Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Cancer Imaging. 2022 Nov 4;22(1):62. doi: 10.1186/s40644-022-00490-2.
Esophageal fistula is one of the most serious complications of chemotherapy or chemoradiotherapy (CRT) for advanced esophageal cancer. This study aimed to evaluate the performance of quantitative computed tomography (CT) analysis and to establish a practical imaging model for predicting esophageal fistula in esophageal cancer patients treated with chemotherapy or chemoradiotherapy.
This study retrospectively enrolled 204 esophageal cancer patients (54 patients with fistula, 150 patients without fistula) and all patients were allocated to the primary and validation cohorts according to the time of inclusion in a 1:1 ratio. Ulcer depth, tumor thickness and length, and minimum and maximum enhanced CT values of esophageal cancer were measured in pretreatment CT imaging. Logistic regression analysis was used to evaluate the associations of CT quantitative measurements with esophageal fistula. Receiver operating characteristic curve (ROC) analysis was also used.
Logistic regression analysis showed that independent predictors of esophageal fistula included tumor thickness [odds ratio (OR) = 1.167; p = 0.037], the ratio of ulcer depth to adjacent tumor thickness (OR = 164.947; p < 0.001), and the ratio of minimum to maximum enhanced CT value (OR = 0.006; p = 0.039) in the primary cohort at baseline CT imaging. These predictors were used to establish a predictive model for predicting esophageal fistula, with areas under the receiver operating characteristic curves (AUCs) of 0.946 and 0.841 in the primary and validation cohorts, respectively. The quantitative analysis combined with T stage for predicting esophageal fistula had AUCs of 0.953 and 0.917 in primary and validation cohorts, respectively.
Quantitative pretreatment CT analysis has excellent performance for predicting fistula formation in esophageal cancer patients who treated by chemotherapy or chemoradiotherapy.
食管瘘是晚期食管癌化疗或放化疗（CRT）最严重的并发症之一。本研究旨在评估定量 CT（CT）分析的性能，并建立一种实用的影像学模型，以预测接受化疗或放化疗的食管癌患者发生食管瘘的风险。
本研究回顾性纳入 204 例食管癌患者（瘘管 54 例，无瘘管 150 例），所有患者均根据纳入时间按 1:1 比例分为主队列和验证队列。在治疗前 CT 影像中测量食管癌的溃疡深度、肿瘤厚度和长度、最小和最大增强 CT 值。采用 logistic 回归分析评估 CT 定量测量与食管瘘的相关性。同时进行受试者工作特征曲线（ROC）分析。
logistic 回归分析显示，食管瘘的独立预测因素包括肿瘤厚度[比值比（OR）=1.167；p=0.037]、溃疡深度与相邻肿瘤厚度的比值（OR=164.947；p<0.001）和最小与最大增强 CT 值的比值（OR=0.006；p=0.039）。这些预测因素用于建立预测食管瘘的模型，在主队列和验证队列中的受试者工作特征曲线下面积（AUC）分别为 0.946 和 0.841。定量分析联合 T 分期预测食管瘘的 AUC 分别为 0.953 和 0.917。
定量 CT 分析对预测接受化疗或放化疗的食管癌患者瘘管形成具有良好的性能。
