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

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• 作者：Mads Nielsen ; Celine M Vachon ; Christopher G Scott…
• 刊名：Breast Cancer Research
• 出版年：2014
• 出版时间：April 2014
• 年：2014
• 卷：16
• 期：2
• 全文大小：269 KB
• 参考文献：1. Boyd, NF, Martin, LJ, Yaffe, M, Minkin, S (2009) Mammographic density. Breast Cancer Res 11: pp. S4 <a class="external" href="http://dx.doi.org/10.1186/bcr2423" target="_blank" title="It opens in new window">CrossRefa>
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  13. Heine, JJ, Scott, CG, Sellers, TA, Brandt, KR, Serie, DJ, Wu, FF, Morton, MJ, Schueler, BA, Couch, FJ, Olson, JE, Pankratz, VS, Vachon, CM (2012) A novel automated mammographic density measure and breast cancer risk. J Natl Cancer Inst 104: pp. 1028-1037 <a class="external" href="http://dx.doi.org/10.1093/jnci/djs254" target="_blank" title="It opens in new window">CrossRefa>
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• 刊物主题：Cancer Research; Oncology;
• 出版者：BioMed Central
• ISSN：1465-5411

文摘

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 (3.7?years) 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 8.5?years 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?±-.7?years and 55.2?±-0.5?years, respectively. The MTR scores on S1 showed significant capability to discriminate cancers from controls (area under the operator characteristics curve (AUC)--.63?±-.02, P Conclusions The local texture patterns associated with breast cancer risk in S1 were also an independent risk factor in S2. Additional textures identified in S2 did not significantly improve risk segregation. Hence, the textural patterns that indicated elevated risk persisted under differences in X-ray technology, population demographics, follow-up time and geography.