Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk

Nguyen, Tuong L.; Aung, Ye Kyaw; Evans, Christopher; Dite, Gillian S.; Stone, Jennifer; MacInnis, Robert J.; Dowty, James G.; Bickerstaffe, Adrian; Aujard, Kelly; Rommens, Johanna M.; Song, Yun Mi; Sung, Joohon; Jenkins, Mark A.; Southey, Melissa C.; Giles, Graham G.; Apicella, Carmel; Hopper, John L.

doi:10.1093/ije/dyw212

Cited by 29 publications

(72 citation statements)

References 22 publications

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“…These general findings have been replicated [21]. Table 1 shows an example in which the different density measures (in absolute terms) were considered together as predictors of, on average, younger age at diagnosis breast cancer for women with a family history of breast cancer [11]. All three measures gave strong and somewhat similar risk predictions, with OPERAs of about 1.6-1.7, when considered alone (univariable analyses).…”

Section: Cirrocumulus and Altocumulusmentioning

confidence: 70%

“…The risk discrimination from conventional mammographic density compares favourably with that achieved by the latest polygenic risk scores for which the OPERA is about 1.6 [9]. Our new mammogram-based risk measures have OPERAs of 1.6 or higher [8,[10][11][12][13][14] and we will now discuss how this came about.…”

Section: Opera: a Measure Of The Ability Of A Risk Factor To Identifymentioning

confidence: 72%

“…Tables 1 and 2 show that the risk gradients for Altocumulus and/or Cirrocumulus were comparable to those for the genetic risk scores developed from genome-wide association studies (OPERA = 1.6; [9]) and often better than for Cumulus (OPERA = 1.4). The risk estimates for Altocumulus were often little different to those for Cumulus, but the risk estimates for Cirrocumulus in particular were in general stronger, especially for Korean women (who are diagnosed on average a decade or so earlier than Australian women) [10] and for Australian women diagnosed at a younger age [11]. These findings applied to film and well as digital mammograms, and for the latter, appeared to depend on the manufacturer [12].…”

Section: Redefining Mammographic Density In Terms Of Brightness: Cumumentioning

confidence: 90%

“…We found from multiple studies of Australian and Korean women [10][11][12][13][14] that the new measures of mammographic density, Altocumulus and Cirrocumulus, did better than Cumulus at predicting risk. One notable exception is risk of interval breast cancer [14].…”

Section: Redefining Mammographic Density In Terms Of Brightness: Cumumentioning

confidence: 92%

“…Odds ratio per unadjusted standard deviation (OPERA 1 ) for dense area mammographic density measures in predicting younger age at diagnosis breast cancer for women with a family history of breast cancer (Nguyen et al (2017))[11]. Odds ratio per unadjusted standard deviation (OPERA 1 ) for dense area mammographic density measures in predicting screen-detected breast cancer (Nguyen et al (2018))[13].…”

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confidence: 99%

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Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Hopper

Nguyen

Schmidt

et al. 2020

JCM

Self Cite

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This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

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Section: Cirrocumulus and Altocumulusmentioning

confidence: 70%

Section: Opera: a Measure Of The Ability Of A Risk Factor To Identifymentioning

confidence: 72%

Section: Redefining Mammographic Density In Terms Of Brightness: Cumumentioning

confidence: 90%

Section: Redefining Mammographic Density In Terms Of Brightness: Cumumentioning

confidence: 92%

mentioning

confidence: 99%

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Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Hopper

Nguyen

Schmidt

et al. 2020

JCM

Self Cite

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Blood DNA methylation profiles improve breast cancer prediction

Kresovich

O’Brien

et al. 2021

Molecular Oncology

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Although blood DNA methylation (DNAm) profiles are reported to be associated with breast cancer incidence, they have not been widely used in breast cancer risk assessment. Among a breast cancer case-cohort of 2774 women (1551 cases) in the Sister Study, we used candidate CpGs and DNAm estimators of physiologic characteristics to derive a methylationbased breast cancer risk score, mBCRS. Overall, 19 CpGs and five DNAm estimators were selected using elastic net regularization to comprise mBCRS. In a test set, higher mBCRS was positively associated with breast cancer incidence, showing similar strength to the polygenic risk score (PRS) based on 313 single nucleotide polymorphisms (313 SNPs). Area under the curve for breast cancer prediction was 0.60 for self-reported risk factors (RFs), 0.63 for PRS, and 0.63 for mBCRS. Adding mBCRS to PRS and RFs improved breast cancer prediction from 0.66 to 0.71. mBCRS findings were replicated in a nested case-control study within the EPIC-Italy cohort. These results suggest that mBCRS, a risk score derived using blood DNAm, can be used to enhance breast cancer prediction.

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Breast and bowel cancers diagnosed in people ‘too young to have cancer’: A blueprint for research using family and twin studies

Hopper,

Li,

MacInnis

et al. 2024

Genetic Epidemiology

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Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age‐specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age‐specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome‐wide association study data, and the within‐pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.

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Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk

Cited by 29 publications

References 22 publications

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Blood DNA methylation profiles improve breast cancer prediction

Breast and bowel cancers diagnosed in people ‘too young to have cancer’: A blueprint for research using family and twin studies

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