Purpose: Pancreatic cancer is known for its poor prognosis. The most effective treatment combines surgery with peri-operative chemotherapy. Current prognostic tools are designed to predict patient outcomes and inform treatment decisions based on collected data. Bayesian networks (BNs) can integrate objective data with subjective clinical insights, such as expert opinions, or they can be independently based on either element. This pilot study is one of the first efforts to incorporate expert opinions into a prognostic model using a Bayesian framework.Methods: A clinical hybrid BN was selected to model the long-term overall survival of pancreatic cancer patients. The SHELF expert judgment method was employed to enhance the BN’s effectiveness. This approach involved a two-phase protocol: an initial single-center pilot phase followed by a definitive international phase.Results: Experts generally agreed on the distribution shape among the 12 clinically relevant predictive variables identified for the BN. However, discrepancies were noted in the tumor size, age, and ASA score nodes. With regard to expert concordance for each node, tumor size, and ASA score exhibited absolute concordance, indicating a strong consensus among experts. Ca19.9 values and resectability status showed high concordance, reflecting a solid agreement among the experts. The remaining nodes showed acceptable concordance.Conclusions:This project introduces a novel clinical hybrid Bayesian network (BN) that incorporates expert elicitation and clinical variables present at diagnosis to model the survival of pancreatic cancer patients. This model aims to provide research-based evidence for more reliable prognosis predictions and improved decision-making, addressing the limitations of existing survival prediction models. A validation process will be essential to evaluate the model’s performance and clinical applicability.
目的：胰腺癌以其不良预后而著称，最有效的治疗方式为手术联合围手术期化疗。现有的预后工具旨在基于收集的数据预测患者结局并指导治疗决策。贝叶斯网络（BNs）能够整合客观数据与主观临床见解（如专家意见），或可独立基于其中任一要素构建。本项初步研究是首次尝试将专家意见纳入贝叶斯框架预后模型的探索之一。 方法：本研究选用临床混合贝叶斯网络对胰腺癌患者的长期总生存期进行建模，并采用SHELF专家判断法以提升网络效能。该方法采用两阶段实施方案：先进行单中心初步研究阶段，随后开展国际性正式研究阶段。 结果：专家们对贝叶斯网络中确定的12个临床相关预测变量的分布形态基本达成共识，但在肿瘤大小、年龄及ASA评分节点上存在差异。就各节点的专家一致性而言，肿瘤大小和ASA评分节点表现出绝对一致性，表明专家间共识度极高；CA19-9数值与可切除状态显示高度一致性，反映专家间达成坚实共识；其余节点则呈现可接受的一致性水平。 结论：本项目提出了一种新型临床混合贝叶斯网络，该网络整合了专家意见与诊断时存在的临床变量，用于模拟胰腺癌患者的生存状况。该模型旨在为更可靠的预后预测和改进临床决策提供研究依据，以弥补现有生存预测模型的不足。后续验证流程对于评估模型性能及临床适用性至关重要。
Expert Judgment Supporting a Bayesian Network to Model the Survival of Pancreatic Cancer Patients
肿瘤(癌症)患者之家