Evidence map›Paper›PMID 24878755›Full record

ArticleVirchows Archiv : an international journal of pathology2014

Molecular classification of breast cancer.

Darina Vuong, Peter T Simpson, Benjamin Green, Margaret C Cummings, Sunil R Lakhani

Abstract read
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In one paragraph

Article in Virchows Archiv : an international journal of pathology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed, 2 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

94 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Spatiotemporal cell type deconvolution leveraging tissue structure.bioRxiv : the preprint server for biology · 2026
    Article
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  7. Review
  8. Article
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  10. Review
  11. Clinical Implications of Breast Cancer Intrinsic Subtypes.Advances in experimental medicine and biology · 2025
    Review
  12. Article
  13. Review
  14. Article
  15. [Research progress of breast pathology image diagnosis based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024
    Review
  16. Review
  17. Review
  18. Review
  19. Article
  20. Article

34 more citing papers are in PubMed but not listed here.

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.

Darina VuongThe University of Queensland, UQ Centre for Clinical Research, Herston, Brisbane, QLD, 4029, Australia.
Peter T Simpson
Benjamin Green
Margaret C Cummings
Sunil R Lakhani

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is a complex, multifaceted disease encompassing a great variety of entities that show considerable variation in clinical, morphological and molecular attributes. Traditional classifications including histological assessment and clinical staging are used to guide patient management. In recent years, there has been exponential progress in molecular analysis with profound implications for our understanding of breast cancer biology and, hence, classification. There are now genome-based frameworks for the molecular categorisation of breast cancer including the development of prognostic and predictive signatures that potentially allow individualisation of treatment. Here we review the current state of the molecular classifications of in situ and invasive breast cancer including special subtypes. Future perspectives are also provided.

Indexed as

Biomarkers, TumorBreast NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansImmunohistochemistryPrognosisBiomarkers, Tumor

Identifiers

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.