A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery

作者：Jincan Huang, Shaocheng Lyu, Bing Pan, Han-Xuan Wang, You-Wei Ma, Tao Jiang, Qiang He, Ren Lang · 发表于：Cancer Imaging · 年份：2025 · DOI：10.1186/s40644-025-00830-y · 被引用次数：9 · 研究领域：Pancreatic and Hepatic Oncology Research、Renal cell carcinoma treatment、Cholangiocarcinoma and Gallbladder Cancer Studies

BACKGROUND: The machine learning model, which has been widely applied in prognosis assessment, can comprehensively evaluate patient status for accurate prognosis classification. There still has been a debate about which predictive strategy is better in patients with borderline resectable pancreatic cancer (BRPC). In the present study, we establish a logistic regression model, aiming to predict long-term survival and identify related prognostic factors in patients with BRPC who underwent upfront surgery. METHODS: Medical records of patients with BRPC who underwent upfront surgery with portal vein resection and reconstruction from Jan. 2011 to Dec. 2020 were reviewed. Based on postoperative overall survival (OS), patients were divided into the short-term group (≤ 2 years) and the long-term group (> 2 years). Univariate and multivariate analyses were performed to compare perioperative variables and long-term prognoses between groups to identify related independent prognostic factors. All patients are randomly divided into the training set and the validation set at a 7:3 ratio. The logistic regression model was established and evaluated for accuracy through the above variables in the training set and the validation set, respectively, and was visualized by Nomograms. Meanwhile, the model was further verified and compared for accuracy, the area under the curve (AUC) of the receiver operating characteristic curves (ROC), and calibration analysis. Then, we plotted and sorted perioper...