Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma

作者全名："Huo, Ji-wen; Luo, Tian-you; Diao, Le; Lv, Fa-jin; Chen, Wei-dao; Yu, Rui-ze; Li, Qi"

作者地址："[Huo, Ji-wen; Luo, Tian-you; Lv, Fa-jin; Li, Qi] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China; [Diao, Le; Chen, Wei-dao; Yu, Rui-ze] Infervis Med Technol Co Ltd, Ocean Int Ctr, Beijing, Peoples R China"

通信作者："Li, Q (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China."

来源：FRONTIERS IN ONCOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000848398000001

JCR分区：Q2

影响因子：4.7

年份：2022

卷号：12

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：lung cancer; epidermal growth factor receptor; radiomics; computed tomography; machine learning

摘要："BackgroundTo investigate the value of computed tomography (CT)-based radiomics signatures in combination with clinical and CT morphological features to identify epidermal growth factor receptor (EGFR)-mutation subtypes in lung adenocarcinoma (LADC). MethodsFrom February 2012 to October 2019, 608 patients were confirmed with LADC and underwent chest CT scans. Among them, 307 (50.5%) patients had a positive EGFR-mutation and 301 (49.5%) had a negative EGFR-mutation. Of the EGFR-mutant patients, 114 (37.1%) had a 19del -mutation, 155 (50.5%) had a L858R-mutation, and 38 (12.4%) had other rare mutations. Three combined models were generated by incorporating radiomics signatures, clinical, and CT morphological features to predict EGFR-mutation status. Patients were randomly split into training and testing cohorts, 80% and 20%, respectively. Model 1 was used to predict positive and negative EGFR-mutation, model 2 was used to predict 19del and non-19del mutations, and model 3 was used to predict L858R and non-L858R mutations. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate their performance. ResultsFor the three models, model 1 had AUC values of 0.969 and 0.886 in the training and validation cohorts, respectively. Model 2 had AUC values of 0.999 and 0.847 in the training and validation cohorts, respectively. Model 3 had AUC values of 0.984 and 0.806 in the training and validation cohorts, respectively. ConclusionCombined models that incorporate radiomics signature, clinical, and CT morphological features may serve as an auxiliary tool to predict EGFR-mutation subtypes and contribute to individualized treatment for patients with LADC."

基金机构：Chongqing Health and Family Planning Commission Foundation; Chongqing Health Commission (Chongqing Talent Program-Innovation Leading Talent Research Project); Chongqing Science and Technology Bureau; [2022MSXM147]; [CQYC20210303348]; [cstc2022ycjh-bgzxm0230]

基金资助正文：Funding This study was supported by Chongqing Health and Family Planning Commission Foundation (2022MSXM147) of China and Chongqing Health Commission (Chongqing Talent Program-Innovation Leading Talent Research Project) (CQYC20210303348) and Chongqing Science and Technology Bureau (cstc2022ycjh-bgzxm0230).