Evidence map›Paper›PMID 40075657›Full record

ArticleCancers2025

Risk Models to Predict Screen-Detected and Interval Breast Cancers in Population Mammography Screening Participants.

Naomi Noguchi, Armando Teixeira-Pinto, Michael Luke Marinovich, Dominique Claire Louw, Elizabeth Jane Wylie, Nehmat Houssami

Abstract read
In one paragraph

Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Naomi NoguchiSydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia.ORCID 0000-0001-5662-1496
Armando Teixeira-PintoSydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia.
Michael Luke MarinovichThe Daffodil Centre, University of Sydney (a Joint Venture with Cancer Council NSW), Camperdown, NSW 2050, Australia.ORCID 0000-0002-3801-8180
Dominique Claire LouwSydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia.
Elizabeth Jane WylieBreastScreen Western Australia, Perth, WA 6000, Australia.
Nehmat HoussamiSydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia.ORCID 0000-0002-3641-952X

Funding

national breast cancer foundation EC-21-001National Health and Medical Research Council 1194410
6 · The paper itself

Abstract

aimThe aim of this study was to determine whether women at risk of having screen-detected (including detected at advanced stage) and interval breast cancer can be accurately identified using conventional risk factors collected by national screening programs.

methodsAll 1,026,137 mammography screening examinations for 323,082 women attending the BreastScreen Western Australia program (part of Australia's national biennial screening program) in July 2007-June 2017 contributed to models for predicting screen-detected breast cancers, screen-detected advanced cancers (≥pT2), and interval cancers.

resultsIn total, 7024 screen-detected (1551 in situ, 5472 invasive, of which 1329 were ≥pT2) and 1866 interval cancers (76 in situ, 1790 invasive) were diagnosed. In a multivariable model for screen-detected cancers, the ORs for the oldest age groups were 2.56 (CI 2.32-2.82) for 60-69 years and 3.60 (CI 3.23-4.00) for ≥70 years, and the OR for symptoms was 7.44 (CI 6.76-8.20). These associations were stronger for screen-detected advanced cancers. First-degree family history and a personal history of breast cancer were also associated with risk. In a multivariable model for interval cancers, the HR for dense breasts was 2.36 (CI 2.14-2.61) and the HR for symptoms was 3.27 (CI 2.53-4.24); family history and recent hormone replacement therapy use were also associated with risk. The areas under the receiver operating characteristic curves were 0.643 (CI 0.636-0.650) for screen-detected cancers, 0.651 (CI 0.638-0.664) for screen-detected advanced cancers, and 0.706 (CI 0.690-0.722) for interval cancers.

conclusionOlder age and symptoms were the strongest predictors of overall and advanced screen-detected breast cancers. Dense breasts and symptoms were the strongest predictors of interval cancers. All models had moderate discrimination, approximating that for established models.

Indexed as

breast neoplasmsearly detection of cancerinterval cancermammographymass screeningrisk factors

Identifiers

PMID40075657
PMCPMC11898880

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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.