ArticleRadiation oncology (London, England)2025
Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: a multicentre study.
Article in Radiation oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions.Journal of cancer research and clinical oncology · 2024Pooled it
- Diagnosing breast masses in BI-RADS 4: construction and interpretation of an intralesional and perilesional radiomics nomogram based on digital breast tomosynthesis.BMC medical imaging · 2026Article
- Multimodal radiomics incorporating intratumoral heterogeneity for prognostic assessment of metastatic outcomes in invasive breast cancer.Breast cancer research : BCR · 2026Article
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10 authors.
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Abstract
background and purposeThe study aimed to create a radiomics model based on breast intra- and peri-tumoral regions in dynamic contrast-enhanced (DCE) MRI to distinguish benign from malignant breast lesions of Breast Imaging Reporting and Data System (BI-RADS) 4. MATERIALS AND
methodsA total of 516 patients from Hospital 1 were assigned to the training cohort. Then, 146 and 52 patients were enrolled from Hospital 2 and 3, respectively, as the internal and external test cohort. Seven classification models were built, using features extracted from the intra- and peri-tumoral regions. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis and compared by the DeLong test. Subgroup analysis was performed after stratifying all lesions by enhancement pattern and the subdivision of BI-RADS 4.
resultsThe Comb2 model, built with features from peri-tumoral 2 mm and intra-tumoral region, demonstrated the best performance with AUCs of 0.828 and 0.844 in the internal and external test cohort, respectively. The Comb2 model was robust in both mass and non-mass enhancement (NME) lesions. At the three exploratory cutoff values on the ROC curve, the model identified 9.1% (sensitivity of C1 ≥ 98%), 27.3% (sensitivity of C2 ≥ 95%) and 36.4% (sensitivity of C3 ≥ 90%) of the benign lesions in the external test cohort. Applying the identified cutoff values in the external test cohort showed the potential to lower the number of unnecessary biopsies of benign lesions.
conclusionAn MRI-based radiomics model built with features extracted from the intra-tumoral region combined with the peri-tumoral 2 mm showed the best potential to reduce false-positive diagnoses and may avoid unnecessary biopsies with a low underestimate risk.
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