Evidence map›Paper›PMID 36749434›Full record

ArticleJournal of general internal medicine2023

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman.

Jeremy S Paige, Christoph I Lee, Pin-Chieh Wang, William Hsu, Adam R Brentnall, Anne C Hoyt, Arash Naeim, Joann G Elmore

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

Article in Journal of general internal medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 3 pooled it
2.3field-weighted citation impact, top 11% 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

9 citing papers in PubMed, 3 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
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  7. Review
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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 at 3 institutions in 2 countries.

Jeremy S PaigeDepartment of Radiology, University of California, Los Angeles, CA, USA.
Christoph I LeeDepartment of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
Pin-Chieh WangDepartment of Medicine, Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, and Office of Health Informatics and Analytics, University of California, Los Angeles, Los Angeles, USA.
William HsuDepartment of Radiology, University of California, Los Angeles, CA, USA.
Adam R BrentnallCentre for Evaluation and Methods, Wolfson Institute of Population Health, Charterhouse Square, Queen Mary University of London, London, UK.
Anne C HoytDepartment of Radiology, University of California, Los Angeles, CA, USA.
Arash NaeimDivision of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Joann G ElmoreDepartment of Medicine, Division of General Internal Medicine and Health Services Research and the National Clinician Scholars Program, David Geffen School of Medicine, University of California, Los Angeles, 1100 Glendon Ave, Ste. 900, Los Angeles, CA, 90024, USA. jelmore@mednet.ucla.edu.ORCID 0000-0002-7311-6835
University of California, Los Angeles · USQueen Mary University of London · GBUniversity of Washington · US

Funding

Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical TranslationR37CA240403 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI CHRISTOPH I LEE · 2020 to 2026
$3.8M
NCI NIH HHS R37 CA240403
6 · The paper itself

Abstract

backgroundBreast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain.

objectiveTo quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer.

designComparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. SUBJECTS: Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. MAIN MEASURES: Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). KEY

resultsA total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level.

conclusionsBreast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

Indexed as

Breast NeoplasmsEarly Detection of CancerFemaleHumansMammographyQuality of LifeRisk AssessmentRisk Factorsbreast cancerchemopreventionmammographyrisk modelsscreening

Identifiers

PMID36749434
PMCPMC10465429
OpenAlexW4319333113

What OpenQuestion holds

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LicenceCC BY
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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.