Unveiling Recurrence Patterns: Analyzing Predictive Risk Factors for Breast Cancer Recurrence after Surgery
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study analyzed breast cancer recurrence intervals and survival in Iran. Key risk factors identified include patient age, progesterone receptor status, tumor grade, and stage, crucial for improving treatment strategies.
Area Of Science
- Oncology
- Biostatistics
Background
- Breast cancer (BC) is a leading cause of cancer mortality in women globally.
- Effective management requires understanding recurrence patterns and survival post-surgery.
Purpose Of The Study
- To investigate time intervals between surgery and recurrence in breast cancer patients.
- To analyze survival outcomes using parametric and semi-parametric models.
Main Methods
- Data from 171 breast cancer cases (2010-2021) at a Tehran center were analyzed.
- Model fitting utilized Akaike Information Criterion (AIC); Cox proportional hazard regression was employed.
- Logistic distribution was identified as the most suitable model for concurrent and independent variables.
Main Results
- The Cox model demonstrated superior fit with the lowest AIC.
- Average age at diagnosis was 50.39 years; estimated typical survival was 53.44 months.
- Significant risk factors for recurrence included patient age, progesterone receptor (PR) status, tumor grade, and tumor stage (P < .05).
Conclusions
- Identified risk factors (age, PR, grade, stage) are critical for predicting breast cancer recurrence.
- Findings emphasize the need for further research and integration of these factors into BC treatment strategies.
Contact us if these videos are not relevant.
Contact us if these videos 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...