Zhao Yuxin, Xu Zihan, Liu Ying, Ye Ming, Chen Rui, Cao Zhongyu, Zhou Hong, Zhou Yang
Department of Ultrasound, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.
Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Front Immunol. 2025 Aug 1;16:1624142. doi: 10.3389/fimmu.2025.1624142. eCollection 2025.
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.
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.
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.
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.
胶质瘤是最常见的原发性恶性脑肿瘤，死亡率很高。探索胶质瘤的流行病学特征和预后因素，并构建基于列线图的预测模型，有助于评估其对公众健康的影响、优化风险分层并指导治疗决策。
这项横断面流行病学分析使用了监测、流行病学和最终结果（SEER）数据库于2000年1月1日至2019年12月31日期间发布的最新数据。SEER-18数据库提供了发病率、患病率、生存率和初始治疗的数据，以及用于预测胶质瘤个体患者生存概率的列线图的建立和验证。
在71040例胶质瘤患者中，大多数为男性（40500例[57.01%]）和白人（52443例[73.82%]），胶质母细胞瘤（41125例[57.89%]）是主要的组织学类型，主要位于大脑（49307例[69.41%]），大多归类为高级别肿瘤（22447例[31.60%]）。胶质瘤的年龄调整发病率从2000年的每10万人4.42例降至2019年的每10万人3.81例[年度百分比变化（APC）为-0.53（95%CI，-0.71至-0.34）]。在不同肿瘤组织学、分级和原发部位的发病率分析中，胶质母细胞瘤、高级别肿瘤和大脑原发部位的发病率分别最高。此外，不同组织学的发病率在不同年龄组之间有显著差异。在多变量分析中，年龄、组织学、分级、部位和治疗（化疗、放疗和手术）被确定为预后因素。在这些因素中，年龄和分级对预后的影响最为显著。此外，开发了一个用于胶质瘤1年/3年/5年生存率的预测列线图模型，纳入了预后因素。对于训练队列和测试队列，列线图的一致性指数分别为0.796（95%CI，0.792-0.805）和0.799（95%CI，0.793-0.808）。
胶质瘤的发病率和生存率在不同年龄、组织学、分级、部位和治疗组之间存在显著差异。基于这些因素的列线图模型可以准确预测胶质瘤患者的生存情况，并有助于优化治疗决策。
