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

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Tác giả: Qiang Cao, Dan Han, Baosheng Li, Shanshan Li, Ying Li, Haining Luo, Guanli Yang, Junfeng Zhao

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: England : BMC cancer , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 714476

 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, ESCC patients undergoing NCRT and radical esophagectomy were retrospectively collected in China. 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: 231 eligible patients were divided into training (n = 159) and validation cohorts (n = 72). 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 min, 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.
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