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Background <p>The application of 21-gene assays in clinical practice is jeopardized by their cost and availability. This study aimed to predict the recurrence score (RS) of a 21-gene assay using MRI peritumoral radiomics in ER+/HER2- breast cancers.</p> Methods <p>154 and 39 patients with ER+/HER2- breast cancer from two centers were enrolled, who underwent 21-gene test and preoperative MRI. Patients from Center 1 were divided into training (<i>n</i> = 108) and internal validation (<i>n</i> = 46) cohorts, and patients from Center 2 were enrolled in the external validation cohort. Radiomics features were extracted from the tumoral, peritumoral and dilation volumes of interest with peritumoral ranges of 1&#xa0;mm, 3&#xa0;mm, 5&#xa0;mm, 7&#xa0;mm, and 9&#xa0;mm. After feature selection, RS-prediction models were constructed using support vector machine method to distinguish high (RS ≥ 26) from low RS (RS &lt; 26).</p> Results <p>As the thickness of the peritumor tissue increased, the AUC of models increased and then decreased, with the 3-mm model performing the best. Among all RS-prediction models, the 3&#xa0;mm peritumoral model based on T2WI (T2-p3) achieved larger AUCs (0.70 and 0.69 in the internal and external validation cohorts, separately). The peritumoral-fusion model integrating intratumoral radiomic and imaging-clinicopathological features with the T2-p3 model, obtained greater AUCs (0.82 and 0.75 in the internal and external validation cohorts, separately).</p> Conclusions <p>MRI peritumoral radiomic data exhibits the potential to serve as a biomarker of recurrence risk in patients with ER+/HER2- breast cancer.</p>

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MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月17日

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  • Yang Chen,
  • Liang You,
  • Yan Huang,
  • Lizhi Xie,
  • Qin Xiao,
  • Tianwen Xie,
  • Ling Zhang,
  • Rong Li,
  • Qifeng Wang,
  • Yingshi Sun,
  • Wei Tang,
  • Yajia Gu,
  • Weijun Peng

摘要

Background

The application of 21-gene assays in clinical practice is jeopardized by their cost and availability. This study aimed to predict the recurrence score (RS) of a 21-gene assay using MRI peritumoral radiomics in ER+/HER2- breast cancers.

Methods

154 and 39 patients with ER+/HER2- breast cancer from two centers were enrolled, who underwent 21-gene test and preoperative MRI. Patients from Center 1 were divided into training (n = 108) and internal validation (n = 46) cohorts, and patients from Center 2 were enrolled in the external validation cohort. Radiomics features were extracted from the tumoral, peritumoral and dilation volumes of interest with peritumoral ranges of 1 mm, 3 mm, 5 mm, 7 mm, and 9 mm. After feature selection, RS-prediction models were constructed using support vector machine method to distinguish high (RS ≥ 26) from low RS (RS < 26).

Results

As the thickness of the peritumor tissue increased, the AUC of models increased and then decreased, with the 3-mm model performing the best. Among all RS-prediction models, the 3 mm peritumoral model based on T2WI (T2-p3) achieved larger AUCs (0.70 and 0.69 in the internal and external validation cohorts, separately). The peritumoral-fusion model integrating intratumoral radiomic and imaging-clinicopathological features with the T2-p3 model, obtained greater AUCs (0.82 and 0.75 in the internal and external validation cohorts, separately).

Conclusions

MRI peritumoral radiomic data exhibits the potential to serve as a biomarker of recurrence risk in patients with ER+/HER2- breast cancer.