局部进展期食管鳞状细胞癌新辅助化疗联合免疫治疗反应的基线CT Logistic回归预测模型研究  

Development of baseline CT logistic regression prediction model for neoadjuvant chemotherapy combined with immunotherapy response in locally advanced esophageal squamous cell carcinoma

作　　者：郭文文 谭邦国 许敏 皈燕[3] 马代远[3] 陈天武 GUO Wen-wen;TAN Bang-guo;XU Min;GUI Yan;MA Dai-yuan;CHEN Tian-wu(Department of Radiology,Affiliated Hospital of North Sichuan Medical College,Nanchong 637000;Department of Radiology,Panzhihua Central Hospital,Panzhihua 617067;Department of Oncology,Affiliated Hospital of North Sichuan Medical College,Nanchong 637000,Sichuan;Department of Radiology,the Second Affiliated Hospital of Chongqing Medical University,Chongqing 400010,China)

机构地区：[1]川北医学院附属医院放射科,四川南充637000 [2]攀枝花中心医院放射科,四川攀枝花617067 [3]川北医学院附属医院肿瘤科,四川南充637000 [4]重庆医科大学附属第二医院放射科,重庆400010

出　　处：《川北医学院学报》2024年第6期737-741,共5页Journal of North Sichuan Medical College

基　　金：国家自然科学基金(82271959)。

摘　　要：目的:建立并验证局部进展期食管鳞状细胞癌(ESCC)新辅助化疗联合免疫治疗前的增强CT对治疗反应性预测的Logistic回归模型。方法:回顾性分析148例局部进展期ESCC患者的CT影像资料及临床资料,患者均接受新辅助化疗联合免疫治疗,并按3∶1的比例分为训练集(n=114)和验证集(n=34)。单因素统计分析训练集治疗前相关指标与ESCC新辅助治疗后反应性的关系;多因素Logistic回归分析治疗反应性的独立预测因素,并构建Logistic预测模型。受试者工作特征(ROC)曲线分析模型的预测效能,并在验证集通过Kappa检验验证回归模型的预测效能。结果:单因素分析显示,训练集治疗前cT分期、淋巴结转移、原发肿瘤体积(GTV)及转移淋巴结体积(GVAMN)在有和无反应患者的差异有统计学意义(P<0.05)。多因素Logistic回归分析显示,cT分期、淋巴结转移和GTV是治疗反应性的独立预测因素(P<0.05)。ROC曲线分析显示,基于独立预测因素构建的Logistic回归模型在训练集中预测治疗反应性的曲线下面积为0.831,Kappa检验显示在验证集,预测模型效能表现良好(Kappa=0.641)。结论:基于治疗前cT分期、淋巴结转移和GTV建立的Logistic回归模型,对预测局部进展期ESCC新辅助化疗联合免疫治疗后的反应性有重要价值。Objective:To develop and validate a Logistic regression model for predicting treatment response using enhanced CT before neoadjuvant chemotherapy combined with immunotherapy for advanced esophageal squamous cell carcinoma(ESCC).Methods:Pretreatment and posttreatment CT imaging data and clinical data of 148 patients with advanced ESCC receiving neoadjuvant chemotherapy combined with immunotherapy were retrospectively collected.The collected cases were assigned into the training cohort(n=114)and the validation cohort(n=34)at a ratio of 3∶1.In the training cohort,univariate analyse were performed to explore the relationship between relevant pretreatment indicators and the response of esophageal squamous cell carcinoma,and the features with statistical difference were fed into a multivariate Logistic regression to determine the independent predictors.Subsequently,a Logistic regression model was constructed based on above independent predictors.Predictive performance of the model was evaluated by the receiver operating characteristic curve(ROC)and area under the ROC curve(AUC).In the validation cohort,the prediction efficiency of the model was verified by Kappa test.Results:In the training cohort,the univariate analysis showed statistically significant difference in cT stage,lymph node metastasis,gross tumor volume(GTV)and gross volume of all metastatic lymph nodes(GVAMN)between patients with and without response(P<0.05).Multivariate analyses showed that cT stage,lymph node metastasis,and GTV were independent predictors of the response(P<0.05).ROC showed that the AUC of the Logistic regression model based on independent predictors to predict the treatment response was 0.831.In the validation cohort,the predictive model also performed well(Kappa=0.641).Conclusion:The Logistic regression model developed based on the cT staging,lymph node metastasis,and GTV are of great value in predicting the response of advanced esophageal squamous cell carcinoma after neoadjuvant chemotherapy combined with immunotherapy.

关 键 词：食管鳞状细胞癌 局部进展期 新辅助化疗 免疫治疗 治疗反应性 电子计算机断层扫描 

分 类 号：R445.3[医药卫生—影像医学与核医学]