Just how accurate are the major risk stratification systems for early-stage endometrial cancer?
Open Access
- 12 February 2015
- journal article
- research article
- Published by Springer Science and Business Media LLC in British Journal of Cancer
- Vol. 112 (5), 793-801
- https://doi.org/10.1038/bjc.2015.35
Abstract
Background: To compare the accuracy of five major risk stratification systems (RSS) in classifying the risk of recurrence and nodal metastases in early-stage endometrial cancer (EC). Methods: Data of 553 patients with early-stage EC were abstracted from a prospective multicentre database between January 2001 and December 2012. The following RSS were identified in a PubMed literature search and included the Post Operative Radiation Therapy in Endometrial Carcinoma (PORTEC-1), the Gynecologic Oncology Group (GOG)-99, the Survival effect of paraaortic lymphadenectomy (SEPAL), the ESMO and the ESMO-modified classifications. The accuracy of each RSS was evaluated in terms of recurrence-free survival (RFS) and nodal metastases according to discrimination. Results: Overall, the ESMO -modified RSS provided the highest discrimination for both RFS and for nodal metastases with a concordance index (C-index) of 0.73 (95% CI, 0.70-0.76) and an area under the curve (AUC) of 0.80 (0.78-0.72), respectively. The other RSS performed as follows: the PORTEC1, GOG-99, SEPAL, ESMO classifications gave a C-index of 0.68 (0.66-0.70), 0.65 (0.63-0.67), 0.66 (0.63-0.69), 0.71 (0.68-0.74), respectively, for RFS and an AUC of 0.69 (0.66-0.72), 0.69 (0.67-0.71), 0.68 (0.66-0.70), 0.70 (0.68-0.72), respectively, for node metastases. Conclusions: None of the five major RSS showed high accuracy in stratifying the risk of recurrence or nodal metastases in patients with early-stage EC, although the ESMO-modified classification emerged as having the highest power of discrimination for both parameters. Therefore, there is a need to revisit existing RSS using additional tools such as biological markers to better stratify risk for these patients.Keywords
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