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

• DOI: 10.1007/s00330-015-3784-2
• Publication Date: 2015-04-30T08:24:43Z
• Authors: Daniela Sacchetto; Lia Morra; Silvano Agliozzo; Daniela Bernardi; Tomas Björklund; Beniamino Brancato; Patrizia Bravetti; Luca A. Carbonaro; Loredana Correale; Carmen Fantò; Elisabetta Favettini; Laura Martincich; Luisella Milanesio; Sara Mombelloni; Francesco Monetti; Doralba Morrone; Marco Pellegrini; Barbara Pesce; Antonella Petrillo; Gianni Saguatti; Carmen Stevanin; Rubina M. Trimboli; Paola Tuttobene; Marvi Valentini; Vincenzo Marra; Alfonso Frigerio; Alberto Bert; Francesco Sardanelli
• Keywords: Breast density; Neuroradiology; Interventional radiology; BI-RADS

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 9 centers and 6 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. Panel majority report (PMR) was used as reference standard. Agreement (k) 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 PMR ranged from k=0.483 to k=0.885, the ABDE correctly classified 563/613 mammograms (92%). A substantial agreement for binary classification was found for individual reader pairs (k=0.620, standard deviation [SD]=0.140), individual versus PMR (k=0.736, SD=0.117), and individual versus ABDE (k=0.674, SD=0.095). Agreement between ABDE and PMR was almost perfect (k=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.