1. Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds
- Author
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Ye Kyaw Aung, Graham G. Giles, Jennifer Stone, Kavitha Krishnan, Christopher F. Evans, Mark A. Jenkins, Yun Mi Song, Tuong L. Nguyen, Laura Baglietto, Nhut Ho Trinh, Shuai Li, Gillian S. Dite, Joohon Sung, John L. Hopper, Melissa C. Southey, Dallas R. English, Hopper, John L [0000-0002-8567-173X], and Apollo - University of Cambridge Repository
- Subjects
medicine.medical_specialty ,Breast Neoplasms ,lcsh:RC254-282 ,Risk Assessment ,03 medical and health sciences ,Interval cancer ,0302 clinical medicine ,Breast cancer ,Nested case–control cohort study ,Risk Factors ,Image Processing, Computer-Assisted ,Medicine ,Mammography ,Humans ,030212 general & internal medicine ,Breast ,Prospective Studies ,Risk factor ,10. No inequality ,Prospective cohort study ,Mammographic density ,Early Detection of Cancer ,Aged ,Breast Density ,2. Zero hunger ,Receiver operating characteristic ,medicine.diagnostic_test ,business.industry ,Obstetrics ,Australian women ,Masking effect ,Screen-detected ,Middle Aged ,medicine.disease ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Prognosis ,Confidence interval ,3. Good health ,030220 oncology & carcinogenesis ,Case-Control Studies ,Female ,business ,Risk assessment ,Body mass index ,Software ,Research Article - 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. Electronic supplementary material The online version of this article (10.1186/s13058-018-1081-0) contains supplementary material, which is available to authorized users.
- Published
- 2018