Quantitative CT analysis to predict esophageal fistula in patients with advanced esophageal cancer treated by chemotherapy or chemoradiotherapy

Abstract

Background 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. Methods 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. Results 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. Conclusion Quantitative pretreatment CT analysis has excellent performance for predicting fistula formation in esophageal cancer patients who treated by chemotherapy or chemoradiotherapy.

Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy

“…Regarding the model performance, the highest testing AUC (average value) is 0.817, which is lower than previous studies (Li et al 2023 ; Shi et al 2022 ; Gui et al 2022 ). This may be due to two reasons.…”

Multi-omics and Multi-VOIs to Predict Esophageal Fistula in Esophageal Cancer Patients Treated with Radiotherapy

“…Regarding the model performance, the highest testing AUC (average value) is 0.817, which is lower than previous studies [22,40,41]. This may be due to two reasons.…”

“…The 11 ltered images included three Laplacian-of-Gaussian lters (sigma = 1, 3, 6 mm) and eight wavelet lters with 63 combinations of high and low-pass ltering along the three axes. Before feature extraction, a xed-bin-number gray-level discretization was applied to the original and ltered images with varying bin numbers (10,20,30,40,50).…”

Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram

“…However, there is currently a scarcity of effective predictive methods. Clinical prediction models and quantitative-CT-based prediction models with AUC of 0.805 and 0.841, respectively, have been reported in previous research [ 11 , 12 ]. In present study, we built the radiomics-clinical model, clinical model, and radiomics model all at the same time and compared their predictive abilities.…”

“…In a retrospective study, ECOG PS, BMI, T 4 , N 2/3 and re-radiotherapy were identified as independent factors, with a C-index of a nomogram incorporating the factors of 0.764 in an external validation cohort [ 11 ]. Another retrospective study revealed quantitative pretreatment CT analysis has excellent performance for predicting fistula formation in esophageal cancer patients [ 12 ].…”