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

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
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article

MRI subregion Radiomics for Predicting Recurrence Risk in ER+/HER2- Breast Cancer

Yang Chen, Jie Shi, Weijun Peng
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
Radiomics and Machine Learning in Medical Imaging
article

MRI subregion Radiomics for Predicting Recurrence Risk in ER+/HER2- Breast Cancer

Yang Chen, Jie Shi, Weijun Peng
article en

Abstract

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.

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
Openalex Percentile: Top 65%
Radiomics and Machine Learning in Medical Imaging
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