MRI subregion Radiomics for Predicting Recurrence Risk in ER+/HER2- Breast Cancer
Motivation: The application of 21-gene assays in clinical practice is jeopardized by the cost and availability. Goal(s): To identify MRI subregion radiomics signatures associated with the Oncotype Dx 21-gene recurrence score (RS). Approach: 154 patients were enrolled. Radiomics features were extracted from intratumoral subregions on T2WI and the last phase of post-enhancement (CL) images, which were combined with clininal-imaging features to construct models for distinguishing high (RS ≥ 26) from low (RS < 26) recurrence risk. Results: The model that combined T2 and CL subregional radiomics features with clinical-imaging characteristics had the highest efficacy, with an AUC of 0.81 for the validation cohort. Impact: This study first identified radiomics signatures using intratumoral subregional features to predict RS accurately and cost-effectively in ER+/HER2- breast cancer.
Authors
- Yang Chen (ORCID: https://orcid.org/0000-0002-5660-6349)
- Jie Shi (ORCID: https://orcid.org/0000-0003-2744-8334)
- Weijun Peng
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/1552
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00