Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 482

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade

Open access
Weigelt, Britta, Baehner, Frederick L, Reis‐Filho, Jorge S·2010·The Journal of Pathology

Abstract

In the last decade, the development of microarrays and the ability to perform massively parallel gene expression analysis of human tumours were received with great excitement by the scientific community. The promise of microarrays was of apocalyptic dimensions, with some experts envisaging that it would be a matter of a few years for this technology to replace traditional clinicopathological markers in clinical practice and treatment decision‐making. The replacement of histopathology by high‐tech and more objective approaches to cancer diagnosis, prognostication and prediction was, at that time, a foregone conclusion. Ten years after the initial publications of translational research studies using microarrays, one cannot deny that this technology has changed the way breast cancer is perceived. It has brought the concept of breast cancer heterogeneity to the forefront of cancer research, and the fact that distinct subtypes of breast cancer are completely different diseases that affect the same anatomical site. Furthermore, it has led to the development of prognostic and predictive ‘gene signatures’, which are yet to be fully incorporated into clinical practice. Importantly, though, the prognostic and predictive power of microarrays has been shown to be complementary to, rather than a replacement for, traditional clinicopathological parameters. Here we endeavour to provide a fair and balanced assessment of what microarray‐based gene expression analysis has taught us in the last decade and its contribution to breast cancer classification, prognostication and prediction. Copyright © 2009 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/path.2648

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 482

Metadata

Authors
Weigelt, B., Baehner, F. L., & Reis‐Filho, J. S.
Venue
The Journal of Pathology
Publisher
Wiley
Published
2009-11-19
Volume
220
Issue
2
Pages
263-280
ISSN
  • 0022-3417 (ISSN-L)
  • 1096-9896
OpenAlex
W2050328564
Cited by
482
References
11
FWCI
11.75
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.