Analysis of epidemiology and nomogram construction for prediction and clinical decision-making in gliomas

Yuxin Zhao¹Zihan Xu²Ying Liu¹Ming Ye¹Rui Chen¹Zhongyu Cao¹Hong Zhou¹Yang Zhou^1*

• ¹Chengdu Third People's Hospital, Chengdu, China
• ²West china hospita, Chengdu, China

Background: Gliomas are the most common primary malignant brain tumors with high mortality. Exploring the epidemiologic characteristics and prognostic factors of gliomas, and constructs a nomogram-based predictive model can help to evaluate the public health impact, optimize risk stratification, and guide treatment decision-making. Methods: This cross-sectional epidemiological analysis used the most recently released data from the Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2000, to December 31, 2019. The SEER-18 database provided data for incidence, prevalence, survival, and initial treatment, as well as the establishment and validation of a nomogram to predict the survival probability of individual patients with gliomas. Results: Among 71,040 cases of glioma patients, the majority were male (40,500 [57.01%]) and White race (52,443 [73.82%]), with glioblastoma (41,125 [57.89%]) as the predominant histology type, primarily located at the cerebrum (49,307 [69.41%]), and mostly categorized as high-grade tumors (22,447 [31.60%]). The age-adjusted incidence rate of gliomas decreased from 4.42 per 100,000 persons in 2000 to 3.81 per 100,000 persons in 2019 [APC of -0.53 (95%CI, -0.71 to -0.34)]. In the incidence analysis among different tumor histology, grade and primary site, glioblastoma, high-grade tumor and primary site of cerebrum were with the highest incidence, respectively. Additionally, the incidence of different histology varied significantly among different age groups. In the multivariable analysis, age, histology, grade, site and treatment (chemotherapy, radiation and surgery) were identified as prognostic factors. Among these factors, age and grade had the most significant impact on prognosis. Furthermore, a predictive nomogram model for 1-/3-/5-year survival rates of gliomas was developed, incorporating the prognostic factors. For the training and test cohorts, the concordance indexes of the nomogram were 0.796 (95%CI, 0.792-0.805) and 0.799 (95%CI, 0.793-0.808), respectively. Conclusion: The incidence and survival of gliomas showed significant variations across different age, histology, grade, site, and treatment groups. The nomogram model based on these factors could accurately predict the survival among patients with gliomas and aid in optimizing treatment decisions.