Pasha Shamsher A, Khalid Abdullah, Levy Todd, Demyan Lyudmyla, Hartman Sarah, Newman Elliot, Weiss Matthew J, King Daniel A, Zanos Theodoros, Melis Marcovalerio
Department of Surgery, Northwell Health, North Shore/Long Island Jewish, Manhasset, New York, USA.
Department of Surgery, Northwell Health, Lenox Hill Hospital, New York City, New York, USA.
J Surg Oncol. 2024 Dec;130(8):1605-1610. doi: 10.1002/jso.27812. Epub 2024 Aug 19.
Chemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction undergoing neoadjuvant treatment. Our study aimed to predict which patients would complete pre- or postoperative chemotherapy through machine learning (ML).
Patients with resectable PC identified in our institutional pancreas database were grouped into two categories: those who completed all intended treatments (i.e., surgery plus either neoadjuvant or adjuvant chemotherapy), and those who did not. We applied logistic regression with lasso penalization and an extreme gradient boosting model for prediction, and further examined it through bootstrapping for sensitivity.
Among 208 patients, the median age was 69, with 49.5% female and 62% white participants. Most had an Eastern Cooperative Oncology Group (ECOG) performance status of ≤2. The PC predominantly affected the pancreatic head. Neoadjuvant and adjuvant chemotherapies were received by 26% and 47.1%, respectively, but only 49% completed all treatments. Incomplete therapy was correlated with older age and lower ECOG status. Negative prognostic factors included worsening diabetes, age, congestive heart failure, high body mass index, family history of PC, initial bilirubin levels, and tumor location in the pancreatic head. The models also flagged other factors, such as jaundice and specific cancer markers, impacting treatment completion. The predictive accuracy (area under the receiver operating characteristic curve) was 0.67 for both models, with performance expected to improve with larger datasets.
Our findings underscore the potential of ML to forecast PC treatment completion, highlighting the importance of specific preoperative factors. Increasing data volumes may enhance predictive accuracy, offering valuable insights for personalized patient strategies.
化疗可提高胰腺癌（PC）患者术后生存率，但完成辅助治疗的患者不到60%，接受新辅助治疗的比例更小。我们的研究旨在通过机器学习（ML）预测哪些患者会完成术前或术后化疗。
在我们机构的胰腺数据库中识别出的可切除PC患者被分为两类：完成所有预定治疗的患者（即手术加新辅助或辅助化疗）和未完成的患者。我们应用带套索惩罚的逻辑回归和极端梯度提升模型进行预测，并通过自举法进一步检验其敏感性。
208例患者中，中位年龄为69岁，女性占49.5%，白人参与者占62%。大多数患者东部肿瘤协作组（ECOG）体能状态≤2。PC主要影响胰头。分别有26%和47.1%的患者接受了新辅助和辅助化疗，但只有49%的患者完成了所有治疗。治疗未完成与年龄较大和ECOG状态较低相关。不良预后因素包括糖尿病恶化、年龄、充血性心力衰竭、高体重指数、PC家族史、初始胆红素水平以及胰头肿瘤位置。模型还标记了其他影响治疗完成的因素，如黄疸和特定癌症标志物。两个模型的预测准确性（受试者工作特征曲线下面积）均为0.67，随着数据集增大，性能有望提高。
我们的研究结果强调了ML预测PC治疗完成情况的潜力，突出了特定术前因素的重要性。增加数据量可能提高预测准确性，为个性化患者策略提供有价值的见解。
