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Abstract
This study aims to assess the diagnostic accuracy of digital breast tomosynthesis in combination with full field digital mammography (DBT + FFDM) in the charaterisation of Breast Imaging-reporting and Data System (BI-RADS) category 3, 4 and 5 lesions. Retrospective cross-sectional study of 390 patients with BI-RADS 3, 4 and 5 mammography with available histopathology examination results were recruited from in a single center of a multi-ethnic Asian population. 2 readers independently reported the FFDM and DBT images and classified lesions detected (mass, calcifications, asymmetric density and architectural distortion) based on American College of Radiology-BI-RADS lexicon. Of the 390 patients recruited, 182 malignancies were reported. Positive predictive value (PPV) of cancer was 46.7%. The PPV in BI-RADS 4a, 4b, 4c and 5 were 6.0%, 38.3%, 68.9%, and 93.1%, respectively. Among all the cancers, 76% presented as masses, 4% as calcifications and 20% as asymmetry. An additional of 4% of cancers were detected on ultrasound. The sensitivity, specificity, PPV and NPV of mass lesions detected on DBT + FFDM were 93.8%, 85.1%, 88.8% and 91.5%, respectively. The PPV for calcification is 61.6% and asymmetry is 60.7%. 81.6% of cancer detected were invasive and 13.3% were in-situ type. Our study showed that DBT is proven to be an effective tool in the diagnosis and characterization of breast lesions and supports the current body of literature that states that integrating DBT to FFDM allows good characterization of breast lesions and accurate diagnosis of cancer.
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Details
1 University of Malaya, Department of Biomedical Imaging, University Malaya Research Imaging Centre, Kuala Lumpur, Malaysia (GRID:grid.10347.31) (ISNI:0000 0001 2308 5949)
2 University of Malaya, Department of Biomedical Imaging, University Malaya Research Imaging Centre, Kuala Lumpur, Malaysia (GRID:grid.10347.31) (ISNI:0000 0001 2308 5949); University Teknologi MARA, Department of Radiology, Faculty of Medicine, Sungai Buloh, Malaysia (GRID:grid.412259.9) (ISNI:0000 0001 2161 1343)
3 Taylor’s University, School of Medicine, Faculty of Health and Medical Sciences, Subang Jaya, Malaysia (GRID:grid.452879.5) (ISNI:0000 0004 0647 0003)