Mammographic density: Comparison of visual assessment with fully automatic calculation on a multivendor dataset.

OBJECTIVES: To compare breast density (BD) assessment provided by an automated BD evaluator (ABDE) with that provided by a panel of experienced breast radiologists, on a multivendor dataset. METHODS: Twenty-one radiologists assessed 613 screening/diagnostic digital mammograms from nine centers and six different vendors, using the BI-RADS a, b, c, and d density classification. The same mammograms were also evaluated by an ABDE providing the ratio between fibroglandular and total breast area on a continuous scale and, automatically, the BI-RADS score. A panel majority report (PMR) was used as reference standard. Agreement (κ) and accuracy (proportion of cases correctly classified) were calculated for binary (BI-RADS a-b versus c-d) and 4-class classification. RESULTS: While the agreement of individual radiologists with the PMR ranged from κ = 0.483 to κ = 0.885, the ABDE correctly classified 563/613 mammograms (92 %). A substantial agreement for binary classification was found for individual reader pairs (κ = 0.620, standard deviation [SD] = 0.140), individual versus PMR (κ = 0.736, SD = 0.117), and individual versus ABDE (κ = 0.674, SD = 0.095). Agreement between ABDE and PMR was almost perfect (κ = 0.831). CONCLUSIONS: The ABDE showed an almost perfect agreement with a 21-radiologist panel in binary BD classification on a multivendor dataset, earning a chance as a reproducible alternative to visual evaluation. KEY POINTS: Individual BD assessment differs from PMR with κ as low as 0.483. An ABDE correctly classified 92 % of mammograms with almost perfect agreement (κ = 0.831). An ABDE can be a valid alternative to subjective BD assessment.

Characterisation of microcalcification clusters on 2D digital mammography (FFDM) and digital breast tomosynthesis (DBT): does DBT underestimate microcalcification clusters? Results of a multicentre study.

OBJECTIVES: To compare DBT and FFDM in the classification of microcalcification clusters (MCs) using BI-RADS. METHODS: This Institutional Review Board-approved study was undertaken in three centres. A total of 107 MCs evaluated with both DBT and FFDM were randomised for prospective reading by six experienced breast radiologists and classified using BI-RADS. RESULTS: The benign/malignant ratio of MC was 66/41. Of 11/107 discordant results, DBT classified MCs as R2 whereas FFDM classified them as R3 in 9 and R4 in 2. Three of these (3/107 = 2.8%) were malignant; 8 (7.5%) were nonmalignant and were correctly classified as R2 on DBT but incorrectly classified as R3 on FFDM. Estimated sensitivity and specificity, respectively, were 100% (95% CI: 91% to 100%) and 94.6% (95% CI: 86.7% to 98.5%) for FFDM and 91.1% (95% CI: 78.8% to 97.5%) and 100% (95% CI: 94.8% to 100%) for DBT. Overall intra- and interobserver agreements were 0.75 (95% CI: 0.61-0.84) and 0.73 (95% CI: 0.62-0.78). CONCLUSIONS: Most MCs are scored similarly on FFDM and DBT. Although a minority (11/107) of MCs are classified differently on FFDM (benign MC classified as R3) and DBT (malignant MC classified as R2), this may have clinical relevance. KEY POINTS: • The BI-RADS classification of MC differs for FFDM and DBT in 11/107 cases • DBT assigned lower BI-RADS classes compared to FFDM in 11 clusters • In 4/107 DBT may have missed some malignant and high-risk lesions • In 7/107 the 'underclassification' on DBT was correct, potentially avoiding unnecessary biopsies • DBT may miss a small proportion of malignant lesions.

A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification.

Breast radiological density is a determinant of breast cancer risk and of mammography sensitivity and may be used to personalize screening approach. We first analyzed the reproducibility of visual density assessment by eleven experienced radiologists classifying a set of 418 digital mammograms: reproducibility was satisfactory on a four (BI-RADS D1-2-3-4: weighted kappa = 0.694-0.844) and on a two grade (D1-2 vs D3-4: kappa = 0.620-0.851), but subjects classified as with dense breast would range between 25.1 and 50.5% depending on the classifying reader. Breast density was then assessed by computer using the QUANTRA software which provided systematically lower density percentage values as compared to visual classification. In order to predict visual classification results in discriminating dense and non-dense breast subjects on a two grade scale (D3-4 vs, D1-2) the best fitting cut off value observed for QUANTRA was ≤22.0%, which correctly predicted 88.6% of D1-2, 89.8% of D3-4, and 89.0% of total cases. Computer assessed breast density is absolutely reproducible, and thus to be preferred to visual classification. Thus far few studies have addressed the issue of adjusting computer assessed density to reproduce visual classification, and more similar comparative studies are needed.