Practical Nomograms and Risk Stratification System for Predicting the Overall and Cancer-specific Survival in Patients with Anaplastic Astrocytoma
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study developed predictive nomograms and risk stratification systems for anaplastic astrocytoma (AA) patients. These tools accurately predict overall survival (OS) and cancer-specific survival (CSS), aiding clinical decision-making.
Area Of Science
- Neuro-oncology
- Clinical Decision Support
- Biostatistics
Background
- Anaplastic astrocytoma (AA) is a rare primary brain tumor with unpredictable clinical outcomes.
- Effective tools are needed for clinical decision-making in AA patient management.
Purpose Of The Study
- To develop practical tools for clinical decision-making in anaplastic astrocytoma (AA) patients.
- To create prognostic nomograms and risk stratification systems for overall survival (OS) and cancer-specific survival (CSS).
Main Methods
- Retrospective analysis of 2997 AA patients from the Surveillance, Epidemiology, and End Results database (2004-2015).
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression for factor selection and nomogram development.
- Validated nomogram predictive ability using concordance index and receiver operating characteristic curves.
Main Results
- Identified age, income, tumor site, extension, surgery, radiotherapy, and chemotherapy as independent prognostic factors for OS and CSS.
- Developed nomograms with high predictive accuracy (concordance index ~0.75) and satisfactory clinical utility.
- Stratified AA patients into high- and low-risk groups based on established nomograms.
Conclusions
- Constructed validated predictive nomograms and risk classification systems for AA patient survival.
- These tools demonstrate considerable accuracy and reliability for predicting OS and CSS rates.
- The developed models offer potential utility for future clinical practice in managing anaplastic astrocytoma.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
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...
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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,...