Journal article

Novel mammogram-based measures improve breast cancer risk prediction beyond an established measure of mammographic density

Tuong Nguyen, Daniel Schmidt, Enes Makalic, Gertraud Maskarinec, Shuai Li, Gillian Dite, Ye Aung, Christopher Evans, Ho Trinh, Laura Baglietto, Jennifer Stone, Yun-Mi Song, Joohon Sung, Robert MacInnis, Pierre-Antoine Dugué, James Dowty, Mark Jenkins, Roger Milne, Melissa Southey, Graham Giles Show all

Published : 2020

Abstract

ABSTRACT Background Mammograms contain information that predicts breast cancer risk. We recently discovered two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus) . It is not known whether these measures improve risk prediction when fitted together, and with an established measure of mammographic density (Cumulus). Methods We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1,197 matched controls; and 354 younger-diagnosis cases and 944 frequency-matched controls. We conducted conditional and unconditional logistic regression analyses of individually-and frequency-matched ..

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