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.

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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

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article

Habitat radiomics based on CT for assessing BRCA mutation status in patients with high-grade serous ovarian cancer: a multicenter study

Feng Wang, Yuwei Zou, Jinwen Jiao, Huayuan Yang et al.
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
article

Habitat radiomics based on CT for assessing BRCA mutation status in patients with high-grade serous ovarian cancer: a multicenter study

Feng Wang, Yuwei Zou, Jinwen Jiao, Huayuan Yang, Shuai Zhang, Chengjian Wang, Haixia Wang, Xinping Yu
article en

Abstract

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.

Frontiers in OncologyVol. 16
Qingdao University (CN), Qingzhou City People's Hospital (CN), Affiliated Hospital of Qingdao University (CN)
Qingdao University
Openalex Percentile: Top 52%
Radiomics and Machine Learning in Medical Imaging
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