Assessing breast cancer risk within the general screening population: developing a breast cancer risk model to identify higher risk women at mammographic screening

Abdolell, Mohamed; Payne, Jennifer; Caines, Judy; Tsuruda, Kaitlyn; Barnes, Penny J.; Talbot, Pam; Tong, Olivia; Brown, Peter; Rivers-Bowerman, Michael D.; Iles, Sian

doi:10.1007/s00330-020-06901-x

Cited by 10 publications

(14 citation statements)

References 29 publications

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“…A brief summary of the 406 16–54 included studies is presented in online supplemental table 3. The included studies were published from 1989 to 2021. twenty-five of the studies were conducted over the past 10 years with five studies published in 2017 especially.…”

Section: Resultsmentioning

confidence: 99%

“…The included studies were published from 1989 to 2021. twenty-five of the studies were conducted over the past 10 years with five studies published in 2017 especially. Seventeen out of the 40 studies used data from case–control studies to develop prediction models,6 17 19 23–26 29–31 39 41 43 46 49 51 54 13 from prospective cohorts,16 18 20–22 27 33–37 40 52 8 from nested case–control studies28 38 42 44 47 48 50 53 and 2 from cross-sectional study 32 45. Thirty-one studies used logistic regression to fit prediction models,6 17–19 22–26 28–32 34 38–51 53 54 seven used cox proportional hazards regression,20 21 27 33 35 36 52 one used Poisson regression16 and one used competing risk regression 37.…”

Section: Resultsmentioning

confidence: 99%

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Risk prediction models for breast cancer: a systematic review

Zheng

et al. 2022

BMJ Open

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ObjectivesTo systematically review and critically appraise published studies of risk prediction models for breast cancer in the general population without breast cancer, and provide evidence for future research in the field.DesignSystematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sourcesPubMed, the Cochrane Library and Embase were searched from inception to 16 December 2021.Eligibility criteriaWe included studies reporting multivariable models to estimate the individualised risk of developing female breast cancer among different ethnic groups. Search was limited to English language only.Data extraction and synthesisTwo reviewers independently screened, reviewed, extracted and assessed studies with discrepancies resolved through discussion or a third reviewer. Risk of bias was assessed according to the PROBAST framework.Results63 894 studies were screened and 40 studies with 47 risk prediction models were included in the review. Most of the studies used logistic regression to develop breast cancer risk prediction models for Caucasian women by case–control data. The most widely used risk factor was reproductive factors and the highest area under the curve was 0.943 (95% CI 0.919 to 0.967). All the models included in the review had high risk of bias.ConclusionsNo risk prediction models for breast cancer were recommended for different ethnic groups and models incorporating mammographic density or single-nucleotide polymorphisms among Asian women are few and poorly needed. High-quality breast cancer risk prediction models assessed by PROBAST should be developed and validated, especially among Asian women.PROSPERO registration numberCRD42020202570.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Risk prediction models for breast cancer: a systematic review

Zheng

et al. 2022

BMJ Open

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“…We identified 28 studies published since 2016 that met eligibility criteria as set out in the selection flow chart. 19-46 .…”

Section: Resultsmentioning

confidence: 99%

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Anandarajah¹,

Chen

Colditz³

et al. 2021

Preprint

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This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to prediction of future breast cancer including the time from mammogram to diagnosis of breast cancer, and methods for the identification of texture features and selection of features for inclusion in analysis. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov. were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. Of these, 7 assessed texture features from film mammograms images, 3 did not report details of the image used, and the others used full field mammograms from Hologic, GE and other manufacturers. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. Reduction in number of features chosen for analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. By following these recommendations, we expect to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

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“…4,5 However, these factors account for <5% of breast cancer prevalence in large, population based studies. 6,7 Current strategies for breast cancer risk prediction for most women rely upon models that incorporate nongenetic breast cancer risk factors such as age, race, family history, 8 estrogen-related factors, 9 body mass index (BMI), and mammographic density. 10 Incorporation of common genetic factors could vastly improve the current risk prediction models for cancer.…”

Section: Introductionmentioning

confidence: 99%

Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank

Wang

Desai

Verma

et al. 2022

Genetics in Medicine

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Genome-wide association studies have identified hundreds of single nucleotide variations (formerly single nucleotide polymorphisms) associated with several cancers, but the predictive ability of polygenic risk scores (PRSs) is unclear, especially among non-Whites. Methods: PRSs were derived from genome-wide significant single-nucleotide variations for 15 cancers in 20,079 individuals in an academic biobank. We evaluated the improvement in discriminatory accuracy by including cancer-specific PRS in patients of geneticallydetermined African and European ancestry. Results: Among the individuals of European genetic ancestry, PRSs for breast, colon, melanoma, and prostate were significantly associated with their respective cancers. Among the individuals of African genetic ancestry, PRSs for breast, colon, prostate, and thyroid were significantly associated with their respective cancers. The area under the curve of the model consisting of age, sex, and principal components was 0.621 to 0.710, and it increased by 1% to 4% with the inclusion of PRS in individuals of European genetic ancestry. In individuals of African genetic ancestry, area under the curve was overall higher in the model without the PRS (0.723-0.810) but increased by <1% with the inclusion of PRS for most cancers. Conclusion: PRS moderately increased the ability to discriminate the cancer status in individuals of European but not African ancestry. Further large-scale studies are needed to identify ancestryspecific genetic factors in non-White populations to incorporate PRS into cancer risk assessment.

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Assessing breast cancer risk within the general screening population: developing a breast cancer risk model to identify higher risk women at mammographic screening

Cited by 10 publications

References 29 publications

Risk prediction models for breast cancer: a systematic review

Risk prediction models for breast cancer: a systematic review

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank

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