Enhanced Prediction of HER2 Status in Breast Cancer through the Integration of Intratumoral and Peritumoral Radiomic Features from DCE-MRI - Report - MDSpire

Enhanced Prediction of HER2 Status in Breast Cancer through the Integration of Intratumoral and Peritumoral Radiomic Features from DCE-MRI

  • By

  • Yang Gao

  • Jiangnian Gong

  • Yuanling Yang

  • Yingyi Luo

  • Weiyi Liu

  • Zisan Zeng

  • February 13, 2026

  • 0 min

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Clinical Report: Enhanced Prediction of HER2 Status in Breast Cancer

Overview

This study evaluates a novel prediction model for HER2 status in breast cancer using intratumoral and peritumoral radiomic features from DCE-MRI. The findings suggest that integrating these features can enhance the accuracy of HER2 status prediction, particularly for the HER2-low subtype.

Background

HER2 status is crucial for determining treatment strategies and prognosis in breast cancer. The emergence of targeted therapies has improved outcomes for HER2-positive patients, but there is a growing need for non-invasive methods to assess HER2 expression, especially for the HER2-low subtype. This study addresses the limitations of traditional biopsy methods by exploring radiomic features derived from MRI.

Data Highlights

No numerical data available in the provided source material.

Key Findings

  • The study developed a prediction model utilizing intratumoral and peritumoral radiomic features from DCE-MRI.
  • HER2-low breast cancer has been identified as a distinct therapeutic target, necessitating accurate assessment methods.
  • Traditional methods for HER2 status evaluation are invasive and may miss low-level expressions.
  • Radiomics can bridge the gap between imaging and precision medicine, offering a non-invasive alternative for HER2 status prediction.
  • The peritumoral region's features are underexplored but may provide critical insights into tumor microenvironments.

Clinical Implications

The integration of radiomic features from MRI may provide a reliable, non-invasive method for assessing HER2 status, particularly for patients with HER2-low breast cancer. This approach could enhance treatment personalization and improve patient outcomes.

Conclusion

The study highlights the potential of radiomics in improving HER2 status prediction in breast cancer, particularly for the HER2-low subtype, thereby supporting the need for further research in this area.

References

  1. Tarantino et al., European Radiology, 2020 -- Evaluation of Anti-HER2 Treatment Response for Tailoring Therapy in Early HER2-Positive Breast Cancer Utilizing an Innovative Deep Learning Radiomics Approach
  2. European Radiology, 2025 -- Radiomics Utilizing Deep Learning Fails to Enhance Prediction of Residual Cancer Burden Following Chemotherapy in the LIMA Breast MRI Study
  3. European Radiology, 2024 -- The Role of Diffusion-Weighted Imaging Combined with Contrast-Enhanced MRI in Assessing Complete Response in HER2-Positive Breast Cancer
  4. Journal of Neuro-Oncology, 2014 -- Predicting early brain metastases based on clinicopathological factors and gene expression analysis in advanced HER2-positive breast cancer patients
  5. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: ASCO-College of American Pathologists Guideline Update, PubMed, 2023
  6. Survival with Trastuzumab Emtansine in Residual HER2-Positive Breast Cancer, PubMed, 2023
  7. Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral radiomics, PubMed, 2023
  8. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: ASCO-College of American Pathologists Guideline Update - PubMed
  9. Survival with Trastuzumab Emtansine in Residual HER2-Positive Breast Cancer - PubMed
  10. Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral radiomics - PubMed

Original Source(s)

Enhanced Prediction of HER2 Status in Breast Cancer through the Integration of Intratumoral and Peritumoral Radiomic Features from DCE-MRI

  • by Yang Gao, Jiangnian Gong, Yuanling Yang, Yingyi Luo, Weiyi Liu, Zisan Zeng

  • February 13, 2026

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