Evidence map›Paper›PMID 37428020›Full record

ArticleCancer prevention research (Philadelphia, Pa.)2023

Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.

Simin Chen, Rulla M Tamimi, Graham A Colditz, Shu Jiang

Abstract read
In one paragraph

Article in Cancer prevention research (Philadelphia, Pa.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Breast Density Changes after Risk-Reducing Salpingo-oophorectomy in Women with a Pathogenic Germline Variant in BRCA1 or BRCA2.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
    Article
  2. Article
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

4 authors.

Simin ChenDivision of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri.ORCID 0009-0006-2807-0109
Rulla M TamimiDepartment of Population Health Sciences, Weill Cornell Medicine, New York, New York.ORCID 0000-0003-2306-8668
Graham A ColditzDivision of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri.ORCID 0000-0002-7307-0291
Shu JiangDivision of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri.ORCID 0000-0003-1464-4838

Funding

Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN · 2001 to 2026
$128.0M
Dynamic prediction incorporating time-varying covariates for the onset of breast cancerR37CA256810 · NCI · WASHINGTON UNIVERSITY · PI JIANG, SHU · 2021 to 2025
$2.4M
NCI NIH HHS P30 CA091842NCI NIH HHS R37 CA256810
6 · The paper itself

Abstract

Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval. PREVENTION RELEVANCE: Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care.

Indexed as

Breast NeoplasmsBreastBreast DensityFemaleHumansMammographyRisk Factors

Identifiers

PMID37428020
PMCPMC10472097

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.