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

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Authors
Nguyen, Tuong L; Aung, Ye K; Li, Shuai; Trinh, Nhut H; Evans, Christopher F; Baglietto, Laura; Krishnan, Kavitha; Dite, Gillian S; Stone, Jennifer; English, Dallas R; Song, Yun-Mi; Sung, Joohon; Jenkins, Mark A; Southey, Melissa C; Giles, Graham G; Hopper, John L
Issue Date
2018-12-13
Publisher
BioMed Central
Citation
Breast Cancer Research, 20(1):152
Keywords
Breast cancerMasking effectInterval cancerScreen-detectedNested case–control cohort studyAustralian womenMammographyMammographic density
Abstract
Background
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.
ISSN
1465-542X
Language
English
URI
http://hdl.handle.net/10371/146989
DOI
https://doi.org/10.1186/s13058-018-1081-0
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Graduate School of Public Health (보건대학원)Dept. of Public Health (보건학과)Journal Papers (저널논문_보건학과)
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