MRI-based assessment for optimal breast cancer local therapy in the era of BI-RADS v2025.

Authors

Maya Honda, Masako Kataoka, Mami Iima, Azusa Nishimoto, Yuji Nakamoto

Published in

Japanese journal of radiology. Jun 15, 2026. Epub Jun 15, 2026.

Abstract

Preoperative breast magnetic resonance imaging (MRI) is the most sensitive modality for assessing disease extent in newly diagnosed breast cancer, detecting additional ipsilateral disease in approximately 20% of patients. While its impact on patients' prognosis is debated, MRI may refine surgical management when it is appropriately evaluated. This review provides a practical framework for optimizing MRI interpretation for individualized surgical planning, incorporating the updated American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) v2025 recommendations. Key updates in BI-RADS v2025 include the introduction of "Additional Close Findings" to streamline the management of suspicious lesions within 2.0 cm of the index cancer. For oncoplastic procedures, MRI provides critical metrics: a tumor-nipple distance of > 1.0 cm has been reported as a significant threshold under specific study conditions for predicting nipple involvement in nipple-sparing mastectomy, while an MRI-to-skin distance of 5 mm is a suggested threshold for predicting margin positivity in skin-sparing mastectomy. This review also addresses the assessment of pectoralis muscle involvement and the new BI-RADS section dedicated to lymph node evaluation. Furthermore, MRI is highlighted as an essential tool for identifying eligible candidates for radiofrequency ablation by precisely excluding multifocality and extensive intraductal components to satisfy strict clinical criteria. The integration of these standardized, surgically-relevant metrics allows radiologists to provide a detailed anatomical roadmap, which is essential to maximize cosmetic outcomes through conservative mastectomy techniques without compromising oncological safety.

PMID:
42295592
Bibliographic data and abstract were imported from PubMed on 15 Jun 2026.

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