Evidence map›Paper›PMID 41324402›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2026

Association between Participant Characteristics and Breast Parenchymal Texture Variation among Postmenopausal Women.

Sarah J Nyante, Yukie Kajita, Xianming Tan, Eric A Cohen, Walter C Mankowski, Despina Kontos, Cherie M Kuzmiak

Abstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sarah J NyanteDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0002-1770-5993
Yukie KajitaDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0002-1969-1871
Xianming TanLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0002-5478-2269
Eric A CohenDepartment of Radiology, Columbia University, New York, New York.ORCID 0000-0002-5067-8835
Walter C MankowskiDepartment of Radiology, Columbia University, New York, New York.ORCID 0000-0001-8591-6592
Despina KontosDepartment of Radiology, Columbia University, New York, New York.ORCID 0000-0001-9031-5126
Cherie M KuzmiakDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0002-5509-2948

Funding

Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Diana L Miglioretti · 2011 to 2026
$52.9M
North Carolina Translational and Clinical Sciences Institute (NC TraCS)UM1TR004406 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NICHOLAS J SHAHEEN · 2023 to 2026
$37.5M
Understanding the biological basis for the association between parenchymal texture features and breast cancer riskR01CA237129 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NYANTE, SARAH JANE · 2019 to 2023
$2.8M
National Institutes of Health (NIH) R01CA237129National Institutes of Health (NIH) UM1TR004406NCATS NIH HHS UM1 TR004406NCI NIH HHS P01 CA154292NCI NIH HHS R01 CA237129
6 · The paper itself

Abstract

backgroundMammographic parenchymal texture is a predictor of breast cancer risk, but knowledge of the clinical or epidemiologic factors that contribute to texture variability is limited. We evaluated the relationship between texture and breast cancer risk factors among women participating in mammography.

methodsPostmenopausal women were invited to participate between October 2020 and July 2022. A total of 305 women provided informed consent. Participant characteristics were obtained from medical records and a questionnaire. A total of 344 texture features were extracted from 2D digital screening mammograms. Data dimensionality was reduced using principal component analysis followed by clustering. Associations between texture feature clusters and participant characteristics were evaluated using χ2 or Wilcoxon tests and multinomial logistic regression. P values < 0.05 were considered statistically significant.

resultsThe 305 participants clustered into three texture feature groups. Group differences were defined by age (P < 0.01), body mass index (BMI, P < 0.01), race (P = 0.02), time since menopause (P < 0.01), breast imaging reporting and data system breast density (P < 0.01), percent breast density (P < 0.01), dense breast area (P = 0.02), and breast thickness (P < 0.01). Ethnicity, menopause type, parity, age at first birth, and family history of breast cancer did not differ between groups (all P > 0.05). In multivariable analysis, participant characteristics explained 32% of texture feature variability.

conclusionsOur findings indicate that texture features in postmenopausal women reflect, in part, factors that change over time (e.g., BMI, breast area, and thickness) and are less influenced by independent reproductive events (e.g., parity, age at first birth). IMPACT: The low proportion of variability explained suggests that texture patterns are related to unknown or underinvestigated breast cancer risk mechanisms.

Indexed as

BreastBreast NeoplasmsMammographyPostmenopauseAgedBreast DensityFemaleHumansMiddle AgedRisk Factors

Identifiers

PMID41324402
PMCPMC12851427

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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.