Breast Density Evaluation According to BI-RADS 5th Edition on Digital Breast Tomosynthesis: AI Automated Assessment Versus Human Visual Assessment

Abstract

Background: The assessment of breast density is one of the main goals of radiologists because the masking effect of dense fibroglandular tissue may affect the mammographic identification of lesions. The BI-RADS 5th Edition has revised the mammographic breast density categories, focusing on a qualitative evaluation rather than a quantitative one. Our purpose is to compare the concordance of the automatic classification of breast density with the visual assessment according to the latest available classification. Methods: A sample of 1075 digital breast tomosynthesis images from women aged between 40 and 86 years (58 ± 7.1) was retrospectively analyzed by three independent readers according to the BI-RADS 5th Edition. Automated breast density assessment was performed on digital breast tomosynthesis images with the Quantra software version 2.2.3. Interobserver agreement was assessed with kappa statistics. The distributions of breast density categories were compared and correlated with age. Results: The agreement on breast density categories was substantial to almost perfect between radiologists (κ = 0.63–0.83), moderate to substantial between radiologists and the Quantra software (κ = 0.44–0.78), and the consensus of radiologists and the Quantra software (κ = 0.60–0.77). Comparing the assessment for dense and non-dense breasts, the agreement was almost perfect in the screening age range without a statistically significant difference between concordant and discordant cases when compared by age. Conclusions: The categorization proposed by the Quantra software has shown a good agreement with the radiological evaluations, even though it did not completely reflect the visual assessment. Thus, clinical decisions regarding supplemental screening should be based on the radiologist’s perceived masking effect rather than the data produced exclusively by the Quantra software.

Practical Challenges of DBT-Guided VABB: Harms and Benefits, from Literature to Clinical Experience

“…A p < 0.05 was considered statistically significant, with a 95% confidence interval. According to Landis J.R. and Koch G.G., the kappa values were interpreted as follows: 0.0-0.20, slight agreement; 0.21-0.40, fair agreement; 0.41-0.60, moderate agreement; 0.61-0.80, substantial agreement; 0.81-1.00, almost perfect agreement [4].…”

“…Evaluation of laboratory tests (PT, PTT, INR, platelet count) (3) DBT images evaluation (to choose the correct gauge and aperture of the needle) VABB examination (4) Full explanations of the procedure (written informed consent; is there psychological stress? )…”

“…Conventional two-dimensional (2D) full-field digital mammography (FFDM) is considered the gold standard for early diagnosis in BC screening programs, but the potential overlap of tissue in dense breasts is a major limitation, because it can obscure an area of interest and lead to a false-negative finding [4]. In addition, the overlap of normal structures can create a pseudolesion, often called summation artifact, which drives to a false-positive result [5].…”

Editorial for “Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter‐Observer Variability”

“…3 Previous studies have demonstrated the effectiveness of AI models and radiomics features in reducing inter-reader variability. [4][5][6] By automating the interpretation process and providing consistent assessments, AI has the potential to significantly improve the accuracy and efficiency of breast density identification.…”