Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study.

Journal: Cancer medicine
PMID: 40052528

Abstract

OBJECTIVE: Gallbladder polyps (GBPs) are increasingly prevalent, with the majority being benign; however, neoplastic polyps carry a risk of malignant transformation, highlighting the importance of accurate differentiation. This study aimed to develop and validate interpretable machine learning (ML) models to accurately predict neoplastic GBPs in a retrospective cohort, identifying key features and providing model explanations using the Shapley additive explanations (SHAP) method.

Authors

  • Zhaobin He
    Department of Hepatobiliary Surgery, General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong, P.R. China.
  • Shengbiao Yang
    Department of Hepatobiliary Surgery, General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong, P.R. China.
  • Jianqiang Cao
    Department of Hepatobiliary Surgery, General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong, P.R. China.
  • Huijie Gao
    Department of Hepatobiliary Surgery, General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong, P.R. China.
  • Cheng Peng
    School of Electrical and Mechanical Engineering, Hefei Technology College, Hefei, China.

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