Evidence map›Paper›PMID 31609476›Full record

ArticleInternational journal of cancer2020

Interval breast cancer risk associations with breast density, family history and breast tissue aging.

Tuong L Nguyen, Shuai Li, Gillian S Dite, Ye K Aung, Christopher F Evans, Ho N Trinh, Laura Baglietto, Jennifer Stone, Yun-Mi Song, Joohon Sung and 8 more

Open access · hybridAbstract read
In one paragraph

Article in International journal of cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 14% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
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  17. The Effects of Lifetime Estrogen Exposure on Breast Epigenetic Age.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021
    Article
  18. The P.I.N.K. Study Approach for Supporting Personalized Risk Assessment and Early Diagnosis of Breast Cancer.International journal of environmental research and public health · 2021
    Article
  19. Article
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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

18 authors at 7 institutions in 4 countries.

Tuong L NguyenCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.ORCID 0000-0002-6597-8363
Shuai LiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.ORCID 0000-0002-8696-8594
Gillian S DiteCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Ye K AungCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Christopher F EvansCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Ho N TrinhCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Laura BagliettoDepartment of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Jennifer StoneCentre for Genetic Origins of Health and Disease, University of Western Australia, Perth, WA, Australia.ORCID 0000-0001-5077-0124
Yun-Mi SongDepartment of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Joohon SungDepartment of Epidemiology School of Public Health, Seoul National University, Seoul, South Korea.
Dallas R EnglishCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Mark A JenkinsCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Pierre-Antoine DuguéCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Roger L MilneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Melissa C SoutheyPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
Graham G GilesCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Malcolm C PikeDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY.ORCID 0000-0003-4891-1199
John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
The University of Melbourne · AUMemorial Sloan Kettering Cancer Center · USMonash Health · AUSamsung Medical Center · KRSeoul National University · KRThe University of Western Australia · AUUniversity of Pisa · IT

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case-control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first-degree family history by questionnaire, measured body mass index (BMI) and calculated age-adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age-adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69-0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts-an increased risk of developing an interval breast cancer.

Indexed as

AdultAgedAustraliaBody Mass IndexBreast DensityBreast NeoplasmsCase-Control StudiesEstrogensFemaleHumansLogistic ModelsMammographyMedical History TakingMiddle AgedProgesteroneROC CurveEstrogensProgesteronebody mass indexbreast densityfamily historyinterval breast cancerPike's model of breast tissue aging

Identifiers

PMID31609476
PMCPMC7318124
OpenAlexW2980627945

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.