Evidence map›Paper›PMID 29843215›Full record

ArticleCancer biomarkers : section A of Disease markers2018

The correlation between mammographic densities and molecular pathology in breast cancer.

Yu Ji, Zhenzhen Shao, Junjun Liu, Yujuan Hao, Peifang Liu

Abstract read
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 37% 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

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Microscopic Tumour Classification by Digital Mammography.Journal of healthcare engineering · 2021
    Article
  4. Article
  5. Article
  6. Article
  7. Review
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

5 authors.

Yu Ji
Zhenzhen Shao
Junjun Liu
Yujuan Hao
Peifang Liu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to analyze the correlation between mammographic density obtained by density analysis software (DAS)/radiologists visual (RV) classification with molecular subtype, and the expression levels of estrogen receptor (ER), progesterone receptor (PR), Ki67 antigen (Ki-67), p53 gene (p53), and human epidermal growth factor receptor-2 (HER2). A total of 688 breast cancer patients with digital mammography and complete molecular pathological results in Tianjin Medical University Cancer Institute and Hospital between February 2015 and February 2016 were collected. The DAS-density grade (DASD) and the radiologists visually classified density grade (RVD) were evaluated by 3 radiologists. The correlation between density grade and the expression levels of ER, PR, Ki-67, p53, HER2 and breast cancer molecular subtype (PMS) were analyzed. The agreement between DASD and RVD was explored. ER, PR and HER-2 positive rate were significantly different among patients with different RVD grades (P< 0.05). HER2 positive rates showed an increasing trend following RVD upgrading (P𝑡𝑟𝑒𝑛𝑑< 0.05). HER-2 positive rate in RVD D1 + D2 was 7.69%, which was higher than that in D3 + D4 (P< 0.05). The ER and Ki-67 expressions in patients were markedly different among DASD (P= 0.009 and 0.002) and RVD (P= 0.012 and 0.036) with different grades. The kappa value of each DASD to RVD was 0.31 (P< 0.01). The RVD 3 proportion was 14.58% (63/432) in HER2 Over-expressing subtype, which was apparently higher than RVD1 (2.43%, 1/41) (P< 0.05). Breast density may be partial correlated with molecular pathology in breast cancer.

Indexed as

Biomarkers, TumorBreast DensityAdultAgedBreast NeoplasmsFemaleGene ExpressionHumansImmunohistochemistryMammographyMiddle AgedOdds RatioBiomarkers, TumorBreast cancercorrelationmammographic densitymolecular pathology

Identifiers

PMID29843215
PMCPMC13078464
OpenAlexW2804750456

What OpenQuestion holds

Textmetadata
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.