Evidence map›Paper›PMID 30594212›Full record

ArticleBreast cancer research : BCR2018

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk.

Sue Hudson, Kirsti Vik Hjerkind, Sarah Vinnicombe, Steve Allen, Cassia Trewin, Giske Ursin, Isabel Dos-Santos-Silva, Bianca L De Stavola

Abstract read
In one paragraph

Article in Breast cancer research : BCR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Mammographic Density in Relation to Breast Cancer Risk Factors among Chinese Women.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
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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.

Sue HudsonDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. susan.hudson@lshtm.ac.uk.ORCID 0000-0003-3872-8649
Kirsti Vik HjerkindCancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway.
Sarah VinnicombeDivision of Imaging and Technology, Ninewells Hospital Medical School, University of Dundee, Dundee, DD2 1SY, UK.
Steve AllenRoyal Marsden NHS Foundation Trust, London, SW3 6JJ, UK.
Cassia TrewinCancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway.
Giske UrsinCancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway.
Isabel Dos-Santos-SilvaDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Bianca L De StavolaFaculty of Population Health Sciences, Institute of Child Health, University College London, London, WC1N 1EH, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD-risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable.

methodsData from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I

resultsBMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I

conclusionsWhen volumetric MD-breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable.

Indexed as

Body Mass IndexBreast DensityAdiposityAgedAged, 80 and overBreastBreast NeoplasmsCase-Control StudiesCohort StudiesFeasibility StudiesFemaleHumansImage Processing, Computer-AssistedLogistic ModelsMammographyMass ScreeningBMIBreast cancerBreast densityMammographic densityOPERA

Identifiers

PMID30594212
PMCPMC6311032

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Registered trials

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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.