Evidence map›Paper›PMID 34843026›Full record

ArticleBreast cancer research and treatment2022

A case-case analysis of women with breast cancer: predictors of interval vs screen-detected cancer.

Nickolas Dreher, Madeline Matthys, Edward Hadeler, Yiwey Shieh, Irene Acerbi, Fiona M McAuley, Michelle Melisko, Martin Eklund, Jeffrey A Tice, Laura J Esserman and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research and treatment, 2022. 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, top 79% of its field
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 citations in OpenAlex.

  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

11 authors at 4 institutions in 2 countries.

Nickolas DreherUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA. Nickolas.Dreher@icahn.mssm.edu.ORCID http://orcid.org/0000-0002-0229-8897
Madeline MatthysUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
Edward HadelerUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
Yiwey ShiehUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
Irene AcerbiUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
Fiona M McAuleyUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
Michelle MeliskoUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
Martin EklundKarolinska Institutet, Stockholm, Sweden.
Jeffrey A TiceUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
Laura J EssermanUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
Laura J Van't VeerUniversity of California San Francisco, 2340 Sutter Street, N415, San Francisco, CA, 94143-0808, USA.
University of California, San Francisco · USKarolinska Institutet · SESutter Health · USUniversity of Miami · US

Funding

Elucidating the molecular and contextual basis for IDLE ultralow risk lesions and the tumor immune microenvironment of high risk in situ and invasive breast cancersU01CA196406 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BOROWSKY, ALEXANDER D, ESSERMAN, LAURA J · 2015 to 2020
$5.8M
Prediction of interval breast cancer risk using genetic variantsK08CA237829 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI SHIEH, YIWEY · 2019 to 2023
$1.3M
NCI NIH HHS 1K08CA237829NCI NIH HHS HHSN261201100031CNCI NIH HHS K08 CA237829NCI NIH HHS U01 CA196406NCI NIH HHS U01CA196406
6 · The paper itself

Abstract

purposeThe Breast Cancer Surveillance Consortium (BCSC) model is a widely used risk model that predicts 5- and 10-year risk of developing invasive breast cancer for healthy women aged 35-74 years. Women with high BCSC risk may also be at elevated risk to develop interval cancers, which present symptomatically in the year following a normal screening mammogram. We examined the association between high BCSC risk (defined as the top 2.5% by age) and breast cancers presenting as interval cancers.

methodsWe conducted a case-case analysis among women with breast cancer in which we compared the mode of detection and tumor characteristics of patients in the top 2.5% BCSC risk by age with age-matched (1:2) patients in the lower 97.5% risk. We constructed logistic regression models to estimate the odds ratio (OR) of presenting with interval cancers, and poor prognosis tumor features, between women from the top 2.5% and bottom 97.5% of BCSC risk.

resultsOur analysis included 113 breast cancer patients in the top 2.5% of risk for their age and 226 breast cancer patients in the lower 97.5% of risk. High-risk patients were more likely to have presented with an interval cancer within one year of a normal screening, OR 6.62 (95% CI 3.28-13.4, p < 0.001). These interval cancers were also more likely to be larger, node positive, and higher stage than the screen-detected cancers.

conclusionBreast cancer patients in the top 2.5% of BCSC risk for their age were more likely to present with interval cancers. The BCSC model could be used to identify healthy women who may benefit from intensified screening.

Indexed as

Breast NeoplasmsAdultAgedEarly Detection of CancerFemaleHumansMammographyMass ScreeningMiddle AgedOdds RatioBreast cancerBreast densityInterval cancerScreeningSupplemental screening

Identifiers

PMID34843026
PMCPMC9189918
OpenAlexW3214832263

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

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.