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Stratification of risk groups according to survival after recurrence in endometrial cancer patients.

ABSTRACT: To identify prognostic factors for overall survival after recurrence (OSr) in endometrioid endometrial cancer (EC) patients and categorize patient subgroups that predict outcomes using these variables.Consecutive patients with recurrent endometrioid EC seen in our institution from 1989 to 2013 were retrospectively reviewed. Cox regression models were used to identify the clinicopathological factors associated with OSr. By summing scores proportionate to the hazard ratio (HR) for each significant variable, we stratified patients into 3 risk groups.Enrolled patients (n?=?108) had a median time to recurrence of 15 (range, 3-163) months after initial treatment and a median OSr of 22 (range, 1-207) months. Twenty patients (18.5%) had locoregional recurrence, and 88 (81.5%) distant. One hundred three patients underwent salvage therapy; 51 (47.2%) received chemotherapy only, 22 (20.3%) received radiotherapy either alone or combined with chemotherapy, and 29 (26.9%) underwent salvage cytoreductive surgery. Multivariate regression analysis revealed that time to relapse after initial treatment, cancer antigen-125 level at recurrence, and the number of recurrent lesions were independent predictors of OSr. Incorporating these factors, we stratified patients into low-risk (n?=?19), intermediate-risk (n?=?43), and high-risk (n?=?46) groups. The likelihood of cancer-specific death was higher in both the high-risk (HR?=?8.948, 95% confidence interval [CI]?=?3.498-22.893, P?

SUBMITTER: Shim SH

PROVIDER: S-EPMC5457863 | biostudies-other | 2017 May

REPOSITORIES: biostudies-other

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Stratification of risk groups according to survival after recurrence in endometrial cancer patients.

Shim Seung-Hyuk SH Kim Dae-Yeon DY Kim Hyun Jung HJ Lee Shin-Wha SW Park Jeong-Yeol JY Suh Dae-Shik DS Kim Jong-Hyeok JH Kim Yong-Man YM Kim Young-Tak YT Nam Joo-Hyun JH

Medicine 20170501 21

To identify prognostic factors for overall survival after recurrence (OSr) in endometrioid endometrial cancer (EC) patients and categorize patient subgroups that predict outcomes using these variables.Consecutive patients with recurrent endometrioid EC seen in our institution from 1989 to 2013 were retrospectively reviewed. Cox regression models were used to identify the clinicopathological factors associated with OSr. By summing scores proportionate to the hazard ratio (HR) for each significant ...[more]

PMID: 28538383

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Project description:IntroductionAmong industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning-based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages.MethodsThe ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures.ResultsThe area under the curve was 0.84 (95% confidence interval [CI], 0.77-0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79-0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1-2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages.ConclusionsOur data confirmed ENDORISK model's laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.

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Stratification of risk groups according to survival after recurrence in endometrial cancer patients.

Publications

Stratification of risk groups according to survival after recurrence in endometrial cancer patients.

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