ArticleQuantitative imaging in medicine and surgery2026
Diagnostic performance of contrast-enhanced mammography and MRI for breast non-mass microcalcifications: a novel Malignant Calcification Score in contrast-enhanced mammography.
Article in Quantitative imaging in medicine and surgery, 2026. 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.
- Machine Learning-Based Classification of BI-RADS 4 and BI-RADS 5 Microcalcifications in Mammography Combined with DCE-MRI for Malignant-Benign Discrimination.Tomography (Ann Arbor, Mich.) · 2026Article
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9 authors.
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Abstract
Background: Non-mass microcalcifications present a significant diagnostic challenge in breast imaging, highlighting the urgent need for more objective and accurate tools to enhance the specificity of pre-biopsy evaluations for suspicious non-mass microcalcifications. This study aims to develop a novel Malignant Calcification Score (MCS) using contrast-enhanced mammography (CEM) to characterize malignant microcalcifications and to compare its diagnostic performance with the established Breast Imaging Reporting and Data System (BI-RADS). Methods: A cohort of 99 patients presenting with non-mass-associated microcalcifications was stratified into benign and malignant groups based on definitive pathological diagnoses. Logistic multiple regression and receiver operating characteristic (ROC) analysis were performed to identify independent predictors and to establish MCS. Its diagnostic performance was evaluated and compared with BI-RADS for mammography (MG) and magnetic resonance imaging (MRI). Results: In low-energy imaging, microcalcification morphology and distribution patterns showed statistically significant differences between benign and malignant lesions (all P<0.05). In the recombined images, enhancement descriptors-including the percentage signal difference (in the craniocaudal oblique view) [%RS(CC)], modified time-intensity curve (mTIC), enhancement morphological distribution, and internal enhancement pattern-served as independent predictors of malignancy (all P<0.05), with %RS(CC) specifically predictive of invasive carcinoma (P=0.007). The MCS demonstrated high diagnostic performance [area under the curve (AUC): 0.908-0.930], significantly outperforming both BI-RADS (MG) (AUC: 0.678-0.679) and BI-RADS (MRI) (AUC: 0.724) (DeLong's test: all P<0.05). Conclusions: The MCS introduces a novel diagnostic framework by integrating key descriptors from low-energy and recombined CEM images, demonstrating superior performance compared to BI-RADS standards. Its clinical application helps reduce diagnostic uncertainty and decreases the rate of unnecessary biopsies.
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