ArticleEuropean journal of radiology open2025
3.0 T multi-parametric MRI combined with clinical features improve malignancy prediction of BI-RADS 4 lesions and preoperative prediction of Nottingham Prognostic Index.
Article in European journal of radiology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Lightweight Transformer-based knowledge distillation framework for high-dimensional spatiotemporal radiomics in breast cancer risk prediction.Visual computing for industry, biomedicine, and art · 2026Article
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10 authors.
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Abstract
Purpose: To establish an optimal model to improve the malignancy prediction of BI-RADS 4 lesions and the preoperative prediction of tumor prognosis. Materials and methods: Ninety-six patients with 126 histopathology-confirmed breast lesions were included in the study. Conventional imaging features, radiomic features based on 3.0 T multi-parametric MRI and patient`s clinical characteristics were analyzed and selected as model candidate features. The least absolute shrinkage and selection operator (Lasso) and Random Forest (RF) were used to construct the combined model. Receiver operating characteristic (ROC) and Net Reclassification Improvement Index (NRI) were performed to assess the diagnostic efficiency between the model and BI-RADS category. Relative ratio (RR) was calculated to assess the ability of model to predict the invasiveness of breast cancers. Finally, the malignant probability (MP) calculated by the optimal model, MRI-based size and lymph node (LN) stage were used by logistic algorithm to construct a preoperative Nottingham Prognostic Index (NPI) model. Results: The combined model incorporating multi-parametric MRI and clinical characteristics was superior to BI-RADS category in the diagnosis of breast cancer (NRI: 1.71, p < 0.05), and had an accuracy of 94 % to predict the malignancy of BI-RADS 4 lesions Conclusions: The combined model based on multi-parametric MRI and clinical characteristics improves the malignancy prediction of BI-RADS 4 lesions and the preoperative prediction of NPI, therefore providing comprehensive information on the characteristics and treatment plans for breast cancer.
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