Radiomics-based multiple machine learning approaches for investigating medial wall invasion of the cavernous sinus in pituitary adenomas
A machine learning model integrating clinical and radiomics features can effectively predict cavernous sinus dural invasion in pituitary adenomas preoperatively, providing a reliable basis for diagnosing tumor invasiveness and developing surgical plans. The LightGBM algorithm exhibited the highest predictive efficacy. Furthermore, the pedunculated satellite tumor feature emerged as a novel imaging marker for cavernous sinus dural invasion, offering new insights into the study of invasive pituitary adenomas.
Authors
- Xiaoyu Wang
- Tianshun Feng
- Yuyang Chen (ORCID: https://orcid.org/0009-0000-1937-9055)
- Pengwei Hou
- Zhi Li (ORCID: https://orcid.org/0000-0003-1927-9294)
- Shousen Wang (ORCID: https://orcid.org/0000-0002-4218-0068)
- Jun Li
- Jiansheng Zhong
- Liangfeng Wei (ORCID: https://orcid.org/0000-0003-2748-6306)
Institutions
- Fujian Medical University (CN)
- Fuzhou Pulmonary Hospital of Fujian (CN)
- Fujian Provincial People's Hospital (CN)
- Fujian Provincial Hospital (CN)
- Shanghai Sixth People's Hospital (CN)
- Huashan Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.3389/fonc.2025.1706895
- Primary Topic
- Pituitary Gland Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00