Predicting anastomotic leak in patients with esophageal squamous cell cancer treated with neoadjuvant chemoradiotherapy using a nomogram based on CT radiomic and clinicopathologic factors

Author:

Zhao Junfeng1,Yang Guanli2,Li Ying1,Li Shanshan3,Luo Haining4,Han Dan1,Li Baosheng1,Cao Qiang1

Affiliation:

1. Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences

2. Shandong Second Provincial General Hospital

3. Yantai Affiliated Hospital of Binzhou Medical University

4. Xiaogan Central Hospital

Abstract

Abstract Background Anastomotic leak (AL) is a common complication in patients with operable esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (NCRT) and radical esophagectomy. Therefore, this study aimed to establish and validate a nomogram to predict the occurrence of AL. Methods Between March 2016 and December 2022, 231 eligible patients with ESCC who underwent NCRT and radical esophagectomy were divided into training (n = 159) and validation cohorts (n = 72). Clinicopathologic and radiomics characteristics were included in the univariate logistic regression analysis, and statistically significant factors were enrolled to develop the nomogram, which was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results Univariate and multivariate analyses revealed that dose at the anastomosis ≥ 24 Gy, gross tumor volume ≥ 60 cm3, postoperative albumin < 35 g/L, comorbidities, duration of surgery ≥ 270 mins, and computed tomography-based radiomics characteristics were independent predictors of AL. The nomogram AUC in the training and validation cohorts was 0.845 (95% confidence interval [CI]: 0.770–0.920) and 0.839 (95% CI: 0.718–0.960), respectively, indicating good discriminatory ability. The calibration curves showed good agreement between the predicted and actual AL occurrence and the DCA demonstrated favorable clinical outcomes. Conclusions We developed and validated a nomogram based on radiomics and clinicopathologic characteristics. This predictive model could be a powerful tool to predict AL occurrence in patients with ESCC treated with NCRT.

Publisher

Research Square Platform LLC

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