. 2018 Dec 13;20(1):152.

doi: 10.1186/s13058-018-1081-0.

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

Tuong L Nguyen¹, Ye K Aung¹, Shuai Li¹, Nhut Ho Trinh¹, Christopher F Evans¹, Laura Baglietto^{1

2}, Kavitha Krishnan¹, Gillian S Dite¹, Jennifer Stone³, Dallas R English^{1

4}, Yun-Mi Song⁵, Joohon Sung^{6

7}, Mark A Jenkins¹, Melissa C Southey^{8

9}, Graham G Giles^{1

4}, John L Hopper¹⁰

Affiliations

¹ Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia.
² Department of Clinical and Experimental Medicine, University of Pisa, ᅟPisa, Italy.
³ Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Western WA, 6009, Australia.
⁴ Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.
⁵ Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro 81, Gangnamgu, Seoul, 06351, South Korea.
⁶ Department of Epidemiology School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea.
⁷ Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea.
⁸ Department of Pathology, University of Melbourne, Carlton, Victoria, 3053, Australia.
⁹ Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
¹⁰ Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia. j.hopper@unimelb.edu.au.

PMID: 30545395
PMCID: PMC6293866
DOI: 10.1186/s13058-018-1081-0

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

Tuong L Nguyen et al. Breast Cancer Res. 2018.

. 2018 Dec 13;20(1):152.

doi: 10.1186/s13058-018-1081-0.

Authors

Affiliations

¹ Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia.
² Department of Clinical and Experimental Medicine, University of Pisa, ᅟPisa, Italy.
³ Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Western WA, 6009, Australia.
⁴ Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.
⁵ Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro 81, Gangnamgu, Seoul, 06351, South Korea.
⁶ Department of Epidemiology School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea.
⁷ Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea.
⁸ Department of Pathology, University of Melbourne, Carlton, Victoria, 3053, Australia.
⁹ Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
¹⁰ Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia. j.hopper@unimelb.edu.au.

PMID: 30545395
PMCID: PMC6293866
DOI: 10.1186/s13058-018-1081-0

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.

Keywords: Australian women; Breast cancer; Interval cancer; Mammographic density; Mammography; Masking effect; Nested case–control cohort study; Screen-detected.

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Conflict of interest statement

Ethics approval and consent to participate

The study was approved by the human research ethics committees of the University of Melbourne and the Cancer Council Victoria and consent was obtained from study participants at the time of recruitment.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

**Fig. 1**
Identification of dense regions (outlined in green) on the same mammogram according to the *Cumulus* (left panel), *Altocumulus* (centre panel) and *Cirrocumulus* (right panel) definitions of mammographic density

**Fig. 2**
Log odds ratio per adjusted standard deviation (Log OPERA) estimates, 95% confidence intervals (CI) and goodness-of-fit relative to the null model, given by twice the absolute change in log likelihood (2ΔLL) for the univariable, bivariable and trivariable model fits presented in Tables 3 and 5, for interval breast cancer and percent density measures of *Cumulus*, *Altocumulus* and *Cirrocumulus*

**Fig. 3**
Log odds ratio per adjusted standard deviation (Log OPERA) estimates, 95% confidence intervals (CI) and goodness-of-fit relative to the null model, given by twice the absolute change in log likelihood (2ΔLL) for the univariable, bivariable and trivariable model fits presented in Tables 4 and 5 , for screen-detected breast cancer and density measures of *Cumulus*, *Altocumulus* and *Cirrocumulus*

See this image and copyright information in PMC

References

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Ethics approval and consent to participate

Consent for publication

Competing interests

Publisher’s Note

Figures

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Medical