CT-based Radiomic Analysis for Prediction of Treatment Response of Salvage Chemoradiotherapy for Loco-regional Lymph Node Recurrence After Curative Esophagectomy

Abstract

Objective: To investigate the capability of computed tomography (CT) radiomic features to predict the therapeutic response and local control of the loco-regional recurrence lymph node (LN) after curative esophagectomy by chemoradiotherapy (CRT).Methods: This retrospective study included 129 LN from 77 patients (training cohort: 102 LN from 59 patients; validation cohort: 27 LN from 18 patients) with postoperative esophageal squamous cell carcinoma (ESCC). The region of the tumor was contoured in pretreatment contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) with logistic regression was used to identify radiomic predictors in the training cohort. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC). The Kaplan-Meier method was used to determine the local recurrence time of cancer.Results: Seven features were selected to construct a radiomics model for predicting therapeutic response. The AUCs in the training and validated cohorts were 0.777 (95%CI: 0.667–0.878) and 0.765(95%CI: 0.556–0.975), respectively. A significant difference of radiomic score (Rad-score) between the response and non-response was observed in the two cohorts (P < 0.001, 0.034, respectively). Two features were identified for classifying whether to relapse in two years. AUC was 0.857(95%CI: 0.780–0.935) in the training cohort. The local control time of the high Rad-score group was higher than the low group in both cohorts (P < 0.001 and 0.025, respectively). After the Cox regression analysis, the Rad-score indicated high-risk factors for local recurrence within two years.Conclusions: The radiomics approach can be used as a potential imaging biomarker to predict treatment response and local control of recurrence LN in ESCC patients.