The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Clinical use of biomarkers in breast cancer: Updated guidelines from the European Group on Tumor Markers (EGTM)
Open accessAbstract
Biomarkers play an essential role in the management of patients with invasive breast cancer. For selecting patients likely to respond to endocrine therapy, both oestrogen receptors (ERs) and progesterone receptors (PRs) should be measured on all newly diagnosed invasive breast cancers. On the other hand, for selecting likely response to all forms of anti-HER2 therapy (trastuzumab, pertuzumab, lapatinib or ado-trastuzumab emtansine), determination of HER2 expression or gene copy number is mandatory. Where feasible, measurement of ER, PR and HER2 should be performed on recurrent lesions and the primary invasive tumour. Although methodological problems exist in the determination of Ki67, because of its clearly established clinical value, wide availability and low costs relative to the available multianalyte signatures, Ki67 may be used for determining prognosis, especially if values are low or high. In oestrogen receptor (ER)-positive, HER2-negative, lymph node-negative patients, multianalyte tests such as urokinase plasminogen activator (uPA)-PAI-1, Oncotype DX, MammaPrint, EndoPredict, Breast Cancer Index (BCI) and Prosigna (PAM50) may be used to predict outcome and aid adjunct therapy decision-making. Oncotype DX, MammaPrint, EndoPredict and Prosigna may be similarly used in patients with 1-3 metastatic lymph nodes. All laboratories measuring biomarkers for patient management should use analytically and clinically validated assays, participate in external quality assurance programs, have established assay acceptance and rejection criteria, perform regular audits and be accredited by an appropriate organisation.
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Cites
Grading quality of evidence and strength of recommendations
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Cited by
Breast Cancer Treatment: A Review
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Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
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Self-citationBreast cancer diagnosis: Imaging techniques and biochemical markers
Jafari, S. H., Saadatpour, Z., Salmaninejad, A., Momeni, F., Mokhtari, M., Nahand, J. S., Rahmati, M., Mirzaei, H., & Kianmehr, M. (2018) · Journal of Cellular Physiology · doi:10.1002/jcp.26379 · Cited by 410
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A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care
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Metadata
- Authors
- Duffy, M. J., Harbeck, N., Nap, M., Molina, R., Nicolini, A., Senkus, E., & Cardoso, F.
- Venue
- European Journal of Cancer
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 75
- Pages
- 284-298
- ISSN
- 0959-8049 (ISSN-L)
- OpenAlex
- W2593104572
- Cited by
- 522
- References
- 95
- FWCI
- 19.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Duffy, M. J., Harbeck, N., Nap, M., Molina, R., Nicolini, A., Senkus, E., & Cardoso, F. (2017). Clinical use of biomarkers in breast cancer: Updated guidelines from the European Group on Tumor Markers (EGTM). European Journal of Cancer. https://doi.org/10.1016/j.ejca.2017.01.017
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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.
