ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025
Multi-parametric MRI radiomics predicts different HER2 expression in breast cancer.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Explainable AI-Assisted Multimodal Ultrasound Radiomics for Preoperative Risk Stratification of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.Journal of imaging informatics in medicine · 2026Article
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13 authors.
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Abstract
purposeTo develop and validate radiomic models using multi-parametric dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent movement (IVIM)-based features for the preoperative differentiation of HER2 expressions levels in breast cancer. MATERIALS AND
methodsThis retrospectively study analyzed 227 female breast cancer patients who underwent breast 3.0T MRI examination at our institution from December 2019 to December 2023. The least absolute shrinkage and selection operator (LASSO) ten-fold cross-validation method was used to develop the radiomic features to identify HER2 positive and HER2 negative cancer(task 1), and further identify HER2 low and HER2 zero cancer(task 2). Then the radiomic features were selected and combined with clinical characteristics to construct predicting models using the logistic regression analysis. The area under the receiver operating characteristic curve(AUC), sensitivity, and specificity were used to evaluate the performance of radiomic models.
resultsFor task 1, the AUCs of clinical model (histological grade and peritumoral edema), DCE combined IVIM(D + D*+f) radiomic model and clinic combined radiomic model were 0.785 (95%CI:0.713,0.846), 0.866 (95%CI:0.803,0.915) and 0.903 (95%CI:0.846,0.944) respectively. In the validation cohort, The AUCs were 0.751 (95%CI:0.633,0.848), 0.751 (95%CI:0.633,0.848) and 0.830 (95%CI:0.720,0.910) respectively. For task 2, the AUCs of DCE combined IVIM radiomic model in training and validation cohort were 0.951 (95%CI:0.888,0.984) and 0.853 (95%CI:0.712,0.942) respectively, and the radiomics score was independent predictors of HER2 low cancer.
conclusionThe radiomic signature derived from multi-parametric MRI, together with peritumoral edema and histological grade, demonstrated strong performance in predicting HER2 expression preoperatively in breast cancer, which may support individualized treatment strategies.
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