94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1510642
Enhanced Preoperative Prediction of Pancreatic Fistula Using Radiomics and Clinical Features with SHAP Visualization
Provisionally accepted- 1 First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- 2 Bishan hospital of Chongqing Medical University, Chongqing, China
Background: Clinically relevant postoperative pancreatic fistula (CR-POPF) represents a significant complication after pancreaticoduodenectomy(PD). Therefore, the early prediction of CR-POPF is of paramount importance. Based on above, this study sought to develop a CR-POPF prediction model that amalgamates radiomics and clinical features to predict CR-POPF, utilizing Shapley Additive explanations (SHAP) for visualization.Methods: Extensive radiomics features were extracted from preoperative enhanced Computed Tomography (CT) images of patients scheduled for PD. Subsequently, feature selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) regression and random forest (RF) algorithm to select pertinent radiomics and clinical features. Last, 15 CR-POPF prediction models were developed using five distinct machine learning (ML) predictors, based on selected radiomics features, selected clinical features, and a combination of both. Model performance was compared using DeLong's test for the area under the receiver operating characteristic curve (AUC) differences.The CR-POPF prediction model based on the XGBoost predictor with the combination of the radiomics and clinical features selected by Lasso regression and RF exhibited superior performance among these 15 CR-POPF prediction models, achieving an accuracy of 0.85, an AUC of 0.93. DeLong's test showed statistically significant differences (P < 0.05) when compared to the radiomics-only and clinical-only models, with recall of 0.63, precision of 0.65, and F1 score of 0.64.The proposed CR-POPF prediction model based on the XGBoost predictor with the combination of the radiomics and clinical features selected by Lasso regression and RF can effectively predicting the CR-POPF and may provide strong support for early clinical management of CR-POPF.
Keywords: computed tomography, Clinically relevant postoperative pancreatic fistula, machine learning, Radiomics, The SHapley Additive exPlanations
Received: 13 Oct 2024; Accepted: 21 Mar 2025.
Copyright: © 2025 Li, Zong, Zhou, Sun, Liu, Zhou and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yanyao Liu, First Affiliated Hospital of Chongqing Medical University, Chongqing, China
Baoyong Zhou, First Affiliated Hospital of Chongqing Medical University, Chongqing, China
Zhongjun Wu, First Affiliated Hospital of Chongqing Medical University, Chongqing, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.