Zhang Jun, Wang Qi, Guo Tian-Hui, Gao Wen, Yu Yi-Miao, Wang Rui-Feng, Yu Hua-Long, Chen Jing-Jing, Sun Ling-Ling, Zhang Bi-Yuan, Wang Hai-Ji
Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China.
Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China.
World J Gastrointest Oncol. 2024 Oct 15;16(10):4115-4128. doi: 10.4251/wjgo.v16.i10.4115.
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.
To establish a radiomic model to predict the response of AGC patients to nICT.
Patients with AGC who received nICT ( = 60) were randomly assigned to a training cohort ( = 42) or a test cohort ( = 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.
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.
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.
新辅助免疫化疗（nICT）已成为全球临床实践中晚期胃癌（AGC）的一种常用治疗方法。然而，AGC患者对nICT的反应存在显著异质性，且现有的放射组学模型均未利用基线计算机断层扫描来预测治疗结果。
建立一种放射组学模型以预测AGC患者对nICT的反应。
将接受nICT的AGC患者（n = 60）随机分为训练队列（n = 42）或测试队列（n = 18）。使用选定的放射组学特征和临床危险因素开发各种机器学习模型，以预测AGC患者对nICT的反应。基于所选的放射组学特征和临床特征建立个体放射组学列线图。通过受试者工作特征曲线分析、决策曲线分析（DCA）和Hosmer-Lemeshow拟合优度检验评估所有模型的性能。
放射组学列线图能够准确预测AGC患者对nICT的反应。在测试队列中，曲线下面积为0.893，95%置信区间为0.803 - 0.991。DCA表明，放射组学列线图的临床应用比其他模型产生更大的净效益。
设计了一种结合放射组学特征和临床特征的列线图，以预测AGC患者nICT的疗效。该工具可协助临床医生进行治疗相关决策。
