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J Exp Clin Cancer Res. 2010 Jan 21;29:5. doi: 10.1186/1756-9966-29-5.

Ultrastaging of lymph node in uterine cancers.

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  • 1Department of Gynaecology and Obstetrics, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, CancerEst, Université Pierre et Marie Curie, Paris 6, France.

Abstract

BACKGROUND:

Lymph node status is an important prognostic factor and a criterion for adjuvant therapy in uterine cancers. While detection of micrometastases by ultrastaging techniques is correlated to prognosis in several other cancers, this remains a matter of debate for uterine cancers. The objective of this review on sentinel nodes (SN) in uterine cancers was to determine the contribution of ultrastaging to detect micrometastases.

METHODS:

Review of the English literature on SN procedure in cervical and endometrial cancers and histological techniques including hematoxylin and eosin (H&E) staining, serial sectioning, immunohistochemistry (IHC) and molecular techniques to detect micrometastases.

RESULTS:

In both cervical and endometrial cancers, H&E and IHC appeared insufficient to detect micrometastases. In cervical cancer, using H&E, serial sectioning and IHC, the rate of macrometastases varied between 7.1% and 36.3% with a mean value of 25.8%. The percentage of women with micrometastases ranged from 0% and 47.4% with a mean value of 28.3%. In endometrial cancer, the rate of macrometastases varied from 0% to 22%. Using H&E, serial sectioning and IHC, the rate of micrometastases varied from 0% to 15% with a mean value of 5.8%. In both cervical and endometrial cancers, data on the contribution of molecular techniques to detect micrometastases are insufficient to clarify their role in SN ultrastaging.

CONCLUSION:

In uterine cancers, H&E, serial sectioning and IHC appears the best histological combined technique to detect micrometastases. Although accumulating data have proved the relation between the risk of recurrence and the presence of micrometastases, their clinical implications on indications for adjuvant therapy has to be clarified.

PMID:
20092644
[PubMed - indexed for MEDLINE]
PMCID:
PMC2828991
Free PMC Article
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