Ultrasound‐Based Machine Learning and <scp>SHapley</scp> Additive <scp>exPlanations</scp> Method Evaluating Risk of Gallbladder Cancer
This study proposed a GBC ML model based on ultrasound, clinical, and serological characteristics, indicating the superior performance of the XGBoost model and enhancing the interpretability of the model through the SHAP method.
Authors
- Huohu Zhong (ORCID: https://orcid.org/0000-0001-7575-960X)
- Shanshan Su (ORCID: https://orcid.org/0000-0003-3850-3968)
- Jiaojiao Lin (ORCID: https://orcid.org/0000-0001-7049-6804)
- Guorong Lyu (ORCID: https://orcid.org/0000-0003-3123-1138)
- Bing Chen (ORCID: https://orcid.org/0000-0002-0026-1249)
Institutions
- Fujian Medical University (CN)
- Zhongshan Hospital of Xiamen University (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
Publication Details
- Journal
- Journal of Ultrasound in Medicine
- Published
- 2025-08-09
- DOI
- https://doi.org/10.1002/jum.70027
- Citations
- 3
- Primary Topic
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.02
Funders
- Science and Technology Bureau of Quanzhou
- Quanzhou City Science and Technology Program