基于内镜超声的影像组学列线图术前预测早期食管鳞状细胞癌的多中心研究  

Endoscopic ultrasound-based radiomics nomogram for preoperative predicting patients with early esophageal squamous cell carcinoma:a multi-center study

作　　者：陈雅静 孙舒涵 缪殊妹 何晓燕 周晓颖[4] 俞飞虹 Chen Yajing;Sun Shuhan;Miao Shumei;He Xiaoyan;Zhou Xiaoying;Yu Feihong(Department of Ultrasound,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China;Department of Information,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China;Department of Gastroenterology,Dongyang People's Hospital,Jinhua 322100,China;Department of Gastroenterology,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China)

机构地区：[1]南京医科大学第一附属医院超声科,南京210029 [2]南京医科大学第一附属医院信息处,南京210029 [3]浙江省东阳市人民医院消化内科,金华322100 [4]南京医科大学第一附属医院消化内科,南京210029

出　　处：《中华超声影像学杂志》2025年第1期56-64,共9页Chinese Journal of Ultrasonography

基　　金：南京市博士后科研资助计划(291937);南京医科大学第一附属医院国自然科学基金青年基金培育计划(PY2021043);南京医科大学第一附属医院临床能力提升项目(QN023);江苏省科学技术厅基础研究计划自然科学基金一一青年基金项目(BK20241122)。

摘　　要：目的评估基于内镜超声(EUS)的影像组学特征联合临床因素构建的列线图模型对鉴别早期食管鳞状细胞癌(ESCC)和非癌病变的预测价值。方法回顾性收集2020年1月至2023年11月在南京医科大学第一附属医院(训练集,323例)和东阳市人民医院(外部验证集,131例)进行EUS检查且内镜检查怀疑食管恶性病变的454例患者的临床超声影像信息。基于训练集数据,使用单因素和多因素Logistic回归分析筛选出早期ESCC的临床独立预测因素,建立临床模型。采用Pearson相关性分析去冗余和最小绝对收缩和选择算子(LASSO)算法建立影像组学模型。基于影像组学评分及临床独立预测因素构建联合模型并绘制列线图。采用ROC曲线下面积(AUC)评估各模型的预测性能并利用校准曲线评价模型的拟合能力。结果训练集和验证集均显示早期ESCC组与非癌病变组在年龄、吸烟史和病变位置三方面差异有统计学意义(均P<0.05)。根据单因素和多因素Logistic回归分析将年龄(OR=1.039,95%CI=1.003~1.077,P=0.036)和吸烟(OR=2.358,95%CI=1.270~4.376,P=0.007)作为早期ESCC的独立预测因素建立临床模型,训练集和验证集的AUC分别为0.608、0.694。最终筛选出14个最优影像组学特征构建影像组学模型,训练集和验证集的AUC分别为0.881、0.807。列线图联合模型表现出更高的预测性能,AUC、敏感度、特异度在训练集分别为0.893、82.5%、82.2%,在验证集分别为0.830、79.1%、81.3%。结论基于EUS的列线图联合模型预测性能最佳,可以作为无创辅助手段协助区分早期ESCC和非癌病变。ObjectiveTo assess the predictive performance of a nomogram model integrating endoscopic ultrasound(EUS)radiomic features with clinical variables for distinguishing early esophageal squamous cell carcinoma(ESCC)from non-cancerous lesions.MethodsClinical and imaging data from 454 patients who underwent EUS for suspected esophageal malignancies were retrospectively collected in the First Affiliated Hospital of Nanjing Medical University(training cohort,n=323)and Dongyang People's Hospital(external validation cohort,n=131)from January 2020 to November 2023.Independent clinical predictors of early ESCC were identified using univariable and multivariable Logistic regression analyses to establish a clinical model.Pearson correlation and Least Absolute Shrinkage and Selection Operator(LASSO)algorithms were used to construct a radiomics model.A combined model integrating radiomics scores and clinical predictors was developed and visualized as a nomogram.The predictive performance of each model was assessed using the area under the ROC curve(AUC),and calibration curves were used to evaluate the model's fitting capability.ResultsThe training set and validation set indicated that there were statistically significant differences in age,smoking history and lesion location between the early ESCC group and the non-cancerous lesion change group(all P<0.05).According to univariate and multivariate Logistic regression analysis,age(OR=1.039,95%CI=1.003–1.077,P=0.036)and smoking(OR=2.358,95%CI=1.270-4.376,P=0.007)were identified as independent predictors and used to develop the clinical model,with AUCs of 0.608 and 0.694 in the training and validation cohorts,respectively.Fourteen optimal radiomic features were selected to construct the radiomics model,with AUCs of 0.881 and 0.807 in the training and validation cohorts,respectively.The combined nomogram model demonstrated superior predictive performance with AUCs of 0.893 and 0.830,sensitivities of 82.5%and 79.1%,and specificities of 82.2%and 81.3%in the training and validation co

关 键 词：内镜超声 影像组学 食管鳞状细胞癌 列线图 

分 类 号：R445.1[医药卫生—影像医学与核医学] R735.1[医药卫生—诊断学]