ArticleThe Indian journal of radiology & imaging2026
Malignancy Risk Stratification of Suspicious Breast Microcalcifications Detected on Mammograms Using Morphological and Distribution Characteristics Based on the Fifth Edition of BI-RADS.
Article in The Indian journal of radiology & imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers 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
- Article
- Explainable machine learning for breast mass characterization and malignancy risk stratification: multimodal integration of AI-derived structured digital breast tomosynthesis features and peripheral blood immune-inflammatory biomarkers.Frontiers in cell and developmental biology · 2026Article
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3 authors.
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Abstract
Background: Breast cancer is a major cause of mortality and morbidity in women. Hence, detecting suspicious microcalcifications on mammograms can be crucial for early diagnosis. Aims: To determine the malignancy risk of suspicious microcalcifications detected on mammograms in terms of positive predictive value (PPV) based on morphology and distribution characteristics and correlate results with BI-RADS Atlas, fifth edition and world literature. Materials and Methods: This is a hospital-based observational study conducted at our institute over 15-month duration and included all symptomatic and asymptomatic females who underwent mammogram, detected with suspicious breast microcalcifications, followed by stereotactic or ultrasound-guided breast biopsy and histopathology. Statistical Analysis: The chi-square test was used to test the association of the outcome. A Results: Among 77 lesions, 56 were not associated with mass and 21 were associated with mass. Overall PPV for malignancy of suspicious microcalcifications not associated with mass was 37.5%, and PPV of these according to morphology descriptors was: amorphous 9.5%; coarse heterogeneous 45.4%; fine pleomorphic 50%, and fine linear/fine linear branching 100% ( Conclusion: Results of our study correlated well with BI-RADS, fifth edition. Subcategorizing morphology and distribution descriptors provides accurate risk stratification, determines the need for image-guided biopsy, and guides further management strategies.
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