Breast cancer risk prediction: an update to the Rosner–Colditz breast cancer incidence model

Rice, Megan S.; Tworoger, Shelley S.; Hankinson, Susan E.; Tamimi, Rulla M.; Eliassen, A. Heather; Willett, Walter C.; Colditz, Graham A.; Rosner, Bernard

doi:10.1007/s10549-017-4391-5

Cited by 15 publications

(17 citation statements)

References 41 publications

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“…Recently, we updated the Rosner–Colditz model by further including early life somatotype [18,31,32]. …”

Section: Methodsmentioning

confidence: 99%

“…Multiple common genetic risk variants [ 8 ] and breast density [ 9 ], as measured on a mammogram, are additional well-confirmed breast cancer risk factors. Recent studies, though limited, have shown that including genetic risk variants (either individually or as a polygenic risk score [PRS]) and/or mammographic density (MD) significantly improves both the Gail model [ 10 – 17 ] and the Rosner–Colditz model [ 18 ]. In addition, considerable evidence supports an association of circulating estrogens, androgens, and prolactin with postmenopausal breast cancer risk [ 19 – 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case–control study

et al. 2018

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BackgroundNo prior study to our knowledge has examined the joint contribution of a polygenic risk score (PRS), mammographic density (MD), and postmenopausal endogenous hormone levels—all well-confirmed risk factors for invasive breast cancer—to existing breast cancer risk prediction models.Methods and findingsWe conducted a nested case–control study within the prospective Nurses’ Health Study and Nurses’ Health Study II including 4,006 cases and 7,874 controls ages 34–70 years up to 1 June 2010. We added a breast cancer PRS using 67 single nucleotide polymorphisms, MD, and circulating testosterone, estrone sulfate, and prolactin levels to existing risk models. We calculated area under the curve (AUC), controlling for age and stratified by menopausal status, for the 5-year absolute risk of invasive breast cancer. We estimated the population distribution of 5-year predicted risks for models with and without biomarkers. For the Gail model, the AUC improved (p-values < 0.001) from 55.9 to 64.1 (8.2 units) in premenopausal women (Gail + PRS + MD), from 55.5 to 66.0 (10.5 units) in postmenopausal women not using hormone therapy (HT) (Gail + PRS + MD + all hormones), and from 58.0 to 64.9 (6.9 units) in postmenopausal women using HT (Gail + PRS + MD + prolactin). For the Rosner–Colditz model, the corresponding AUCs improved (p-values < 0.001) by 5.7, 6.2, and 6.5 units. For estrogen-receptor-positive tumors, among postmenopausal women not using HT, the AUCs improved (p-values < 0.001) by 14.3 units for the Gail model and 7.3 units for the Rosner–Colditz model. Additionally, the percentage of 50-year-old women predicted to be at more than twice 5-year average risk (≥2.27%) was 0.2% for the Gail model alone and 6.6% for the Gail + PRS + MD + all hormones model. Limitations of our study included the limited racial/ethnic diversity of our cohort, and that general population exposure distributions were unavailable for some risk factors.ConclusionsIn this study, the addition of PRS, MD, and endogenous hormones substantially improved existing breast cancer risk prediction models. Further studies will be needed to confirm these findings and to determine whether improved risk prediction models have practical value in identifying women at higher risk who would most benefit from chemoprevention, screening, and other risk-reducing strategies.

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“…Recently, we updated the Rosner–Colditz model by further including early life somatotype [18,31,32]. …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case–control study

et al. 2018

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“…For example, the well-validated Rosner–Colditz model includes age at menarche, age at first birth, age at subsequent births, age at menopause, family history of BC, body mass index (BMI), alcohol intake, and postmenopausal hormone therapy use ( 14 ). Recent studies demonstrated that the including of genetic risk variants, mammographic density, and endogenous hormones improves the Rosner–Colditz model to predict BC risk ( 11 , 12 ).…”

mentioning

confidence: 99%

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Vellal

Sirinukunwattan

Kensler

et al. 2021

JNCI Cancer Spectrum

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Background New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses’ Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all P < .05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, Ptrend = .047). No morphometric signature was associated with BC. Conclusions The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.

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“…In the BCSC cohort used to fit their model [61] this pattern was not observed for proliferative disease, and for non-proliferative disease the direction was even reversed (a larger effect for women older than 50y). In the Rosner-Colditz model there is a positive interaction with age at menarche and a negative interaction for the other terms (albeit none are univariately significant in the most recent model at a 5% level [51]), suggesting an older age at menarche is not protective for women with benign disease. These three models have been fitted in populations with individual-level data.…”

Section: Regression Function Modelsmentioning

confidence: 90%

Risk Models for Breast Cancer and Their Validation

Brentnall¹,

Cuzick²

2020

Statist. Sci.

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Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer-Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132 139 women attending mammography screening in Washington, USA.

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Breast cancer risk prediction: an update to the Rosner–Colditz breast cancer incidence model

Cited by 15 publications

References 41 publications

Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case–control study

Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case–control study

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Risk Models for Breast Cancer and Their Validation

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