Survival of infants ≤24 months of age with brain tumors: A population-based study using the SEER database

Claire Faltermeier1, Timothy Chai2, Sharjeel Syed2

  • 1Department of Neurosurgery, University of California, San Francisco, CA, United States of America.

Plos One
|September 26, 2019
PubMed

Insights

Survival for infant brain tumors has improved since the 1970s, but not for all types. Updated prognostication is needed for these pediatric brain tumors, considering changes in treatment and outcomes.

Area of Science:

  • Pediatric oncology
  • Neuro-oncology
  • Epidemiology

Background:

  • Infant brain tumors are the most common solid malignancy and leading cause of cancer-related death in children.
  • Existing epidemiological data is limited by small case numbers.
  • This study provides updated prognostication using population-based data.

Purpose of the Study:

  • To analyze survival trends for infant brain tumors.
  • To update prognostication based on contemporary and historical data.
  • To identify changes in management and outcomes over time.

Main Methods:

  • Population-based cohort analysis using the Surveillance, Epidemiology and End Results (SEER) database.
  • Inclusion of infants diagnosed with brain tumors between 1973 and 2013.
  • Stratification of survival rates by tumor type and decade, analyzing management trends.

Main Results:

  • Survival improved for most infant brain tumor types, excluding embryonal and choroid plexus tumors.
  • Ependymal tumors showed the most significant survival improvement, with 5-year survival increasing from 28% to 77% between the 1980s and 2000s.
  • Radiation use declined overall but increased for embryonal and ependymal tumors after 2000.

Conclusions:

  • Overall survival for infant brain tumors has improved, but not uniformly across all types.
  • Prognostication for infants with brain tumors requires updating due to evolving management and survival rates.
Abstract

Related Concept Videos

Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
290
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
650
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
756
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
581