Clinical nomogram for predicting the survival of patients with cerebral anaplastic gliomas

Prediction of Cancer-Specific Survival of Brainstem Glioma in Children Based on Risk Stratification Model

Objective

To develop and authenticate a risk stratification framework and nomogram for ascertaining cancer-specific survival (CSS) among the pediatric brainstem gliomas.

Methods

For patients less than 12 years, according to Surveillance, Epidemiology, and End Results (SEER), information from 1998 to 2016 is found in their databases. The survival outcomes, treatments, and demographic clinicopathologic conditions are scrutinized per the database validation, and training cohorts are divided and validated using multivariate Cox regression analysis. A nomogram was designed, and predominantly, the risk stratification conceptualization engaged selected tenets according to the multivariate analysis. The model's authenticity was substantiated through C-index measure and calibration curves.

Results

There are 806 pediatric concerns of histologically concluded brainstem glioma in the research. According to multivariate analysis, age, grade, radiotherapy, and race (with P value < 0.05) depicted independent prognostic variations of the pediatric gliomas. The nomogram's C-index was approximately 0.75 and an accompanied predictive capability for CSS.

Conclusion

The nomogram constructed in this glioma's context is the primary predictor of using risk stratification. A combination of nomograms with the risk stratification mechanism assists clinicians in monitoring high-risk individuals and engage targeted accessory treatment.

Utility of Administrative Databases and Big Data on Understanding Glioma Treatment—A Systematic Review

Background Gliomas are a heterogeneous group of tumors where large multicenter clinical and genetic studies have become increasingly popular in their understanding. We reviewed and analyzed the findings from large databases in gliomas, seeking to understand clinically relevant information.

Methods A systematic review was performed for gliomas studied using large administrative databases up to January 2020 (e.g., National Inpatient Sample [NIS], National Surgical Quality Improvement Program [NSQIP], and Surveillance, Epidemiology, and End Results Program [SEER], National Cancer Database [NCDB], and others).

Results Out of 390 screened studies, 122 were analyzed. Studies included a wide range of gliomas including low- and high-grade gliomas. The SEER database (n = 83) was the most used database followed by NCDB (n = 28). The most common pathologies included glioblastoma multiforme (GBM) (n = 67), with the next category including mixes of grades II to IV glioma (n = 31). Common study themes involved evaluation of descriptive epidemiological trends, prognostic factors, comparison of different pathologies, and evaluation of outcome trends over time. Persistent health care disparities in patient outcomes were frequently seen depending on race, marital status, insurance status, hospital volume, and location, which did not change over time. Most studies showed improvement in survival because of advances in surgical and adjuvant treatments.

Conclusions This study helps summarize the use of clinical administrative databases in gliomas research, informing on socioeconomic issues, surgical outcomes, and adjuvant treatments over time on a national level. Large databases allow for some study questions that would not be possible with single institution data; however, limitations remain in data curation, analysis, and reporting methods.

Clinical Characteristics and Overall Survival Prognostic Nomogram for Oligodendroglioma: A Surveillance, Epidemiology, and End Results Population-Based Analysis

OBJECTIVE

Oligodendroglioma is a rare primary malignant brain tumor that has highly variable clinical outcomes. The aim of this study was to investigate demographics, outcomes, and prognostic factors of all oligodendroglioma cases from the Surveillance, Epidemiology, and End Results database to build a clinical prognosis model to predict survival time of patients with oligodendroglioma.

METHODS

Cases diagnosed between 1975 and 2016 were selected from the Surveillance, Epidemiology, and End Results database. Age, sex, race, insurance, year of diagnosis, marital status, tumor location, tumor size, summary stage, surgery method, and use of radiotherapy and chemotherapy were evaluated with respect to overall survival by univariate and multivariate analysis. A nomogram predicting 5- and 10-year survival probability for oligodendroglioma was constructed and validated.

RESULTS

After data cleaning, 4568 patients with oligodendroglioma were included. At the time of last follow-up, mean survival times among grade II and grade III oligodendrogliomas were 74 and 39 months, respectively. In multivariate analysis, radiotherapy, age, tumor site, summary stage, and surgery demonstrated independent associations with survival in both cohorts. Race and radiotherapy demonstrated independent associations with survival in grade II oligodendroglioma. Sex and chemotherapy demonstrated independent associations with survival in grade III oligodendroglioma. Independent factors in either cohort were selected to build a clinical nomogram. The C-index for the nomogram was 0.738 (95% confidence interval 0.718-0.757). The calibration curves of 5- and 10-year survival rates showed good agreement between the nomogram predictions and actual observations.

CONCLUSIONS

This study was the first to develop a nomogram for predicting overall survival of patients with oligodendroglioma to help clinicians predict patient prognosis accurately and conduct further treatment.