Mammographic texture resemblance generalizes as an independent risk factor for breast cancer

Research output: Contribution to journalJournal articleResearchpeer-review

INTRODUCTION:Breast density has been established as a major risk factor for breast cancer. We have previously demonstrated that mammographic texture resemblance (MTR), recognizing the local texture patterns of the mammogram, is also a risk factor for breast cancer, independent of percent breast density. We examine if these findings generalize to another population.METHODS:Texture patterns were recorded in digitalized pre-diagnosis (3.7years) film mammograms of a nested case-control study within the Dutch screening program (S1) comprising of 245 breast cancers and 250 matched controls. The patterns were recognized in the same study using cross-validation to form resemblance scores associated with breast cancer. Texture patterns from S1 were examined in an independent nested case-control study within the Mayo Mammography Health Study cohort (S2) of 226 cases and 442 matched controls: mammograms on average 8.5years prior to diagnosis, risk factor information and percent mammographic density (PD) estimated using Cumulus were available. MTR scores estimated from S1, S2 and S1+S2 (the latter two as cross-validations) were evaluated in S2. MTR scores were analyzed as both quartiles and continuously for association with breast cancer using odds ratios (OR) and adjusting for known risk factors including age, body mass index (BMI), and hormone usage.RESULTS:The mean ages of S1 and S2 were 58.0+/-5.7years and 55.2+/-10.5years, respectively. The MTR scores on S1 showed significant capability to discriminate cancers from controls (area under the operator characteristics curve (AUC)=0.63+/-0.02, P
Original languageEnglish
Article numberR37
JournalBreast Cancer Research (Online Edition)
Volume16
Issue number2
Number of pages8
ISSN1465-5411
DOIs
Publication statusPublished - 2014

Number of downloads are based on statistics from Google Scholar and www.ku.dk


No data available

ID: 109878440