ArticleBMC cancer2025
Revolutionizing HER-2 assessment: multidimensional radiomics in breast cancer diagnosis.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Radiomic Characterization of Breast Tissue from Breast CT Images Obtained with Synchrotron Beams.Tomography (Ann Arbor, Mich.) · 2026Article
- Mammography-Based Radiomics for Prediction of Nodal Status and Disease Burden in Breast Cancer: A Temporally Validated Study.Cancers · 2026Article
- Multiparametric MRI-derived radiomic signatures enable noninvasive prediction of HER2 expression in bladder cancer.International urology and nephrology · 2026Article
- Breast tumor segmentation and morphological feature-based classification in ultrasound using a two-stage U-net and SVM.Frontiers in bioengineering and biotechnology · 2026Article
- Preliminary Evaluation of Radiomics in Contrast-Enhanced Mammography for Prognostic Prediction of Breast Cancer.Cancers · 2025Article
- Multidimensional evaluation of 3.0T HR-MRI, ultrasound imaging, and GATA3 protein expression in breast cancer, and their prognostic analysis.Frontiers in oncology · 2025Article
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4 authors.
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Abstract
objectiveTo explore the application value of multidimensional radiomics based on ultrasound imaging in assessing the HER-2 status of breast cancer.
methodsWe retrospectively analyzed the ultrasound imaging, clinical, and laboratory data of 850 breast cancer patients from two centers. During the study, we first utilized automation technology to accurately delineate the tumor region of interest (ROI) in breast ultrasound imaging. Subsequently, the intra-tumoral ROI was automatically expanded by 1 cm and 2 cm to obtain larger areas including the peritumoral tissues, and further generated three-dimensional volumes of interest (VOI) within and around the tumor. Through the K-means clustering method, we identified the sub-regions of interest within the ROI and extracted corresponding radiomic features using the pyradiomics toolkit. Additionally, we employed an advanced Vision Transformer (VIT) model to perform deep radiomic feature extraction on the ROI. Based on feature selection, we utilized various machine learning algorithms for modeling and analysis to assess the HER-2 status of breast cancer.
resultsAfter comprehensive comparison and evaluation of multiple models, we found that the diagnostic model based on multidimensional feature fusion exhibited excellent diagnostic performance in assessing the HER-2 status of breast cancer. In the training set, the model achieved an accuracy of 0.949 and an AUC value of 0.990 (95% CI: 0.986-0.995), with outstanding key performance indicators such as sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. The model showed good generalization in the test set, with accuracy 0.747, AUC 0.848 (95% CI: 0.791-0.904), and sensitivity 0.911. Specificity was slightly lower, but other indicators remained high, and the F1 score was 0.703. Calibration and clinical decision curves further confirmed the model's effectiveness and reliability.
conclusionThis study fully demonstrates that multidimensional breast ultrasonography-based radiomic features can effectively assess the HER-2 status of breast cancer. This finding not only provides new evidence for early diagnosis of breast cancer but also offers new ideas and methods for personalized treatment planning and prognosis assessment.
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