Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features.

ABSTRACT:

Background

We applied machine learning to find a novel breast cancer predictor based on information in a mammogram.

Methods

Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case-control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index.

Results

SUBMITTER: Schmidt DF 

PROVIDER: S-EPMC6649799 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature