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Time-Dependent Diffusion MRI-Based Microstructural Mapping for Characterizing HER2-Zero, -Low, -Ultra-Low, and

Xiaoxia Wang1,2, Yao Huang2, Ying Cao2

  • 1Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.

Journal of Magnetic Resonance Imaging : JMRI
|August 20, 2025
PubMed
Summary
This summary is machine-generated.

Time-dependent diffusion MRI (td-dMRI) shows promise for non-invasively distinguishing breast cancer subtypes. Selected histogram parameters accurately identified HER2-positive, HER2-low, and HER2-ultralow subtypes, aiding treatment decisions.

Keywords:
breast neoplasmhuman epidermal growth factor receptor 2time‐dependent diffusion MRI

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Area of Science:

  • Biomedical Imaging
  • Oncology
  • Radiology

Background:

  • Accurate non-invasive methods are crucial for differentiating human epidermal growth factor receptor 2 (HER2) subtypes in breast cancer.
  • Time-dependent diffusion MRI (td-dMRI) offers potential for characterizing breast cancer microstructures.
  • The utility of td-dMRI in identifying HER2 subtypes remains unexplored.

Purpose of the Study:

  • To assess the feasibility of using td-dMRI-derived microstructural histogram parameters for characterizing four HER2 subtypes in breast cancer.
  • To explore the potential of td-dMRI in non-invasively classifying breast cancer subtypes.

Main Methods:

  • Prospective study involving 495 participants with invasive breast cancer.
  • 3-T td-dMRI using oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) sequences.
  • Analysis of 49 histogram parameters, including microstructural and apparent diffusion coefficient maps, with statistical comparisons and predictive performance assessment using area under the curve (AUC).

Main Results:

  • Thirty-two histogram parameters demonstrated significant differences across the four HER2 subgroups (HER2-zero, -ultralow, -low, and -positive).
  • Developed models achieved high AUCs for distinguishing HER2-positive vs. negative (0.85), HER2-positive vs. low (0.87), and HER2-low vs. immunohistochemistry 0 (0.81).
  • Moderate performance was observed for differentiating HER2-zero vs. -ultralow (AUC of 0.77).

Conclusions:

  • Selected td-dMRI-derived histogram parameters show potential for non-invasively identifying HER2 subtypes in breast cancer.
  • This imaging technique could aid in personalized treatment strategies for breast cancer patients based on HER2 status.