Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Malkov, Serghei; Shepherd, John; Scott, Christopher; Tamimi, Rulla; Ma, Lin; Bertrand, Kimberly; Couch, Fergus; Jensen, Matthew; Mahmoudzadeh, Amir; Fan, Bo; Norman, Aaron; Brandt, Kathleen; Pankratz, V.; Vachon, Celine; Kerlikowske, Karla
• Is Part Of: Breast cancer research. Volume 18:Issue 1(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-12
• Subjects: Breast -- Cancer -- Periodicals; 616.99449
• DOI: 10.1186/s13058-016-0778-1
• Languages: English
• ISSNs: 1465-542X

Abstract

Abstract Background Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density. Methods This study combines five case–control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study. Results Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH10 andFD_TH15) were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH60 toFD_TH85) were associated with a decreased risk. Increasing theFD_TH75 andEnergy feature values were associated with a decreased risk of breast cancer while increasingEntropy was associated with a decreased risk of breast cancer. For example, 1 standard deviation increase of FD_TH75 was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79–0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar. Conclusion Mammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images.