背景:恶性食管瘘(MEF),发生在5%至15%的食管癌(EC)患者中,预后不良。准确识别MEF高风险的食管癌患者具有挑战性。本研究的目的是建立并验证预测EC患者食管瘘发生的模型。
方法:本研究回顾性纳入了122例接受化疗或放化疗的食管癌患者(53例瘘管,69无),所有患者被随机分配到训练组(n=86)和验证组(n=36).从治疗前CT中提取影像组学特征,通过logistic回归分析确定临床预测因子.Lasso回归模型用于特征选择,和影像组学签名大楼。多变量logistic回归分析用于建立临床列线图,影像组学-临床列线图和影像组学预测模型。通过区别对待,对模型进行了验证和比较,校准,重新分类,和临床效益。
结果:由十个选定特征组成的放射学标记,与食管瘘显著相关(P=0.001)。影像组学临床列线图由两个预测因子创建,包括影像组学特征和狭窄,通过逻辑回归分析确定。模型显示出良好的区分度,训练集中的AUC=0.782(95%CI0.684-0.8796),验证集中的AUC=0.867(95%CI0.7461-0.987),AIC=101.1,校准良好。与临床预测模型相比,影像组学临床列线图将NRI提高了0.236(95%CI0.153,0.614),将IDI提高了0.125(95%CI0.040,0.210),P=0.004。
结论:我们开发并验证了第一个恶性食管瘘的影像组学临床列线图,这可以帮助临床医生识别MEF高危患者。
BACKGROUND: Malignant esophageal fistula (MEF), which occurs in 5% to 15% of esophageal cancer (EC) patients, has a poor prognosis. Accurate identification of esophageal cancer patients at high risk of MEF is challenging. The goal of this study was to build and validate a model to predict the occurrence of esophageal fistula in EC patients.
METHODS: This study retrospectively enrolled 122 esophageal cancer patients treated by chemotherapy or chemoradiotherapy (53 with fistula, 69 without), and all patients were randomly assigned to a training (n = 86) and a validation (n = 36) cohort. Radiomic features were extracted from pre-treatment CTs, clinically predictors were identified by logistic regression analysis. Lasso regression model was used for feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the clinical nomogram, radiomics-clinical nomogram and radiomics prediction model. The models were validated and compared by discrimination, calibration, reclassification, and clinical benefit.
RESULTS: The radiomic signature consisting of ten selected features, was significantly associated with esophageal fistula (P = 0.001). Radiomics-clinical nomogram was created by two predictors including radiomics signature and stenosis, which was identified by logistic regression analysis. The model showed good discrimination with an AUC = 0.782 (95% CI 0.684-0.8796) in the training set and 0.867 (95% CI 0.7461-0.987) in the validation set, with an AIC = 101.1, and good calibration. When compared to the clinical prediction model, the radiomics-clinical nomogram improved NRI by 0.236 (95% CI 0.153, 0.614) and IDI by 0.125 (95% CI 0.040, 0.210), P = 0.004.
CONCLUSIONS: We developed and validated the first radiomics-clinical nomogram for malignant esophageal fistula, which could assist clinicians in identifying patients at high risk of MEF.