Background. Postoperative pancreatic fistula (POPF) remains one of the most relevant complications following pancreaticoduodenectomy (PD), significantly impacting short-term outcomes and delaying adjuvant therapies. Current predictive models offer limited accuracy, often failing to incorporate early postoperative data. This retrospective study aimed to develop and validate machine learning (ML) models to predict the absence and severity of POPF using clinical, surgical, and early postoperative variables. Methods. Data from 216 patients undergoing PD were analyzed. A total of twenty-four machine learning (ML) algorithms were systematically evaluated using the Matthews Correlation Coefficient (MCC) and AUC-ROC metrics. Among these, the GradientBoostingClassifier consistently outperformed all other models, demonstrating the best predictive performance, particularly in identifying patients at low risk of postoperative pancreatic fistula (POPF) during the early postoperative period. To enhance transparency and interpretability, a SHAP (SHapley Additive exPlanations) analysis was applied, highlighting the key role of early postoperative biomarkers in the model predictions. Results. The performance of the GradientBoostingClassifier was also directly compared to that of a traditional logistic regression model, confirming the superior predictive performance over conventional approaches. This study demonstrates that ML can effectively stratify POPF risk, potentially supporting early drain removal and optimizing postoperative management. Conclusions. While the model showed promising performance in a single-center cohort, external validation across different surgical settings will be essential to confirm its generalizability and clinical utility. The integration of ML into clinical workflows may represent a step forward in delivering personalized and dynamic care after pancreatic surgery.
背景：术后胰瘘（POPF）是胰十二指肠切除术（PD）后最相关的并发症之一，显著影响短期预后并延迟辅助治疗。现有预测模型准确性有限，且往往未能纳入术后早期数据。本研究旨在利用临床、手术及术后早期变量，开发并验证机器学习（ML）模型以预测POPF的发生与否及其严重程度。 方法：对216例接受PD患者的资料进行分析。采用马修斯相关系数（MCC）和AUC-ROC指标系统评估了24种机器学习算法。其中，梯度提升分类器（GradientBoostingClassifier）在各项评估中均表现最优，尤其在术后早期识别低风险POPF患者方面展现出最佳预测性能。为增强模型透明度和可解释性，研究应用SHAP（沙普利加性解释）分析，揭示了术后早期生物标志物在模型预测中的关键作用。 结果：梯度提升分类器的性能与传统逻辑回归模型进行直接比较，证实其预测能力优于传统方法。研究表明，机器学习能有效对POPF风险进行分层，可能为早期引流管拔除和优化术后管理提供支持。 结论：尽管该模型在单中心队列中表现出良好性能，仍需在不同手术环境中进行外部验证以确认其普适性和临床实用性。将机器学习整合到临床工作流程中，可能为胰腺术后实现个性化、动态化诊疗迈出重要一步。
肿瘤(癌症)患者之家