Evidence map›Paper›PMID 41436470›Full record

ArticleNPJ breast cancer2025

Investigating the relationship between breast cancer risk factors and an AI-generated mammographic texture feature in the Nurses' Health Study II.

Xueyao Wu, Shu Jiang, Aaron Ge, Constance Turman, Graham Colditz, Rulla M Tamimi, Peter Kraft

Abstract read
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Article in NPJ breast cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Xueyao WuDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Shu JiangWashington University School of Medicine in St. Louis, St. Louis, MO, USA.
Aaron GeUniversity of Maryland School of Medicine, Baltimore, MD, USA.
Constance TurmanProgram in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Graham ColditzWashington University School of Medicine in St. Louis, St. Louis, MO, USA.
Rulla M TamimiPopulation Health Sciences Department, Weill Cornell Medical School, New York, NY, USA.
Peter KraftDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA. phillip.kraft@nih.gov.

Funding

Life Course Cancer Epidemiology Cohort in WomenU01CA176726 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI ELIASSEN, A. HEATHER, WILLETT, WALTER C. · 2018 to 2025
$22.4M
Premonopausal Hormone Levels and Risk of Breast CancerR01CA067262 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI HANKINSON, SUSAN E · 2002 to 2011
$11.9M
NCI NIH HHS R01 CA067262NCI NIH HHS U01 CA176726NCI NIH HHS U01CA176726
6 · The paper itself

Abstract

The mammogram risk score (MRS), an AI-driven mammographic texture feature, strongly predicts breast cancer risk independently of breast density, though underlying mechanisms remain unclear. Using data from the Nurses' Health Study II (292 cases, 561 controls), we validated MRS's association with breast cancer and evaluated its relationships with established breast cancer risk factors through observational analyses, polygenic score analyses, and Mendelian randomization. MRS was significantly associated with breast cancer risk before (OR=1.92 per SD increase; 95% CI:1.57 to 2.35; 10-year AUC=0.69) and after adjustment for predicted BI-RADS density (OR=1.85; 95% CI:1.49 to 2.30). Early life body size and adult body mass index (BMI) were inversely associated with MRS, while benign breast disease history and predicted BI-RADS density showed positive associations; after adjusting for density, associations between MRS and the other three risk factors were attenuated. Polygenic score analyses and Mendelian randomization consistently demonstrated significant positive associations between genetic predictors of breast density measures (dense area, percent density, predicted BI-RADS density) and MRS. After adjusting for predicted BI-RADS density and BMI, genetic predictors of higher waist-to-hip ratio were significantly associated with increased MRS. Our findings reveal robust associations between breast density measures and MRS and suggest a potential impact of central obesity on MRS. Future larger-scale validation studies are needed.

Identifiers

PMID41436470
PMCPMC12779946

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.