Habitat radiomics based on CT for assessing BRCA mutation status in patients with high-grade serous ovarian cancer: a multicenter study
CT-based habitat radiomics offers a promising, non-invasive method for predicting BRCA mutation status in HGSOC. The combined habitat model outperformed traditional clinical and whole-tumor radiomic models by more effectively capturing tumor heterogeneity. SVM, demonstrating stable and reliable performance across datasets, emerged as the most robust model for clinical use. These findings support the integration of habitat radiomics, particularly SVM, for personalized, non-invasive molecular assessment in clinical practice.
Authors
- Feng Wang (ORCID: https://orcid.org/0000-0002-1528-9711)
- Yuwei Zou
- Jinwen Jiao
- Huayuan Yang
- Shuai Zhang
- Chengjian Wang
- Haixia Wang
- Xinping Yu
Institutions
- Qingdao University (CN)
- Qingzhou City People's Hospital (CN)
- Affiliated Hospital of Qingdao University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2026-02-18
- DOI
- https://doi.org/10.3389/fonc.2026.1784439
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Qingdao University