Analysis of Prognostic Factors and Surgical Management of Elderly Patients with Low-Grade Gliomas

Geriatric grade 2 and 3 gliomas: A national cancer database analysis of demographics, treatment utilization, and survival

With increasing life expectancies and population aging, the incidence of elderly patients with grade 2 and 3 gliomas is increasing. However, there is a paucity of knowledge on factors affecting their treatment selection and overall survival (OS). Geriatric patients aged between 60 and 89 years with histologically proven grade 2 and 3 intracranial gliomas were identified from the National Cancer Database between 2010 and 2017. We analyzed patients' demographic data, tumor characteristics, treatment modality, and outcomes. The Kaplan-Meier method was used to analyze OS. Univariate and multivariate analyses were performed to assess the predictive factors of mortality and treatment selection. A total of 6257 patients were identified: 3533 (56.3 %) hexagenerians, 2063 (32.9 %) septuagenarians, and 679 (10.8 %) octogenarians. We identified predictors of lower OS in patients, including demographic factors (older age, non-zero Charlson-Deyo score, non-Hispanic ethnicity), socioeconomic factors (low income, treatment at non-academic centers, government insurance), and tumor-specific factors (higher grade, astrocytoma histology, multifocality). Receiving surgery and chemotherapy were associated with a lower risk of mortality, whereas receiving radiotherapy was not associated with better OS. Our findings provide valuable insights into the complex interplay of demographic, socioeconomic, and tumor-specific factors that influence treatment selection and OS in geriatric grade 2 and 3 gliomas. We found that advancing age correlates with a decrease in OS and a reduced likelihood of undergoing surgery, chemotherapy, or radiotherapy. While receiving surgery and chemotherapy were associated with improved OS, radiotherapy did not exhibit a similar association.

Development and external validation of dual online tools for prognostic assessment in elderly patients with high-grade glioma: a comprehensive study using SEER and Chinese cohorts

Background

Elderly individuals diagnosed with high-grade gliomas frequently experience unfavorable outcomes. We aimed to design two web-based instruments for prognosis to predict overall survival (OS) and cancer-specific survival (CSS), assisting clinical decision-making.

Methods

We scrutinized data from the SEER database on 5,245 elderly patients diagnosed with high-grade glioma between 2000-2020, segmenting them into training (3,672) and validation (1,573) subsets. An additional external validation cohort was obtained from our institution. Prognostic determinants were pinpointed using Cox regression analyses, which facilitated the construction of the nomogram. The nomogram's predictive precision for OS and CSS was gauged using calibration and ROC curves, the C-index, and decision curve analysis (DCA). Based on risk scores, patients were stratified into high or low-risk categories, and survival disparities were explored.

Results

Using multivariate Cox regression, we identified several prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in elderly patients with high-grade gliomas, including age, tumor location, size, surgical technique, and therapies. Two digital nomograms were formulated anchored on these determinants. For OS, the C-index values in the training, internal, and external validation cohorts were 0.734, 0.729, and 0.701, respectively. We also derived AUC values for 3-, 6-, and 12-month periods. For CSS, the C-index values for the training and validation groups were 0.733 and 0.727, with analogous AUC metrics. The efficacy and clinical relevance of the nomograms were corroborated via ROC curves, calibration plots, and DCA for both cohorts.

Conclusion

Our investigation pinpointed pivotal risk factors in elderly glioma patients, leading to the development of an instrumental prognostic nomogram for OS and CSS. This instrument offers invaluable insights to optimize treatment strategies.