Objective: To build a Bayesian approach-based model to predict the success of surgical exploration post-neoadjuvant treatment. Background: Pancreatic cancer (PDAC) is best treated with radical surgery and chemotherapy, offering the greatest chance of survival. Surgery after neoadjuvant treatment (NAT) is indicated in the absence of progression, knowing the limits in accurately predicting resectability with traditional radiology. R Status being a pathological parameter, it can be assessed only after surgery. Method: Patients successfully resected for histologically confirmed PDAC after NAT for BR and LA disease were included, with attention to the predictors of R status from the existing literature. The Bayesian logistic regression model was estimated for predicting the R1 status. The area under curve (AUC) of the average posterior probability of R1 was calculated and results were reported considering the 95% posterior credible intervals for the odds ratios, along with the probability of direction. Results: The final model demonstrated a commendable AUC value of 0.72, indicating good performance. The likelihood of positive margins was associated with older age, higher ASA score, the presence of venous and/or arterial involvement at preoperative radiology, tumor location within the pancreatic body, a lack of tumor size reduction post-NAT, and the persistence of an elevated Ca19.9 value. Conclusions: A Bayesian approach using only preoperative items is firstly used with good performance to predict R Status in pancreatic cancer patients who underwent resection after neoadjuvant therapy.
目的：构建基于贝叶斯方法的模型，用于预测新辅助治疗后手术探查的成功率。背景：胰腺导管腺癌（PDAC）的最佳治疗方案为根治性手术联合化疗，此方案可提供最高的生存机会。新辅助治疗（NAT）后若病情无进展，则建议进行手术，但需认识到传统影像学方法在准确预测可切除性方面存在局限。由于R状态属于病理学参数，仅能在术后进行评估。方法：本研究纳入了因BR期和LA期PDAC接受NAT后成功切除并经组织学确诊的患者，重点关注现有文献中关于R状态的预测因子。采用贝叶斯逻辑回归模型预测R1状态，计算R1平均后验概率的曲线下面积（AUC），并报告比值比的95%后验可信区间及方向概率结果。结果：最终模型表现出色，AUC值达0.72，表明其具有良好的预测性能。切缘阳性的可能性与年龄较大、ASA评分较高、术前影像学显示静脉和/或动脉受累、肿瘤位于胰体部、NAT后肿瘤体积未缩小以及Ca19.9值持续升高相关。结论：本研究首次采用仅包含术前指标的贝叶斯方法，成功预测了新辅助治疗后接受切除术的胰腺癌患者的R状态，模型表现出良好的预测性能。
Bayesan Model to Predict R Status After Neoadjuvant Therapy in Pancreatic Cancer
肿瘤(癌症)患者之家