Accuracy of a nomogram for prediction of lymph-node metastasis detected with conventional histopathology and ultrastaging in endometrial cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-04

AUTHORS

M Koskas, E Chereau, M Ballester, G Dubernard, F Lécuru, D Heitz, P Mathevet, H Marret, D Querleu, F Golfier, E Leblanc, D Luton, R Rouzier, E Daraï

ABSTRACT

BACKGROUND: We developed a nomogram based on five clinical and pathological characteristics to predict lymph-node (LN) metastasis with a high concordance probability in endometrial cancer. Sentinel LN (SLN) biopsy has been suggested as a compromise between systematic lymphadenectomy and no dissection in patients with low-risk endometrial cancer. METHODS: Patients with stage I-II endometrial cancer had pelvic SLN and systematic pelvic-node dissection. All LNs were histopathologically examined, and the SLNs were examined by immunohistochemistry. We compared the accuracy of the nomogram at predicting LN detected with conventional histopathology (macrometastasis) and ultrastaging procedure using SLN (micrometastasis). RESULTS: Thirty-eight of the 187 patients (20%) had pelvic LN metastases, 20 had macrometastases and 18 had micrometastases. For the prediction of macrometastases, the nomogram showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.76, and was well calibrated (average error =2.1%). For the prediction of micro- and macrometastases, the nomogram showed poorer discrimination, with an AUC of 0.67, and was less well calibrated (average error =10.9%). CONCLUSION: Our nomogram is accurate at predicting LN macrometastases but less accurate at predicting micrometastases. Our results suggest that micrometastases are an 'intermediate state' between disease-free LN and macrometastasis. More... »

PAGES

1267

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/bjc.2013.95

DOI

http://dx.doi.org/10.1038/bjc.2013.95

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024034477

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23481184


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