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Record W4409616278 · doi:10.1101/2025.04.17.25325994

Mammographic density, pathogenic breast cancer susceptibility gene variants and breast cancer risk

2025· preprint· en· W4409616278 on OpenAlexafffund
Xiaomeng Zhang, Mikael Eriksson, Nasim Mavaddat, Joe Dennis, Susan Astley, Marike Gabrielson, Graham G. Giles, Steven N. Hart, David J. Hunter, Loı̈c Le Marchand, Michael Lush, Kyriaki Michailidou, Christopher G. Scott, Qin Wang, Sacha J. Howell, Marc Naven, Antonis C. Antoniou, Kristan J. Aronson, Manjeet K. Bolla, Jose E. Castelao, Fergus J. Couch, Kamila Czene, Alison M. Dunning, D. Gareth Evans, Manuela Gago-Domínguez, Christopher A. Haiman, Roger L. Milne, Paul D.P. Pharoah, Melissa C. Southey, Jennifer Stone, Rachel A. Murphy, Amy Berrington de González, Celine M. Vachon, Per Hall, Douglas F. Easton

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsBC Cancer AgencyQueen's University
FundersNIHR Cambridge Biomedical Research CentreEuropean CommissionDepartment of Health and Social CareNational Cancer InstituteNational Institutes of HealthCancer Research UKGovernment of CanadaFondation du cancer du sein du QuébecCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchGenome Canada
KeywordsBreast cancerMAMMOGRAPHIC DENSITYOncologyGeneCancerMedicineInternal medicineBiologyGeneticsMammography

Abstract

fetched live from OpenAlex

Abstract Importance Mammographic density (MD) and pathogenic variants (PVs) in breast cancer susceptibility genes are major determinants of breast cancer risk, but their association and joint effects on breast cancer risk are unclear. Objective To investigate the association between the presence or absence of PVs in breast cancer susceptibility genes and MD measures, and their joint effects on breast cancer risk in an observational study; and to evaluate causality using Mendelian randomisation (MR) analyses. Design Case-control analyses using data from the Breast Cancer Association Consortium (1991-2016). Sequencing and genotyping took place between 2009 and 2021. Setting Multicenter Participants A total of 6,809 cases and 18,189 controls were included, from 15 studies, comprising women aged 19 to 92 years with mammograms taken at least one year before diagnosis. Exposure MD measures, including dense area (DA), non-dense area (NDA), percentage density (PD) and absolute difference in PD between left and right breasts (ADPD), and PVs in ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C and RAD51D . Main outcomes and measures Breast cancer risk overall, by oestrogen receptor expression-defined subtypes, and among BRCA1 and BRCA2 PV carriers. Results No association was found between the overall burden of PVs and any MD measure. There was some evidence for a negative interaction between the burden of PVs in the eight genes and PD (OR=0.79,95%CI int =0.62,1.00, P LRT =0.047). This appears to be largely driven by a positive interaction with NDA. MR analyses indicated attenuated effects for BRCA1 (for PD, OR per standard deviation =1.02(95%CI:0.78,1.34) but not BRCA2 PV carriers (1.54,95%CI=1.08,2.24)). Conclusions and Relevance There was no evidence of association between PVs in breast cancer susceptibility genes and MD measures, but some suggestion that the association between MD and breast cancer risk may be weaker in PV carriers. Replication of these findings in further large datasets is required.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.288
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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