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Prediction model for regional or distant recurrence in endometrial cancer based on classical pathological and immunological parameters.

Versluis, M A; de Jong, R A; Plat, A; Bosse, T; Smit, V T; Mackay, H; Powell, M; Leary, A; Mileshkin, L; Kitchener, H C; Crosbie, E J; Edmondson, R J; Creutzberg, C L; Hollema, H; Daemen, T; de Bock, G H; Nijman, H W

British journal of cancer. 2015;113(5):786-93.

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Abstract

BACKGROUND: Adjuvant therapy increases disease-free survival in endometrial cancer (EC), but has no impact on overall survival and negatively influences the quality of life. We investigated the discriminatory power of classical and immunological predictors of recurrence in a cohort of EC patients and confirmed the findings in an independent validation cohort. METHODS: We reanalysed the data from 355 EC patients and tested our findings in an independent validation cohort of 72 patients with EC. Predictors were selected and Harrell's C-index for concordance was used to determine discriminatory power for disease-free survival in the total group and stratified for histological subtype. RESULTS: Predictors for recurrence were FIGO stage, lymphovascular space invasion and numbers of cytotoxic and memory T-cells. For high risk cancer, cytotoxic or memory T-cells predicted recurrence as well as a combination of FIGO stage and lymphovascular space invasion (C-index 0.67 and 0.71 vs 0.70). Recurrence was best predicted when FIGO stage, lymphovascular space invasion and numbers of cytotoxic cells were used in combination (C-index 0.82). Findings were confirmed in the validation cohort. CONCLUSIONS: In high-risk EC, clinicopathological or immunological variables can predict regional or distant recurrence with equal accuracy, but the use of these variables in combination is more powerful.

Bibliographic metadata

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Published date:
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Place of publication:
England
Volume:
113
Issue:
5
Pagination:
786-93
Digital Object Identifier:
10.1038/bjc.2015.268
Pubmed Identifier:
26217922
Pii Identifier:
bjc2015268
Access state:
Active

Institutional metadata

University researcher(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:279435
Created by:
Crosbie, Emma
Created:
21st November, 2015, 22:01:05
Last modified by:
Crosbie, Emma
Last modified:
23rd November, 2015, 08:03:09

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