Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer  

作　　者：Jun Zhang Qi Wang Tian-Hui Guo Wen Gao Yi-Miao Yu Rui-Feng Wang Hua-Long Yu Jing-Jing Chen Ling-Ling Sun Bi-Yuan Zhang Hai-Ji Wang 

机构地区：[1]Department of Radiation Oncology,Affiliated Hospital of Qingdao University,Qingdao 266000,Shandong Province,China [2]Department of Radiology,Affiliated Hospital of Qingdao University,Qingdao 266000,Shandong Province,China [3]Department of Pathology,Affiliated Hospital of Qingdao University,Qingdao 266000,Shandong Province,China

出　　处：《World Journal of Gastrointestinal Oncology》2024年第10期4115-4128,共14页世界胃肠肿瘤学杂志（英文）

基　　金：Supported by the Affiliated Hospital of Qingdao University Horizontal Fund,No.3635;Intramural Project Fund,No.4618.

摘　　要：BACKGROUND Neoadjuvant immunochemotherapy(nICT)has emerged as a popular treatment approach for advanced gastric cancer(AGC)in clinical practice worldwide.However,the response of AGC patients to nICT displays significant heterogeneity,and no existing radiomic model utilizes baseline computed tomography to predict treatment outcomes.AIM To establish a radiomic model to predict the response of AGC patients to nICT.METHODS Patients with AGC who received nICT(n=60)were randomly assigned to a training cohort(n=42)or a test cohort(n=18).Various machine learning models were developed using selected radiomic features and clinical risk factors to predict the response of AGC patients to nICT.An individual radiomic nomogram was established based on the chosen radiomic signature and clinical signature.The performance of all the models was assessed through receiver operating characteristic curve analysis,decision curve analysis(DCA)and the Hosmer Lemeshow goodness-of-fit test.RESULTS The radiomic nomogram could accurately predict the response of AGC patients to nICT.In the test cohort,the area under curve was 0.893,with a 95%confidence interval of 0.803-0.991.DCA indicated that the clinical application of the radiomic nomogram yielded greater net benefit than alternative models.CONCLUSION A nomogram combining a radiomic signature and a clinical signature was designed to predict the efficacy of nICT in patients with AGC.This tool can assist clinicians in treatment-related decision-making.

关 键 词：Gastric cancer Radiomics Computed tomography Neoadjuvant immunochemotherapy Machine learning IMMUNOLOGY 

分 类 号：R735.9[医药卫生—肿瘤]