  Objective: Brain metastases (BMs) are common in cancer patients and
determining the primary tumor site is crucial for effective treatment. This
study aims to predict the primary tumor site from BM MRI data using radiomic
features and advanced machine learning algorithms. Methods: We utilized a
comprehensive dataset from Ocana-Tienda et al. (2023) comprising MRI and
clinical data from 75 patients with BMs. Radiomic features were extracted from
post-contrast T1-weighted MRI sequences. Feature selection was performed using
the GINI index, and data normalization was applied to ensure consistent
scaling. We developed and evaluated Random Forest and XGBoost classifiers, both
with and without hyperparameter optimization using the FOX (Fox optimizer)
algorithm. Model interpretability was enhanced using SHAP (SHapley Additive
exPlanations) values. Results: The baseline Random Forest model achieved an
accuracy of 0.85, which improved to 0.93 with FOX optimization. The XGBoost
model showed an initial accuracy of 0.96, increasing to 0.99 after
optimization. SHAP analysis revealed the most influential radiomic features
contributing to the models' predictions. The FOX-optimized XGBoost model
exhibited the best performance with a precision, recall, and F1-score of 0.99.
Conclusion: This study demonstrates the effectiveness of using radiomic
features and machine learning to predict primary tumor sites from BM MRI data.
The FOX optimization algorithm significantly enhanced model performance, and
SHAP provided valuable insights into feature importance. These findings
highlight the potential of integrating radiomics and machine learning into
clinical practice for improved diagnostic accuracy and personalized treatment
planning.
