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Ultrasound-Based Machine Learning and SHapley Additive exPlanations Method Evaluating Risk of Gallbladder Cancer: A

Binqiong Chen¹, Huohu Zhong¹, Jiaojiao Lin²

¹Department of Ultrasound, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.

Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine

|August 9, 2025

Summary

This summary is machine-generated.

This study developed machine learning models to predict gallbladder cancer (GBC) risk using ultrasound, clinical, and serological data. The XGBoost model showed superior performance, with SHAP analysis improving interpretability.

Keywords:

XGBoost bicentric study gallbladder cancer machine learning ultrasound

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Area of Science:

Oncology
Medical Imaging
Machine Learning

Background:

Gallbladder cancer (GBC) poses a significant health challenge.
Accurate risk assessment is crucial for early detection and intervention.
Integrating diverse data sources can improve predictive accuracy.

Purpose of the Study:

To construct and evaluate machine learning models for GBC risk prediction.
To integrate ultrasound imaging, clinical, and serological features.
To assess the performance and interpretability of various predictive models.

Main Methods:

Retrospective analysis of data from 369 suspected GBC patients.
Feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression.
Development of 8 machine learning models including XGBoost, with SHapley Additive exPlanations (SHAP) for interpretability.

Main Results:

LASSO identified key predictors: gender, age, ALP, liver interface clarity, gallbladder wall stratification, and lesion characteristics.
The XGBoost model achieved high AUC values (0.934 training, 0.916 validation, 0.813 test).
SHAP analysis highlighted the importance of imaging features and ALP in GBC prediction, enhancing model transparency.

Conclusions:

An XGBoost-based machine learning model effectively predicts GBC risk.
Integration of ultrasound, clinical, and serological data improves prediction.
SHAP analysis provides crucial interpretability for the developed GBC risk model.