ArticleFrontiers in oncology2026
Development and validation of a multimodality radiomics-based nomogram for predicting HER2 expression status in invasive breast cancer.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: This study aimed to develop and validate a nomogram integrating multimodal imaging and clinical features for the preoperative prediction of human epidermal growth factor receptor 2 (HER2) expression status in patients with invasive breast cancer. Methods: A total of 204 patients with pathologically confirmed breast cancer who underwent preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and mammography (MG) were retrospectively enrolled. Patients were randomly divided into training and test sets in a 7:3 ratio. Pearson correlation coefficient (PCC) combined with recursive feature elimination (RFE) was used to select optimal radiomics features. Three binary classification tasks were constructed (Model_1: HER2-Positive vs. HER2-Zero; Model_2: HER2-Positive vs. HER2-Low; Model_3: HER2-Low vs. HER2-Zero), each containing three sub-models based on MG, DCE-MRI, and their combination. The optimal radiomics model for each task was selected and integrated with independent clinicopathological factors to establish a clinical-radiomics nomogram. Results: The combined radiomics model achieved the best performance for Model_1 and Model_2, with area under the curve (AUC) values of 0.742 (95% confidence interval [CI]: 0.624-0.860) and 0.823 (95% CI: 0.749-0.897) in the training set, and 0.718 (95% CI: 0.594-0.842) and 0.778 (95% CI: 0.696-0.861) in the validation set, respectively. Corresponding nomograms were subsequently constructed incorporating independent clinical predictors. For Model_3, the single DCE-MRI model demonstrated optimal performance (training AUC = 0.831, 95% CI: 0.747-0.916; validation AUC = 0.745, 95% CI: 0.640-0.850). However, no nomogram was developed due to the absence of significant clinicopathological features (p > 0.05). Conclusion: The multimodal radiomics-based nomogram, integrating imaging features and clinical factors, provides a promising non-invasive quantitative tool for preoperative evaluation of HER2 expression status in breast cancer, offering significant potential for clinical translation.
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