MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer.
[BACKGROUND] The application of 21-gene assays in clinical practice is jeopardized by their cost and availability.
APA
Chen Y, You L, et al. (2026). MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer.. BMC medical imaging, 26(1), 83. https://doi.org/10.1186/s12880-026-02153-1
MLA
Chen Y, et al.. "MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer.." BMC medical imaging, vol. 26, no. 1, 2026, pp. 83.
PMID
41547718
Abstract
[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 ( = 108) and internal validation ( = 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.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12880-026-02153-1.
[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 ( = 108) and internal validation ( = 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.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12880-026-02153-1.
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