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

• Published in: Breast (Edinburgh) 2012-08, Vol.21 (4), p.503-506
• Main Authors: Ciatto, Stefano, Bernardi, Daniela, Calabrese, Massimo, Durando, Manuela, Gentilini, Maria Adalgisa, Mariscotti, Giovanna, Monetti, Francesco, Moriconi, Enrica, Pesce, Barbara, Roselli, Antonella, Stevanin, Carmen, Tapparelli, Margherita, Houssami, Nehmat
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Breast Neoplasms - diagnostic imaging; Computer assessment; Female; Hematology, Oncology and Palliative Medicine; Humans; Mammography; Mammography - methods; Middle Aged; Observer Variation; Radiographic Image Interpretation, Computer-Assisted - methods; Radiological density; Reproducibility of Results; Software
• ISSN: 0960-9776; 1532-3080
• DOI: 10.1016/j.breast.2012.01.005

Abstract 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.