Predicting poor prognosis recurrence in women with endometrial cancer: a nomogram developed by the FRANCOGYN study group View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-11

AUTHORS

Lobna Ouldamer, Sofiane Bendifallah, Gilles Body, Cyril Touboul, Olivier Graesslin, Emilie Raimond, Pierre Collinet, Charles Coutant, Vincent Lavoué, Jean Lévêque, Emile Daraï, Marcos Ballester

ABSTRACT

BACKGROUND: The purpose of this study was to develop a nomogram to predict 'poor prognosis recurrence' (PPR) in women treated for endometrial cancer (EC). METHODS: The data of 861 women who received primary surgical treatment between January 2001 and December 2013 were abstracted from a prospective multicenter database. Data were randomly split into two sets: training and validation with a predefined 2/3 ratio. A Cox proportional hazards multivariate model of selected prognostic features was performed in the training cohort (n=574) to develop a nomogram predicting PPRs. The nomogram was validated in the validation cohort of 287 patients. RESULTS: In the training cohort, 82 (14.3%) developed subsequent PPR. Age, histologic type and grade, lymphovascular space invasion status, FIGO stage, and nodal staging (SLN±pelvic and/or para-aortic lymphadenectomy) were independently associated with subsequent PPR. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.82 (95% confidence interval (CI), 0.73-0.89) in the training set. The validation set showed a good discrimination with an AUC of 0.75 (95% CI, 0.65-0.83). CONCLUSIONS: We have developed a robust tool that is able to predict subsequent PPRs in women with FIGO I-III EC. More... »

PAGES

1296

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/bjc.2016.337

DOI

http://dx.doi.org/10.1038/bjc.2016.337

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012451647

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27824810


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