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Development and Validation of a Radiomics-Based Nomogram for Predicting HER-2 Status in Breast Cancer: A
Qingxiang Qiu1, Chajin Chen1, Jinyin Chen1
1Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, 364000, People's Republic of China.
Breast Cancer (Dove Medical Press)
|December 8, 2025
Summary
This study developed a radiomics nomogram using MRI to predict HER-2 status in breast cancer, offering a non-invasive tool for personalized treatment. The nomogram showed high accuracy in predicting HER-2 expression.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
Background:
- HER-2 expression is crucial for breast cancer treatment decisions.
- Accurate prediction of HER-2 status is essential for personalized therapy.
- Non-invasive methods for predicting HER-2 status are highly desirable.
Purpose of the Study:
- To develop and validate a radiomics-based nomogram using multimodal magnetic resonance imaging (MRI) features.
- To predict HER-2 expression status in breast cancer non-invasively.
- To improve personalized treatment strategies for breast cancer patients.
Main Methods:
- Retrospective selection of 320 breast cancer patients (80 HER-2 positive, 240 HER-2 negative).
- Extraction of radiomic features from pre-treatment multimodal MRI (DCE-MRI, DWI, T2-weighted imaging).
- Development and validation of a radiomics nomogram using logistic regression, ROC curve analysis, and DCA.
Main Results:
- Multivariate analysis identified key predictors: tumor type, edge, skin thickening/depression, and axillary lymph node enlargement.
- The radiomics nomogram achieved excellent predictive accuracy (AUC 0.866 training, 0.876 validation).
- The model demonstrated good consistency and significant clinical benefit, with higher tumor marker expression and immune cell infiltration in HER-2 positive cases.
Conclusions:
- A radiomics-based nomogram using multimodal MRI is a promising non-invasive tool for predicting HER-2 expression in breast cancer.
- This model aids in personalized treatment strategies by providing accurate predictions.
- Further validation in larger, multicenter studies is recommended to confirm generalizability.
