Prediction scoring system based on clinicohematologic parameters for cervical cancer patients undergoing chemoradiation
- 16 June 2020
- journal article
- research article
- Published by BMJ in International Journal of Gynecologic Cancer
- Vol. 30 (11), 1689-1696
- https://doi.org/10.1136/ijgc-2019-001050
Abstract
Objective A scoring system based on clinicohematologic parameters in cervical cancer patients receiving chemoradiation has not been reported to date. The aim of this study was to determine the prognostic value of clinicohematologic parameters in patients with cervical cancer undergoing chemoradiation and to develop a prediction scoring system based on these results. Methods A total of 107 patients who received definitive chemoradiation for cervical cancer were enrolled in this study. The clinical data and hematologic parameters were retrospectively reviewed, and their prognostic value in predicting survival was analyzed. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) and the changes in these hematologic parameters (ΔNLR, ΔPLR, and ΔLMR) between pre- and post-treatment were calculated to determine the specific value of these parameters for predicting patient survival. Results The median follow-up time was 39.9 (range 2.7–114.6) months. The 3-year overall survival rate and progression-free survival rate were 80.9% (95% CI 72.7 to 90.0) and 53.4% (95% CI 44.1 to 64.8), respectively. The median progression-free survival was 67.5 months and the median overall survival was not reached. According to multivariable analysis, a ΔNLR≥0 was significantly associated with decreased progression-free survival (HR=2.91, 95% CI 1.43 to 5.94) and overall survival (HR=3.13, 95% CI 1.18 to 8.27). In addition, age (age Conclusions Both the age and FIGO stage, as clinical parameters, and the ΔNLR, as a hematologic parameter, were independent prognostic factors for survival for cervical cancer patients treated with chemoradiation. Based on these results, we developed a risk score-based classification system for predicting survival.Keywords
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