Differentiation of Benign and Malignant Cervical Lymph Nodes Using a Multi‐Modal Ultrasound‐Based Machine Learning Model with <scp>SHAP</scp> Interpretability
The GBM model based on multi-modal ultrasound features enables accurate differentiation between benign and malignant cervical lymph nodes. SHAP provides a transparent, visual interpretation of model decisions, demonstrating a novel methodological framework that integrates multi-modal data with explainable AI, supporting its potential as a reliable tool for clinical decision-making.
Authors
- Kuo Miao (ORCID: https://orcid.org/0000-0001-6985-3253)
- Junhao Zhao (ORCID: https://orcid.org/0000-0002-3271-6455)
- Xiaoqiu Dong (ORCID: https://orcid.org/0000-0001-7930-2340)
- Mingru Gao
- Qian Lv
- Xiao Tian
- Yu Guo (ORCID: https://orcid.org/0000-0001-5135-2928)
- Haohao Hao
- Ning Zhao
Institutions
- Harbin Medical University (CN)
- Fourth Affiliated Hospital of Harbin Medical University (CN)
- Beijing University of Civil Engineering and Architecture (CN)
Publication Details
- Journal
- Journal of Ultrasound in Medicine
- Published
- 2026-03-05
- DOI
- https://doi.org/10.1002/jum.70223
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 0.00