Cai Wei, Zhu Yongjian, Li Dengfeng, Hu Mancang, Teng Ze, Cong Rong, Chen Zhaowei, Sun Xujie, Ma Xiaohong, Zhao Xinming
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (W.C., Y.Z., D.L., M.H., Z.T., R.C., Z.C., X.M., X.Z.).
Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (X.S.).
Acad Radiol. 2025 Apr;32(4):2027-2040. doi: 10.1016/j.acra.2024.10.010. Epub 2024 Nov 12.
Clinically relevant postoperative pancreatic fistula (CR-POPF) is a threatening complication in body and/or tail pancreatic ductal adenocarcinoma (PDAC) receiving distal pancreatectomy (DP) and is difficult to predict preoperatively. We aimed to identify the role of baseline CT-based body composition analysis and extracellular volume (ECV) map in predicting the risk of CR-POPF preoperatively.
A total of 329 resectable PDAC patients were enrolled and underwent multiphasic contrast-enhanced CT. Body composition indicators were calculated, and ECV maps were generated through multiphasic CT images. The differences in clinical variables and quantitative parameters between CR-POPF and non-CR-POPF patients were compared. Correlations between ECV fraction and pancreatic fibrosis stage were analyzed. Multivariate logistic regression was performed to screen the independent predictors and develop prediction models for CR-POPF. Receiver operating characteristic curve was utilized to evaluate the predictive performance.
Among 329 patients, 19.76% (65/329) developed CR-POPF. Albumin, pancreatic texture, and intraoperative blood loss were used to build the clinical model with an AUC of 0.764. ECV fraction and total muscle ratio (TMR) were chosen to build the radiological model with an AUC of 0.872. A combined nomogram integrated with albumin, ECV fraction, and TMR could significantly improve the discrimination ability to an AUC of 0.924 (Delong test, all p < 0.05). The ECV fraction showed high positive correlation with histological fibrosis grade (Spearman ρ = 0.81).
CT-based body composition analysis and ECV exhibited great potential for predicting CR-POPF in body and/or tail PDAC after DP. The combined nomogram could further improve the predictive performance.
临床相关的术后胰瘘（CR-POPF）是接受胰体尾切除术（DP）的胰体尾导管腺癌（PDAC）患者面临的一种威胁性并发症，术前难以预测。我们旨在确定基于基线CT的身体成分分析和细胞外容积（ECV）图在术前预测CR-POPF风险中的作用。
共纳入329例可切除的PDAC患者，并进行多期增强CT检查。计算身体成分指标，并通过多期CT图像生成ECV图。比较CR-POPF患者与非CR-POPF患者临床变量和定量参数的差异。分析ECV分数与胰腺纤维化分期之间的相关性。进行多因素逻辑回归分析以筛选独立预测因素并建立CR-POPF的预测模型。利用受试者工作特征曲线评估预测性能。
329例患者中，19.76%（65/329）发生CR-POPF。白蛋白、胰腺质地和术中失血量用于构建临床模型，曲线下面积（AUC）为0.764。选择ECV分数和总肌肉比率（TMR）构建放射学模型，AUC为0.872。结合白蛋白、ECV分数和TMR的联合列线图可显著提高鉴别能力，AUC为0.924（德龙检验，所有p<0.05）。ECV分数与组织学纤维化分级呈高度正相关（斯皮尔曼ρ=0.81）。
基于CT的身体成分分析和ECV在预测DP术后胰体尾PDAC患者的CR-POPF方面具有巨大潜力。联合列线图可进一步提高预测性能。
