Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case–control study

• Published in: BMC cancer Vol. 16; no. 1; p. 414
• Main Authors: Winkel, Rikke Rass, von Euler-Chelpin, My, Nielsen, Mads, Petersen, Kersten, Lillholm, Martin, Nielsen, Michael Bachmann, Lynge, Elsebeth, Uldall, Wei Yao, Vejborg, Ilse
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.07.2016; BioMed Central Ltd
• Subjects: Analysis; Automation; Biomedical and Life Sciences; Biomedicine; Breast cancer; Breast Density; Breast Neoplasms - diagnostic imaging; Cancer; Cancer Research; Case-Control Studies; Classification; Denmark; Diagnosis; Digitization; Early Detection of Cancer; Epidemiology; Female; Health aspects; Health Promotion and Disease Prevention; Humans; Mammography; Mammography - methods; Medicine/Public Health; Middle Aged; Odds Ratio; Oncology; Precision Medicine; prevention and public health; Radiology; Research Article; Risk Assessment; Risk factors; ROC Curve; Surgical Oncology; Women; Denmark; United States > US; Mammographic texture; BI-RADS density; Risk prediction; Breast cancer; Tabár; Mammographic parenchymal pattern; Mammographic breast density
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-016-2450-7

Background Mammographic density is a well-established risk factor for breast cancer. We investigated the association between three different methods of measuring density or parenchymal pattern/texture on digitized film-based mammograms, and examined to what extent textural features independently and jointly with density can improve the ability to identify screening women at increased risk of breast cancer. Methods The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár’s classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association with breast cancer was estimated using binary logistic regression to calculate Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs). Results Cases showed significantly higher BI-RADS and texture scores on average than controls ( p  < 0.001). All three methods were individually able to segregate women into different risk groups showing significant ORs for BI-RADS D3 and D4 (OR: 2.37; 1.32–4.25 and 3.93; 1.88–8.20), Tabár’s PIII and PIV (OR: 3.23; 1.20–8.75 and 4.40; 2.31–8.38), and the highest quartile of the texture score (3.04; 1.63–5.67). AUCs for BI-RADS, Tabár and the texture scores (continuous) were 0.63 (0.57–0–69), 0.65 (0.59–0–71) and 0.63 (0.57–0–69), respectively. Combining two or more methods increased model fit in all combinations, demonstrating the highest AUC of 0.69 (0.63-0.74) when all three methods were combined (a significant increase from standard BI-RADS alone). Conclusion Our findings suggest that the (relative) amount of fibroglandular tissue (density) and mammographic structural features (texture/parenchymal pattern) jointly can improve risk segregation of screening women, using information already available from normal screening routine, in respect to future personalized screening strategies.