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

Tuong L. Nguyen, Ye K. Aung, Shuai Li, Nhut Ho Trinh, Christopher F. Evans, Laura Baglietto, Kavitha Krishnan, Gillian S. Dite, Jennifer Stone, Dallas R. English, Yun Mi Song, Joohon Sung, Mark A. Jenkins, Melissa C. Southey, Graham G. Giles, John L. Hopper

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article number152
Number of pages11
JournalBreast Cancer Research
Volume20
Issue number1
DOIs
Publication statusPublished - 13 Dec 2018

Keywords

  • Australian women
  • Breast cancer
  • Interval cancer
  • Mammographic density
  • Mammography
  • Masking effect
  • Nested case-control cohort study
  • Screen-detected

Cite this

Nguyen, Tuong L. ; Aung, Ye K. ; Li, Shuai ; Trinh, Nhut Ho ; 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. / Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds. In: Breast Cancer Research. 2018 ; Vol. 20, No. 1.
@article{cd91088aede9412faa30dac9ea41b781,
title = "Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds",
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",
author = "Nguyen, {Tuong L.} and Aung, {Ye K.} and Shuai Li and Trinh, {Nhut Ho} and Evans, {Christopher F.} and Laura Baglietto and Kavitha Krishnan and Dite, {Gillian S.} and Jennifer Stone and English, {Dallas R.} and Song, {Yun Mi} and Joohon Sung and Jenkins, {Mark A.} and Southey, {Melissa C.} and Giles, {Graham G.} and Hopper, {John L.}",
year = "2018",
month = "12",
day = "13",
doi = "10.1186/s13058-018-1081-0",
language = "English",
volume = "20",
journal = "Breast Cancer Research",
issn = "1465-5411",
publisher = "Springer-Verlag London Ltd.",
number = "1",

}

Nguyen, TL, Aung, YK, Li, S, Trinh, NH, Evans, CF, Baglietto, L, Krishnan, K, Dite, GS, Stone, J, English, DR, Song, YM, Sung, J, Jenkins, MA, Southey, MC, Giles, GG & Hopper, JL 2018, 'Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds', Breast Cancer Research, vol. 20, no. 1, 152. https://doi.org/10.1186/s13058-018-1081-0

Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds. / Nguyen, Tuong L.; Aung, Ye K.; Li, Shuai; Trinh, Nhut Ho; 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.

In: Breast Cancer Research, Vol. 20, No. 1, 152, 13.12.2018.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds

AU - Nguyen, Tuong L.

AU - Aung, Ye K.

AU - Li, Shuai

AU - Trinh, Nhut Ho

AU - Evans, Christopher F.

AU - Baglietto, Laura

AU - Krishnan, Kavitha

AU - Dite, Gillian S.

AU - Stone, Jennifer

AU - English, Dallas R.

AU - Song, Yun Mi

AU - Sung, Joohon

AU - Jenkins, Mark A.

AU - Southey, Melissa C.

AU - Giles, Graham G.

AU - Hopper, John L.

PY - 2018/12/13

Y1 - 2018/12/13

N2 - 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.

AB - 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.

KW - Australian women

KW - Breast cancer

KW - Interval cancer

KW - Mammographic density

KW - Mammography

KW - Masking effect

KW - Nested case-control cohort study

KW - Screen-detected

UR - http://www.scopus.com/inward/record.url?scp=85058549923&partnerID=8YFLogxK

U2 - 10.1186/s13058-018-1081-0

DO - 10.1186/s13058-018-1081-0

M3 - Article

VL - 20

JO - Breast Cancer Research

JF - Breast Cancer Research

SN - 1465-5411

IS - 1

M1 - 152

ER -