Radiomics Analysis of Contrast-Enhanced Breast MRI for Optimized Modelling of Virtual Prognostic Biomarkers in Breast Cancer
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View abstract on PubMed
Summary
This summary is machine-generated.Magnetic resonance imaging (MRI) radiomics can predict breast cancer prognostic biomarkers. This approach shows promise in identifying aggressive, node-positive triple-negative breast cancer, correlating with higher grades and stages.
Area Of Science
- Radiology
- Oncology
- Medical Imaging
Background
- Breast cancer management relies on clinical stage, nodal status, and biomarkers like ER, HER2, and grade.
- Accurate prediction of these factors can prevent unnecessary interventions, such as surgery.
Purpose Of The Study
- To evaluate the utility of MRI radiomics in predicting prognostic biomarkers for breast cancer.
- Investigate the potential of radiomics to yield virtual biomarkers including ER, HER2 expression, tumor grade, molecular subtype, and T-stage.
Main Methods
- Retrospective review of 209 invasive breast cancer patients who underwent dynamic contrast-enhanced (DCE) MRI.
- Extraction of Haralick texture features from DCE images and selection using Bootstrap Lasso.
- Performance assessment using area under the receiver operating characteristic curve (AUC).
Main Results
- Radiomics models showed moderate performance in differentiating nodal status (AUC=0.78 for N0 vs N1-N3) and predicting HER2 status (AUC=0.64).
- The model demonstrated potential in distinguishing high nuclear grade (AUC=0.71) and ER status (AUC=0.67).
- Performance was also assessed for molecular subtypes, with AUCs of 0.60 for triple-negative and 0.66 for Luminal A.
Conclusions
- Quantitative MRI radiomics texture analysis shows potential for identifying aggressive, node-positive triple-negative breast cancer.
- Radiomic features correlated well with higher nuclear grades, T-stages, and N-positive stages.
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