸部无增强CT（NECT）和/或增强CT（CECT）上的肿瘤病灶提取放射学特征。我们使用了8个特征选择器和8个分类器算法建立了放射学模型。模型通过接受者操作特征曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）进行评估。CT外周位置和胸膜凹陷征形态学表现与EGFR-T790M相关。对于NECT、CECT和NECT+CECT的放射学特征，分别选择了LASSO和逐步逻辑回归、Boruta和支持向量机、LASSO和支持向量机的特征选择器和分类器算法以开发最优模型（AUC：0.844、0.811和0.897）。所有模型在校准曲线和DCA中均表现良好。Cohort-2模型的独立验证揭示了NECT和CECT模型单独预测ctDNA检测到的EGFR-T790M突变的能力有限（AUC：0.649、0.675），然而NECT+CECT放射学模型的AUC值令人满意（0.760）。本研究证实使用CT放射学特征预测EGFR-T790M耐药突变的可行性，这有助于指导个性化治疗策略。版权所有©2023大学影像医师协会。Elsevier Inc. 发表，版权所有。

We aim to explore the value of chest CT radiomics in predicting the epidermal growth factor receptor (EGFR)-T790M resistance mutation of advanced non-small cell lung cancer (NSCLC) patients after the failure of first-line EGFR-tyrosine kinase inhibitor (EGFR-TKI).A total of 211 and 135 advanced NSCLC patients with tumor tissue-based (Cohort-1) or circulating tumor DNA (ctDNA)-based (Cohort-2) EGFR-T790M testing were included, respectively. Cohort-1 was used for modeling and Cohort-2 was for models' validation. Radiomic features were extracted from tumor lesions on chest nonenhanced CT (NECT) and/or contrast-enhanced CT (CECT). We used eight feature selectors and eight classifier algorithms to establish radiomic models. Models were evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).CT morphological manifestations of peripheral location and pleural indentation sign were associated with EGFR-T790M. For NECT, CECT, and NECT+CECT radiomic features, the feature selector and classifier algorithms of LASSO and Stepwise logistic regression, Boruta and SVM, and LASSO and SVM were chosen to develop the optimal model, respectively (AUC: 0.844, 0.811, and 0.897). All models performed well in calibration curves and DCA. Independent validation of models in Cohort-2 revealed that both NECT and CECT models individually had limited power for predicting EGFR-T790M mutation detected by ctDNA (AUC: 0.649, 0.675), while the NECT+CECT radiomic model had a satisfactory AUC (0.760).This study proved the feasibility of using CT radiomic features to predict the EGFR-T790M resistance mutation, which could be helpful in guiding personalized therapeutic strategies.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.