AUTHOR=Zhang Zhihui , Xu ShiCong , Song MeiXuan , Huang WeiRong , Yan ManLin , Li XianRong 

TITLE=Machine learning-based prediction model and web calculator for postoperative LDVT in colorectal cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1673705

DOI=10.3389/fonc.2025.1673705

ISSN=2234-943X

ABSTRACT=BackgroundLower limb deep vein thrombosis (LDVT) is a common but often underdiagnosed complication after colorectal cancer (CRC) surgery. Its early symptoms are subtle, and delayed detection can lead to post-thrombotic syndrome or even life-threatening events. However, effective tools for early risk assessment are lacking.ObjectiveTo identify risk factors for postoperative LDVT in CRC patients and develop a machine learning (ML)-based risk prediction model with an accessible web calculator.MethodsThis retrospective study included 1,200 CRC patients undergoing radical surgery. A modeling cohort of 1,000 patients (January 2021–December 2022) was randomly split 8:2 into training and testing sets, and 200 patients (March–August 2024) formed an external validation cohort. Risk factors were screened using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Eight ML models were constructed and compared based on area under the curve (AUC), accuracy, sensitivity, and F1-score. The best-performing model was interpreted using SHapley Additive exPlanations (SHAP), and a web-based calculator was developed.ResultsAmong 1,200 patients, 369 (30.75%) developed LDVT (31.5% in the modeling cohort, 27% in the validation cohort). Seventeen variables were associated with LDVT in univariate and LASSO analyses, and the top 10 were used to build models. The random forest (RF) model showed the best performance, with AUCs of 0.942, 0.897, and 0.891 in the training, testing, and validation sets, respectively, demonstrating high accuracy and generalizability. SHAP analysis identified D-dimer, preoperative intestinal obstruction, Caprini score, age, intraoperative blood loss, and diabetes as major predictors, with D-dimer having the strongest impact. A web-based calculator (https://crc-ldvt.shinyapps.io/RF-model/) was constructed to provide individualized risk estimation.ConclusionThis study developed and validated a robust ML-based model for predicting postoperative LDVT in CRC patients. The RF model, incorporating key clinical predictors, demonstrated high predictive performance and clinical relevance. The online calculator enables rapid, individualized risk assessment and may help guide early prevention strategies, reducing postoperative complications and improving patient outcomes.