Multiparametric MRI-Derived Habitat Radiomics in Subregional Analysis for Predicting Axillary Lymph Node Metastatic Burden in Breast Cancer
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View abstract on PubMed
Summary
This summary is machine-generated.A new nomogram combining clinical data and imaging features accurately predicts high axillary nodal burden in breast cancer patients. This tool aids in preoperative assessment, improving treatment planning for breast cancer.
Area Of Science
- Radiology and Medical Imaging
- Oncology
- Breast Cancer Research
Background
- Axillary nodal burden is a key indicator of breast cancer aggressiveness and prognosis.
- Accurate preoperative assessment of nodal burden is crucial for effective treatment planning.
Purpose Of The Study
- To develop and evaluate a subregional habitat radiomics model using multiparametric MRI for predicting high axillary nodal burden in breast cancer.
- To compare the performance of habitat radiomics, conventional radiomics (C-radiomics), and clinical models.
Main Methods
- Retrospective study of 221 breast cancer patients undergoing axillary lymph node dissection.
- Development of clinical, C-radiomics, and habitat radiomics models based on multiparametric MRI.
- Integration of models into a comprehensive nomogram for quantitative prediction of nodal burden.
Main Results
- The habitat radiomics model showed superior performance (AUCs 0.791-0.798) compared to C-radiomics (AUCs 0.733-0.738) and clinical models (AUCs 0.733-0.753).
- The combined nomogram achieved the highest diagnostic performance with AUCs of 0.895 (training) and 0.885 (validation).
- The nomogram demonstrated strong predictive efficacy for preoperative assessment of axillary nodal burden.
Conclusions
- An integrated nomogram combining clinical, C-radiomics, and habitat radiomics models offers robust preoperative prediction of axillary nodal burden in breast cancer.
- This approach enhances the accuracy of assessing nodal status, potentially guiding surgical and systemic treatment decisions.
- Further multicenter prospective studies are recommended to validate and refine the clinical applicability of the developed nomogram.
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