Evidence map›Paper›PMID 37289565›Full record

ArticleJNCI cancer spectrum2023

Breast density quantitative measures and breast cancer risk among screened Black women.

Mattia A Mahmoud, Sarah Ehsan, Lauren Pantalone, Walter Mankowski, Emily F Conant, Despina Kontos, Jinbo Chen, Anne Marie McCarthy

Abstract read
In one paragraph

Article in JNCI cancer spectrum, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

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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

8 authors.

Mattia A MahmoudPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Sarah EhsanDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Lauren PantaloneDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Walter MankowskiDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Emily F ConantDepartment of Radiology, Hospital of the University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Despina KontosDepartment of Radiology, Hospital of the University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Jinbo ChenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Anne Marie McCarthyDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Data and Information Integration for Risk Prediction in the Era of Big DataR01CA236468 · NCI · UNIVERSITY OF PENNSYLVANIA · PI CHEN, JINBO · 2019 to 2024
$2.2M
NCI NIH HHS R01 CA236468
6 · The paper itself

Abstract

Mammographic density is a strong predictor of breast cancer but only slightly increased the discriminatory ability of existing risk prediction models in previous studies with limited racial diversity. We assessed discrimination and calibration of models consisting of the Breast Cancer Risk Assessment Tool (BCRAT), Breast Imaging-Reporting and Data System density and quantitative density measures. Patients were followed up from the date of first screening mammogram until invasive breast cancer diagnosis or 5-year follow-up. Areas under the curve for White women stayed consistently around 0.59 for all models, whereas the area under the curve increased slightly from 0.60 to 0.62 when adding dense area and area percent density to the BCRAT model for Black women. All women saw underprediction in all models, with Black women having less underprediction. Adding quantitative density to the BCRAT did not statistically significantly improve prediction for White or Black women. Future studies should evaluate whether volumetric breast density improves risk prediction.

Indexed as

Breast NeoplasmsBreastBreast DensityFemaleHumansRisk AssessmentRisk Factors

Identifiers

PMID37289565
PMCPMC10320059

What OpenQuestion holds

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Registered trials

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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.