Machine learning-based differentiation of benign and malignant adrenal lesions using 18F-FDG PET/CT: a two-stage classification and SHAP interpretation study
Machine learning models based on PET/CT-derived features enable highly accurate and interpretable classification of adrenal lesions. Integrating metabolic and anatomical parameters improves diagnostic precision, while SHAP analysis offers clinical transparency, supporting personalized decision-making in adrenal lesion management.
Authors
- Jaesik Jeong
- Heqing Yi (ORCID: https://orcid.org/0000-0002-5164-4821)
- Youwu Su (ORCID: https://orcid.org/0000-0001-9146-4780)
- Changyu Sun (ORCID: https://orcid.org/0000-0001-8102-7130)
- Dong Xie (ORCID: https://orcid.org/0000-0002-1150-8657)
- Zhuolin Liu (ORCID: https://orcid.org/0000-0001-8019-2054)
- Yukun Cai
- Ye Yuan (ORCID: https://orcid.org/0000-0002-8339-0504)
- Jing Li (ORCID: https://orcid.org/0000-0001-8692-4981)
- Jingjing Zhang (ORCID: https://orcid.org/0000-0002-7953-1159)
- Yun Wang (ORCID: https://orcid.org/0000-0002-3644-3466)
Institutions
- Chonnam National University (KR)
- Wenzhou Medical University (CN)
- Zhejiang Cancer Hospital (CN)
- Amicable Knowledge Solution University (IN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-11-07
- DOI
- https://doi.org/10.1186/s12885-025-15243-0
- Citations
- 1
- Primary Topic
- Adrenal and Paraganglionic Tumors
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- National Research Foundation
- National Natural Science Foundation of China
- National Research Foundation of Korea
- Ministry of Education, India