ArticleTechnology in cancer research & treatment
Multiparametric MRI-Derived Habitat Radiomics in Subregional Analysis for Predicting Axillary Lymph Node Metastatic Burden in Breast Cancer.
Article in Technology in cancer research & treatment. 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
IntroductionAxillary nodal burden reflects the biological aggressiveness and prognostic behavior of breast cancer. This study aimed to develop a subregional habitat radiomics model based on multiparametric magnetic resonance imaging (MRI) and to evaluate its performance in predicting high axillary nodal burden in patients with breast cancer.MethodsIn this retrospective study, a total of 221 patients who underwent axillary lymph node dissection were categorized as having limited (0-2 metastatic nodes) or high (≥3 metastatic nodes) nodal burden based on pathological findings. Morphological MRI features were visually evaluated by experienced radiologists. A clinical model was established using univariate and multivariate logistic regression analyses. Conventional radiomics (C-radiomics) and habitat radiomics features were extracted from the whole tumor and its subregions, respectively, based on multiparametric MRI. The clinical, C-radiomics, and habitat radiomics models were then integrated into a comprehensive nomogram for quantitative prediction of axillary nodal burden.ResultsIn predicting axillary nodal burden, the habitat radiomics model outperformed both the C-radiomics and clinical models, achieving areas under the curve (AUCs) of 0.791 (0.712-0.870) and 0.798 (0.686-0.911) in the training and validation cohorts, respectively. The C-radiomics model achieved AUCs of 0.733 (0.631-0.836) and 0.738 (0.612-0.865), while the clinical model achieved AUCs of 0.753 (0.663-0.843) and 0.733 (0.596-0.870). The combined nomogram demonstrated the highest diagnostic performance, with AUCs of 0.895 (0.839-0.951) and 0.885 (0.802-0.969) in the training and validation cohorts, respectively.ConclusionsThe integrated nomogram combining clinical, C-radiomics, and habitat radiomics models demonstrated strong predictive efficacy for preoperative assessment of axillary nodal burden in breast cancer. Future multicenter prospective studies are warranted to validate these results and refine the model's clinical applicability.
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