Objective:

To predict the risk of death and cancer recurrence in breast cancer patients with pesticide exposure and to propose a reevaluation of current risk stratification standards, emphasizing the potential impact on patient outcomes.

Key Findings:

• Incorporating pesticide exposure improved the prediction quality of the best model (random forest) by 24.12%, highlighting the critical role of environmental factors in risk assessment.
• Machine learning models demonstrated enhanced ability to identify patterns when pesticide exposure was included as a risk factor, suggesting a need for updated clinical guidelines.

Interpretation:

The study highlights the necessity of including pesticide exposure in breast cancer risk stratification, particularly in regions with significant agricultural activity, to improve patient outcomes and inform public health policies.

Limitations:

• The study is limited to a specific population of women in Brazil, which may affect the generalizability of the findings; further research is needed in diverse populations.
• Potential confounding factors related to pesticide exposure and breast cancer outcomes were not fully explored, which may influence the validity of the results.

Conclusion:

The findings suggest a need for reevaluation of current breast cancer risk stratification standards to include pesticide exposure as a significant risk factor, potentially leading to improved patient management.