Prognostic value of peripheral blood inflammatory-nutritional parameter nomogram model in epithelial ovarian cancer
1Yuan Li,2Xiao Huajing
1Department of Obstetrics and Gynecology Nanjing Lishui People ' s Hospital Nanjing 211200 Jiangsu China
2Department of
Obstetrics and Gynecology the Second Affiliated Hospital of Nanjing Medical University Nanjing 210000 Jiangsu China
Abstract：Objective To investigate the prognostic value of six inflammation and nutrition-related parameters in peripheral blood
for ovarian cancer patients and develop a nomogram model based on these parameters and clinicopathological features and test its
clinical value for prognostic assessment. Method A total of 206 ovarian cancer patients who underwent surgical treatment in Nanjing
Lishui People's Hospital n = 56 and the Second Affiliated Hospital of Nanjing Medical University n = 150 between June 2017 and
June 2020 were included in this retrospective study and their clinical data and peripheral blood parameters were collected. The
predictive values of the neutrophil - to - lymphocyte ratio NLR platelet - to - lymphocyte ratio PLR lymph - to - monocyte ratio
LMR prognostic nutritional index PNI total cholesterol-to-lymphocyte ratio TCLR and C-reactive protein-to-albumin ratio
CAR for overall survival OS of ovarian cancer patients were compared using the receiver operation characteristics ROC
curve. The univariate and multivariate Cox regression analysis was conducted to select independent prognostic factors for ovarian cancer
patients and a nomogram model was developed based on these factors. The predictive performance of the model was evaluated by the
Harrell's consistency index C-index and calibration curves. Result The best cutoff values of PNI and CAR were 47. 8 and 0. 08
with the AUC values of 0. 803 95%CI = 0. 736-0. 870 and 0. 749 95% CI = 0. 673-0. 824 respectively. The predictive values of
PNI and CAR were superior to other parameters. In the univariate analysis a total of 8 variables may affect the OS of ovarian cancer patients they are age P= 0. 011 serum CA125 P= 0. 001 postoperative residual focus P<0. 001 FIGO stage P = 0. 001
LMR P<0. 001 PNI P<0. 001 CAR P<0. 001 and TCLR P = 0. 037 . The multivariate Cox regression analysis showed that
serum CA125 level HR= 2. 814 95%CI = 1. 469-5. 394 P = 0. 002 residual disease HR = 3. 324 95%CI = 1. 736-6. 361 P<
0. 001 FIGO stage HR= 4. 454 95%CI = 1. 360-14. 583 P = 0. 014 PNI HR = 3. 615 95%CI = 1. 852-7. 057 P<0. 001
and CAR HR = 3. 330 95%CI = 1. 684-6. 584 P = 0. 001 were independent prognostic factors for ovarian cancer patients. A
prognostic model was constructed according to the above five variables with a C-index of 0. 821 95%CI = 0. 756-0. 886 . The
calibration curves shows a good consistency between the predicted 1-year 3-year as well as 5-year survival probabilities and
the actual observations. Conclusion PNI and CAR were independent prognostic parameters for ovarian cancer patients. The
prognostic model integrated with these parameters and clinicopathological features showed an excellent and stable predictive
performance and it might be used as an effective tool of risk stratification and personalized treatment decision-making.
