Evidence map›Paper›PMID 35026019›Full record

ArticleJournal of the National Cancer Institute2022

Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population.

Karla Kerlikowske, Shuai Chen, Marzieh K Golmakani, Brian L Sprague, Jeffrey A Tice, Anna N A Tosteson, Garth H Rauscher, Louise M Henderson, Diana S M Buist, Janie M Lee and 2 more

Registry-linked trialOpen access · greenAbstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06629896 (Comparison Between Contrast-Enhanced Mammography and Magnetic Resonance Imaging in the Surveillance of High-Risk Women for Breast Cancer), which is not on this map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 pooled it
2.6field-weighted citation impact, top 8% 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.

NCT06629896 narecruitingnot on this mapstarted 2025, after this paper: background citation

Comparison Between Contrast-Enhanced Mammography and Magnetic Resonance Imaging in the Surveillance of High-Risk Women for Breast Cancer: The Randomized Controlled Trial 'Con-trust. Funded by European Commission NextGenerationEU - Ministero Della Salute PNRR: M6/C2_CALL 2023 Full Proposal

TypeinterventionalSponsorAzienda USL Reggio Emilia - IRCCSRan2025 to 2030Enrolled2,200ConditionsBreast Cancer Screening and DiagnosisArmsContrast Enhancement Mammography, magnetic resonance imaging (MRI) and digital mammography (DM)
3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

  1. Pooled it
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  4. Advancing Evidence of the Associations between specific Benign Breast Diagnoses and Future Breast Cancer Risk.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
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  5. Performance of Statistical and Machine Learning Risk Prediction Models for Advanced Breast Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 11 institutions in 1 country.

Karla KerlikowskeDepartment of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.ORCID 0000-0001-8793-8779
Shuai ChenDepartment of Public Health Sciences, University of California, Davis, CA, USA.ORCID 0000-0002-4016-8217
Marzieh K GolmakaniPfizer Inc, San Diego, CA, USA.ORCID 0000-0002-2111-0773
Brian L SpragueDepartment of Surgery and Radiology, University of Vermont, Burlington, VT, USA.
Jeffrey A TiceDepartment of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.ORCID 0000-0002-9857-2028
Anna N A TostesonThe Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.ORCID 0000-0001-7718-8943
Garth H RauscherSchool of Public Health, Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, USA.
Louise M HendersonDepartment of Radiology, University of North Carolina, Chapel Hill, NC, USA.
Diana S M BuistKaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA.ORCID 0000-0001-5408-2804
Janie M LeeDepartment of Radiology, University of Washington, and Seattle Cancer Care Alliance, Seattle, WA, USA.ORCID 0000-0002-6452-8168
Charlotte C GardDepartment of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM, USA.ORCID 0000-0001-7201-4450
Diana L MigliorettiDepartment of Public Health Sciences, University of California, Davis, CA, USA.ORCID 0000-0002-5547-1833
Kaiser Permanente Washington Health Research Institute · USDartmouth College · USNew Mexico State University · USPfizer (United States) · USSan Francisco VA Medical Center · USSeattle Cancer Care Alliance · USUniversity of California, Davis · USUniversity of California, San Francisco · USUniversity of Illinois Chicago · USUniversity of North Carolina at Chapel Hill · USUniversity of Vermont · US

Funding

Staff InvestigatorsP30CA093373 · NCI · UNIVERSITY OF CALIFORNIA DAVIS · PI Gina M Dayton · 2002 to 2026
$84.9M
Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KARLA M KERLIKOWSKE · 2011 to 2026
$52.9M
Vermont Screening Process Documentation UnitU54CA163303 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI SPRAGUE, BRIAN L · 2011 to 2017
$6.4M
Comparative effectiveness of breast imaging modalities: a natural experimentR01HS018366 · AHRQ · UNIVERSITY OF ILLINOIS AT CHICAGO · PI RAUSCHER, GARTH H · 2010 to 2014
$1.8M
AHRQ HHS R01 HS018366NCI NIH HHS P01 CA154292NCI NIH HHS U54 CA163303
6 · The paper itself

Abstract

backgroundEstimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval.

methodsWe included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (<0.380%).

resultsObesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women's predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval.

conclusionMost women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.

Indexed as

Breast NeoplasmsMammographyBreast DensityEarly Detection of CancerFemaleHumansMass ScreeningTime Factors

Identifiers

PMID35026019
PMCPMC9086807
OpenAlexW4206768782

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.