Unveiling Recurrence Patterns: Analyzing Predictive Risk Factors for Breast Cancer Recurrence after Surgery.

Objectives: Breast cancer (BC) stands as the second-leading cause of female-specific cancer-related fatalities globally, necessitating comprehensive research to address its critical aspects. This study aimed to explore the time intervals between surgery and disease recurrence in BC patients and their survival utilizing various parametric and semi-parametric models.

Methods: After the examination of data collected from 2010 to 2021 at a BC Center in Tehran, Iran, 171 cases met the criteria for analysis out of 2246 datasets. Model fitting, was assessed through the Akaike Information Criterion (AIC), and indicated the logistic distribution as the most fit one among concurrent and independent variable models.

Results: The Cox proportional hazard regression model consistently demonstrated superior fitting, characterized by the lowest AIC values. The average age at diagnosis was 50.39 years, with a standard deviation of 11.13. Typical survival time was estimated 53.44 months, falling within a confidence interval of 51.41-55.48 months at a 95% confidence level. The 1-year survival rate was determined at 0.92 (95% CI: 0.89-0.94). Notably, patient age while cancer diagnosis, progesterone receptor (PR), tumor grade, and tumor stage were found to be statistically significant (P < .05) risk factors for prediction of BC recurrence after surgery in Iran by Cox model.

Conclusions: Our findings underscore the importance of further exploration and consideration of the identified risk factors in BC research and treatment strategies.