Hepato Pancreato Biliary Division, Hospital Del Mar, Universitat Pompeu Fabra, Barcelona, Spain.
Department of Surgery, Rigshospitalet, University of Copenhagen, Denmark.
Eur J Surg Oncol. 2024 Jul;50(7):108375. doi: 10.1016/j.ejso.2024.108375. Epub 2024 May 9.
Distal Cholangiocarcinoma (dCCA) represents a challenge in hepatobiliary oncology, that requires nuanced post-resection prognostic modeling. Conventional staging criteria may oversimplify dCCA complexities, prompting the exploration of novel prognostic factors and methodologies, including machine learning algorithms. This study aims to develop a machine learning predictive model for recurrence after resected dCCA.
This retrospective multicentric observational study included patients with dCCA from 13 international centers who underwent curative pancreaticoduodenectomy (PD). A LASSO-regularized Cox regression model was used to feature selection, examine the path of the coefficient and create a model to predict recurrence. Internal and external validation and model performance were assessed using the C-index score. Additionally, a web application was developed to enhance the clinical use of the algorithm.
Among 654 patients, LNR (Lymph Node Ratio) 15, neural invasion, N stage, surgical radicality, and differentiation grade emerged as significant predictors of disease-free survival (DFS). The model showed the best discrimination capacity with a C-index value of 0.8 (CI 95 %, 0.77%-0.86 %) and highlighted LNR15 as the most influential factor. Internal and external validations showed the model's robustness and discriminative ability with an Area Under the Curve of 92.4 % (95 % CI, 88.2%-94.4 %) and 91.5 % (95 % CI, 88.4%-93.5 %), respectively. The predictive model is available at https://imim.shinyapps.io/LassoCholangioca/.
This study pioneers the integration of machine learning into prognostic modeling for dCCA, yielding a robust predictive model for DFS following PD. The tool can provide information to both patients and healthcare providers, enhancing tailored treatments and follow-up.
远端胆管癌（dCCA）是肝胆肿瘤学的一个挑战，需要细致的术后预后建模。传统的分期标准可能过于简化了 dCCA 的复杂性，促使人们探索新的预后因素和方法，包括机器学习算法。本研究旨在开发一种用于预测接受胰十二指肠切除术（PD）切除后的 dCCA 复发的机器学习预测模型。
这是一项回顾性多中心观察性研究，纳入了来自 13 个国际中心的接受根治性 PD 的 dCCA 患者。使用 LASSO-正则化 Cox 回归模型进行特征选择、检查系数路径并创建预测复发的模型。使用 C 指数评分评估内部和外部验证以及模型性能。此外，还开发了一个网络应用程序来增强算法的临床应用。
在 654 例患者中，淋巴结比率（LNR）15、神经侵犯、N 分期、手术根治性和分化程度是无病生存（DFS）的显著预测因素。该模型的 C 指数值为 0.8（95%CI，0.77%-0.86%），具有最佳的区分能力，突出了 LNR15 是最具影响力的因素。内部和外部验证表明，该模型具有稳健性和区分能力，曲线下面积分别为 92.4%（95%CI，88.2%-94.4%）和 91.5%（95%CI，88.4%-93.5%）。该预测模型可在 https://imim.shinyapps.io/LassoCholangioca/ 上获得。
本研究率先将机器学习纳入 dCCA 的预后建模中，为 PD 后 DFS 生成了一个稳健的预测模型。该工具可以为患者和医疗保健提供者提供信息，增强个性化治疗和随访。
