本研究旨在基于术前临床和影像资料建立一个用于预测胰十二指肠切除术（PD）后发生临床相关的胰瘘（CR-POPF）风险的预测图谱。对205名患者的数据进行了回顾性分析，并随机分为训练组（n = 125）和测试组（n = 80）。收集了患者术前实验室指标、术前临床基线数据和术前影像资料[增强计算机断层扫描（CT）、增强磁共振成像（MRI）]。使用单变量分析结合多变量 logistic 回归来确定 CR-POPF 的独立危险因素。利用这些因素来训练和验证模型，并构建风险预测图谱。采用曲线下面积（AUC）衡量模型的预测能力。采用综合差异改进指数（IDI）和决策曲线分析（DCA）来评估在文献中建立的其他五个模型与本图谱在临床可行性方面的比较。 经确定CT内脏脂肪面积（P = 0.014）、脂肪抑制 T1 合并 MRI 序列中的胰脾信号比值（P < 0.001）以及CT主胰管直径（P = 0.001）为独立的预后因素并用于构建模型。最终的预测图谱的AUC为0.903。IDI和DCA显示，在训练组和测试组中，该图谱优于其他五个CR-POPF模型。 本图谱的预测能力优于文献中描述的其他模型，可供临床医生根据患者发生CR-POPF的风险来优化围手术期计划。© 2023. Society of Surgical Oncology.

The aim of this study was to develop a nomogram to predict the risk of developing clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy (PD) using preoperative clinical and imaging data.The data of 205 patients were retrospectively analyzed, randomly divided into training (n = 125) and testing groups (n = 80). The patients' preoperative laboratory indicators, preoperative clinical baseline data, and preoperative imaging data [enhanced computed tomography (CT), enhanced magnetic resonance imaging (MRI)] were collected. Univariate analyses combined with multivariate logistic regression were used to identify the independent risk factors for CR-POPF. These factors were used to train and validate the model and to develop the risk nomogram. The area under the curve (AUC) was used to measure the predictive ability of the models. The integrated discrimination improvement index (IDI) and decision curve analysis (DCA) were used to assess the clinical feasibility of the nomogram in relation to five other models established in literature.CT visceral fat area (P = 0.014), the pancreatic spleen signal ratio on T1 fat-suppressed MRI sequences (P < 0.001), and CT main pancreatic duct diameter (P = 0.001) were identified as independent prognostic factors and used to develop the model. The final nomogram achieved an AUC of 0.903. The IDI and DCA showed that the nomogram outperformed the other five CR-POPF models in the training and testing cohorts.The nomogram achieved a superior predictive ability for CR-POPF following PD than other models described in literature. Clinicians can use this simple model to optimize perioperative planning according to the patient's risk of developing CR-POPF.© 2023. Society of Surgical Oncology.