Development and Validation of a Nomogram Model Based on Hematological Indicators for Predicting the Prognosis of Diffused Gliomas

Song Han; Fang-Wen Qu; Peng-Fei Wang; Ying-Xin Liu; Shou-Wei Li; Chang-Xiang Yan

doi:10.3389/fsurg.2022.803237

Development and Validation of a Nomogram Model Based on Hematological Indicators for Predicting the Prognosis of Diffused Gliomas

Front Surg. 2022 Apr 13:9:803237. doi: 10.3389/fsurg.2022.803237. eCollection 2022.

Authors

Song Han¹, Fang-Wen Qu^{1

2}, Peng-Fei Wang¹, Ying-Xin Liu², Shou-Wei Li¹, Chang-Xiang Yan¹

Affiliations

¹ Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.
² Grade 2018, Medical College, Qingdao University, Qingdao, China.

Abstract

Background: Diffused gliomas are aggressive malignant brain tumors. Various hematological factors have been proven to predict the prognosis of patients with gliomas. The aim of this study is to integrate these hematological markers and develop a comprehensive system for predicting the prognosis of patients with gliomas.

Method: This retrospective study included 723 patients pathologically diagnosed with diffused gliomas. Hematological indicators were collected preoperatively, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), albumin globulin ratio (AGR), platelet distribution width (PDW), red blood cell distribution width (RDW), fibrinogen (FIB), and prognostic nutritional index (PNI). Least absolute shrinkage and selection operator (LASSO) Cox was applied to screen the hematological indicators for a better prediction of patients' prognosis and to build an inflammation-nutrition score. A nomogram model was developed to predict the overall survival (OS), which included age, tumor grade, IDH-1 mutations, and inflammation-nutrition score.

Result: Patients were randomly divided into a primary cohort (n = 509) and a validation cohort (n = 214). There was no difference in age and IDH-1 mutation frequency between the cohorts. In the primary cohort, NLR, LMR, AGR, FIB, and PNI were selected to build an inflammation nutrition score. Patients with a high-risk inflammation-nutrition score had a short median OS of 17.40 months compared with 27.43 months in the low-risk group [HR 2.54; 95% CI (1.91-3.37); p < 0.001]. Moreover, age, tumor grade, IDH-1 mutations, and inflammation-nutrition score were independent prognostic factors in the multivariate analysis and thus were included in the nomogram model. The nomogram model showed a high prediction value with a Harrell's concordance index (C-index) of 0.75 [95% CI (0.72-0.77)]. The validation cohort supported these results.

Conclusion: The prognostic nomogram model provided a high prognostic predictive power for patients with gliomas.

Keywords: gliomas; hematological; nomogram; predictive modeling; prognosis.