Evidence map›Paper›PMID 31015834›Full record

ArticleJournal of oncology2019

Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement.

Tong Li, Lichen Tang, Ziba Gandomkar, Rob Heard, Claudia Mello-Thoms, Qin Xiao, Yajia Gu, Genhong Di, Carolyn Nickson, Zhimin Shao and 1 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

11 authors at 3 institutions in 2 countries.

Tong LiMedical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East Street, Lidcombe, NSW, Australia.ORCID 0000-0003-4956-765X
Lichen TangDepartment of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, China.
Ziba GandomkarMedical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East Street, Lidcombe, NSW, Australia.
Rob HeardBehaviour and Social Sciences, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East Street, Lidcombe, NSW, Australia.
Claudia Mello-ThomsMedical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East Street, Lidcombe, NSW, Australia.
Qin XiaoDepartment of Radiology, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, China.
Yajia GuDepartment of Radiology, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, China.
Genhong DiDepartment of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, China.
Carolyn NicksonCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, 207 Bouverie Street, Melbourne, VIC 3010, Australia.
Zhimin ShaoDepartment of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, China.ORCID 0000-0001-7633-0151
Patrick BrennanMedical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East Street, Lidcombe, NSW, Australia.
Fudan University Shanghai Cancer Center · CNThe University of Sydney · AUThe University of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCharacteristics of mammographic density for Chinese women are understudied. This study aims to identify factors associated with mammographic density in China using a quantitative method.

methodsMammographic density was measured for a total of 1071 (84 with and 987 without breast cancer) women using an automatic algorithm AutoDensity. Pearson tests examined relationships between density and continuous variables and t-tests compared differences of mean density values between groupings of categorical variables. Linear models were built using multiple regression.

resultsPercentage density and dense area were positively associated with each other for cancer-free (r=0.487, p<0.001) and cancer groups (r=0.446, p<0.001), respectively. For women without breast cancer, weight and BMI (p<0.001) were found to be negatively associated (r=-0.237, r=-0.272) with percentage density whereas they were found to be positively associated (r=0.110, r=0.099) with dense area; age at mammography was found to be associated with percentage density (r=-0.202, p<0.001) and dense area (r=-0.086, p<0.001) but did not add any prediction within multivariate models; lower percentage density was found within women with secondary education background or below compared to women with tertiary education. For women with breast cancer, percentage density demonstrated similar relationships with that of cancer-free women whilst breast area was the only factor associated with dense area (r=0.739, p<0.001).

conclusionThis is the first time that mammographic density was measured by a quantitative method for women in China and identified associations should be useful to health policy makers who are responsible for introducing effective models of breast cancer prevention and diagnosis.

Identifiers

PMID31015834
PMCPMC6444251
OpenAlexW2923436613

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.