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Cited by 252Digital Radiography and Breast ImagingGlobal Cancer Incidence and ScreeningAI in cancer detection

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort

Open access
Brentnall, Adam R., Harkness, Elaine F., Astley, Susan M., Donnelly, Louise S., Stavrinos, Paula, Sampson, Sarah, Fox, Lynne, Sergeant, Jamie C., Harvie, Michelle N., Wilson, Mary, Beetles, Ursula, Gadde, Soujanya, Lim, Yit, Jain, Anil, Bundred, Sara, Barr, Nicola, Reece, Valerie, Howell, Anthony, Cuzick, Jack, Evans, D. Gareth R.·2015·Breast Cancer Research
DOI 10.1186/s13058-015-0653-5PMC PMC4665886
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Abstract

INTRODUCTION: The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model (International Breast Intervention Study model). METHODS: Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression. RESULTS: The analysis included 50,628 women aged 47-73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density [interquartile range odds ratio (IQR-OR) 1.48, 95 % CI 1.34-1.63, AUC 0.59] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [IQR-OR 1.36 (95 % CI 1.25-1.48), O/E 60 % (95 % CI 44-74), AUC 0.57] or the Gail model [IQR-OR 1.22 (95 % CI 1.12-1.33), O/E 46 % (95 % CI 26-65 %), AUC 0.55]. It continued to add information after allowing for Tyrer-Cuzick [IQR-OR 1.47 (95 % CI 1.33-1.62), combined AUC 0.61] or Gail [IQR-OR 1.45 (95 % CI 1.32-1.60), combined AUC 0.59]. CONCLUSIONS: Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model.

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  • The benefit of using mammographic breast density in risk models is relatively small.

    “Despite its widespread adoption, the benefit of using mammographic breast density in risk models is relatively small.”

    From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [101]

    The Results (Analysis and presentation; page unavailable) say mammographic density added “substantial significant information” to both models, rather than a relatively small benefit. They report a 0.04 AUC increase for each model and more accurate identification of high- and low-risk women. (LLM, conf=0.88)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • Most existing traditional risk models were developed on predominantly White populations and have known limitations in predicting risk for other racial groups.

    “Most existing traditional risk models were developed on predominantly White populations101,107 and have known limitations in predicting risk for other racial groups.108–111”

    From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [101]

    The citation is attached to the claim about where traditional risk models were developed, not the separate claim about their performance in other racial groups. The Introduction describes the origins of the Gail and Tyrer-Cuzick models, and the References identify the original Gail model as concerning “white females.” The paper does not establish that most traditional risk models were developed in predominantly White populations. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Metadata

Authors
Brentnall, A. R., Harkness, E. F., Astley, S. M., Donnelly, L. S., Stavrinos, P., Sampson, S., Fox, L., Sergeant, J. C., Harvie, M. N., Wilson, M., Beetles, U., Gadde, S., Lim, Y., Jain, A., Bundred, S., Barr, N., Reece, V., Howell, A., Cuzick, J., & Evans, D. G. R.
Affiliations
  • Queen Mary University of London (GB)
  • Manchester Academic Health Science Centre (GB)
  • University of Manchester (GB)
  • The Christie Hospital (GB)
Corresponding author
Brentnall, Adam R.
ORCID
Venue
Breast Cancer Research
Publisher
Springer Science and Business Media LLC
Published
2015-12
Volume
17
Issue
1
Pages
147-147
ISSN
  • 1465-5411 (ISSN-L)
  • 1465-542X
DOI
10.1186/s13058-015-0653-5
PMC
PMC4665886
OpenAlex
W2182350758
Cited by
252
References
1
Citations by year
2026: 15 · 2025: 18 · 2024: 25 · 2023: 40 · 2022: 23 · 2021: 18 · 2020: 31 · 2019: 33 · 2018: 26 · 2017: 16 · 2016: 6
FWCI
12.67
Type
journal article
Language
en
Open access
Licence
Locations
  • Breast Cancer Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Explorer (The University of Manchester) (publishedVersion)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
Funders
  • National Institute for Health Research (GB) (RP-PG-0707-10031)
  • Genesis Breast Cancer Prevention Appeal (GA10-033, GA13-006)
  • Medical Research Council
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Digital Radiography and Breast Imaging
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
breast density, breast cancer risk prediction, Tyrer-Cuzick model, Gail model, risk model calibration, breast cancer screening, risk prediction accuracy, breast cancer incidence, risk prediction model validation
MeSH terms
Breast Density, Aged, Breast Neoplasms, Female, United Kingdom, Humans, Middle Aged, Prospective Studies, Radiography, ROC Curve, Reproducibility of Results, Risk Assessment, Mammary Glands, Human, Quality Improvement

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