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September 17, 2025

Combining Intratumoral and Peritumoral Multimodal MRI to Predict the HER-2 Expression Level in Breast Cancer

Key Points

Combined multimodal MRI models significantly predict HER-2 expression in breast cancer patients, enhancing treatment planning.
The study observed an AUC of 0.822 for combined MRI models, indicating high diagnostic performance.
Retrospective analysis utilized multiple MRI techniques, including DCE-MRI and MUSE-IVIM, to assess HER-2 status.
The findings suggest that multimodal MRI can provide a non-invasive method for determining HER-2 status, beneficial for personalized treatments.

Abstract

Motivation: Understanding the prognostic factors in breast cancer is crucial for improving clinical diagnosis and treatment. Goal(s): This study aims to evaluate the predictive value of multimodal MRI parameters for determining human epidermal growth factor receptor 2 (HER-2) status in breast cancer. Approach: A retrospective analysis was conducted using multimodal MRI techniques, including DCE-MRI, MUSE-IVIM, IDEAL-IQ, and MAGiC, on patients diagnosed with breast cancer. Quantitative parameters were extracted and analyzed. Results: Significant differences in MRI parameters between HER-2 positive and negative groups were found, with combined models showing high diagnostic performance (AUC=0.822) for predicting HER-2 expression. Impact: This study's findings could enhance non-invasive HER-2 status assessment in breast cancer, aiding clinicians in personalized treatment planning. It opens avenues for further research on multimodal MRI's role in predicting other molecular characteristics, improving diagnostic precision.

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