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Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds

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dc.contributor.authorNguyen, Tuong L-
dc.contributor.authorAung, Ye K-
dc.contributor.authorLi, Shuai-
dc.contributor.authorTrinh, Nhut H-
dc.contributor.authorEvans, Christopher F-
dc.contributor.authorBaglietto, Laura-
dc.contributor.authorKrishnan, Kavitha-
dc.contributor.authorDite, Gillian S-
dc.contributor.authorStone, Jennifer-
dc.contributor.authorEnglish, Dallas R-
dc.contributor.authorSong, Yun-Mi-
dc.contributor.authorSung, Joohon-
dc.contributor.authorJenkins, Mark A-
dc.contributor.authorSouthey, Melissa C-
dc.contributor.authorGiles, Graham G-
dc.contributor.authorHopper, John L-
dc.date.accessioned2019-03-12T05:14:00Z-
dc.date.available2019-03-12T14:15:11Z-
dc.date.issued2018-12-13-
dc.identifier.citationBreast Cancer Research, 20(1):152ko_KR
dc.identifier.issn1465-542X-
dc.identifier.urihttp://hdl.handle.net/10371/146989-
dc.description.abstractBackground
Case–control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers.

Method
We conducted a nested case–control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (ΔBIC).

Results
For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA = 2.33 (95% confidence interval (CI) 1.85–2.92); all ΔBIC > 14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P > 0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ΔBIC > 6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P > 0.07).

Conclusion
The amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes.
ko_KR
dc.description.sponsorshipThis work was supported by the National Health and Medical Research Council (grant numbers 251533, 209057 and 504711), the Victorian Health Promotion Foundation, Cancer Council Victoria, Cancer Council NSW, Cancer Australia and the National Breast Cancer Foundation. TLN has been supported by a NHMRC Post-Graduate Scholarship, The Richard Lowell Travelling Scholarship, The University of Melbourne, VCCC Picchi Award for Excellence in Cancer Research and a Cancer Council Victoria Post-doctoral Fellowship. YKA has been supported by the Australian Agency for International Development (AusAID). SL is supported by the Australian Postgraduate Award, International Postgraduate Research Scholarship and The Richard Lowell Travelling Scholarship from The University of Melbourne. JS has been supported by the National Breast Cancer Foundation Post-doctoral Training Fellowship. JLH is a NHMRC Senior Principal Research Fellow and a Distinguished Visiting Professor at Seoul National University. MCS and MAJ are NHMRC Senior Research Fellows.ko_KR
dc.language.isoenko_KR
dc.publisherBioMed Centralko_KR
dc.subjectBreast cancerko_KR
dc.subjectMasking effectko_KR
dc.subjectInterval cancerko_KR
dc.subjectScreen-detectedko_KR
dc.subjectNested case–control cohort studyko_KR
dc.subjectAustralian womenko_KR
dc.subjectMammographyko_KR
dc.subjectMammographic densityko_KR
dc.titlePredicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholdsko_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor송윤미-
dc.identifier.doi10.1186/s13058-018-1081-0-
dc.language.rfc3066en-
dc.rights.holderThe Author(s).-
dc.date.updated2018-12-16T04:15:17Z-
Appears in Collections:
Graduate School of Public Health (보건대학원)Dept. of Public Health (보건학과)Journal Papers (저널논문_보건학과)
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