Development of Nomogram Models Based on Peripheral Blood Score and Clinicopathological Parameters to Predict Preoperative Advanced Stage and Prognosis for Epithelial Ovarian Cancer Patients

Abstract

Nutritional and inflammatory states are crucial in cancer development. The purpose of this study is to construct a scoring system grounded on peripheral blood parameters associated with nutrition and inflammation and explore its value in stage, overall survival (OS), and progression-free survival (PFS) prediction for epithelial ovarian cancer (EOC) patients. Patients and Methods: Four hundred and fifty-three EOC patients were retrospectively identified and their clinical data and relevant peripheral blood parameters were collected. The ratio of neutrophil to lymphocyte, lymphocyte to monocyte, fibrinogen to lymphocyte, total cholesterol to lymphocyte and albumin level were calculated and dichotomized. A scoring system named peripheral blood score (PBS) was constructed. Univariate and multivariate Logistic or Cox regression analyses were used to select independent factors; these factors were then used to develop nomogram models of advanced stage and OS, PFS, respectively. The internal validation and DCA analysis were performed to evaluate models. Results: Lower PBS indicated a better prognosis and higher PBS indicated inferior. High PBS is associated with advanced stage, high CA125, serous histological type, poor differentiation, and accompanied ascites. The logistic regression showed age, CA125, and PBS were independent factors for the FIGO III-IV stage. The nomogram models for advanced FIGO stage based on these factors showed good efficiency. FIGO stage, residual disease, and PBS were independent factors affecting OS and PFS, the nomogram models composed of these factors had good performance. DCA curves revealed the models augmented net benefits. Conclusion: PBS can be a noninvasive biomarker for EOC patients' prognosis. The related nomogram models could be powerful, cost-effective tools to provide information of advanced stage, OS, and PFS for EOC patients.

Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer

“…The nomogram was further modified to increase the clinical utility of BRS. It showed higher AUC values compared to BRS alone, and exceeding the predictive power of a nomogram also based on peripheral blood features constructed by Bai et al [ 48 ], implying a higher predictive value for prognostic prediction in OC patients, which suggested that it may be a promising alternative metric for assessing prognostic risk in clinical OC.…”

CD47—a novel prognostic predicator in epithelial ovarian cancer and correlations with clinicopathological and gene mutation features

“…Notably, our nomogram area under the curve (AUC) (0.803, 95% CI, 0.673–0.932; 0.869, 95% CI, 0.757–0.981) (Fig. 4 C) used to predict 1- and 3-year PFS for EOC patients was significantly higher than the AUC of other nomograms [ 46 , 47 ]. On the basis of the existing necessary detection items of EOC, incorporating easy-to-detect CD47 IHC score makes our model low-cost and convenient application.…”

Combined aqupla, paclitaxel liposome, and docetaxel treatment: survival and biomarker outcomes in recurrent ovarian cancer patients

“…For example, FIGO III-IV was identified as one independent prognostic factor for OS and PFS in OC patients in research by Bai et al. ( 27 ). In addition, a SEER database –based study proposed FIGO staging as an independent prognostic factor for malignant germ cell tumours of the ovary ( 28 ).…”