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<p>A total of 150 patients with pathologically confirmed unilateral invasive breast cancer who underwent preoperative 3.0T MRI (including SyMRI sequences) at our institution were enrolled in the development set, while an external validation cohort consisted of 98 patients from Yidu Central Hospital. Based on IHC/FISH results, patients were categorized into HER2-low, HER2-zero and HER2-over groups. Two radiologists independently measured T1, T2, PD, and ADC values of the lesions. Logistic regression analysis was employed to identify the most effective predictors of HER2 expression status, and ROC curve analysis was performed to evaluate their discriminative ability. Univariate logistic regression indicated that the T2 value was a significant predictor for differentiating HER2 expression status. In the development set, T2 values demonstrated moderate diagnostic performance, with AUC values of 0.813 for HER2-low vs. over, 0.816 for HER2-zero vs. over and 0.876 for HER2-zero vs. low. Similarly, in the external validation set, T2 values showed moderate diagnostic efficacy, with AUCs of 0.837, 0.808 and 0.835 for the respective comparisons. T2 quantification derived from SyMRI shows promise as a noninvasive biomarker for identifying HER2-low-expressing breast cancer, supporting its potential role in guiding individualized treatment strategies.</p>

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A novel biomarker for identifying HER2-low breast cancer using synthetic MRI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Junzhong Liu,
  • Zhaofeng Zheng,
  • Yujing Chu,
  • Mingyuan Pang,
  • Qi Wang,
  • Wenjuan Wang,
  • Linkun Li,
  • Longjiang Fang,
  • Huanlei Zhang,
  • Shiying Ju

摘要

A total of 150 patients with pathologically confirmed unilateral invasive breast cancer who underwent preoperative 3.0T MRI (including SyMRI sequences) at our institution were enrolled in the development set, while an external validation cohort consisted of 98 patients from Yidu Central Hospital. Based on IHC/FISH results, patients were categorized into HER2-low, HER2-zero and HER2-over groups. Two radiologists independently measured T1, T2, PD, and ADC values of the lesions. Logistic regression analysis was employed to identify the most effective predictors of HER2 expression status, and ROC curve analysis was performed to evaluate their discriminative ability. Univariate logistic regression indicated that the T2 value was a significant predictor for differentiating HER2 expression status. In the development set, T2 values demonstrated moderate diagnostic performance, with AUC values of 0.813 for HER2-low vs. over, 0.816 for HER2-zero vs. over and 0.876 for HER2-zero vs. low. Similarly, in the external validation set, T2 values showed moderate diagnostic efficacy, with AUCs of 0.837, 0.808 and 0.835 for the respective comparisons. T2 quantification derived from SyMRI shows promise as a noninvasive biomarker for identifying HER2-low-expressing breast cancer, supporting its potential role in guiding individualized treatment strategies.