BrainMetDetect: Predicting Primary Tumor from Brain Metastasis MRI Data Using Radiomic Features and Machine Learning Algorithms

TL;DR

This study uses radiomic features and machine learning algorithms, especially optimized XGBoost, to accurately predict primary tumor sites from brain metastasis MRI data, demonstrating high diagnostic potential.

Contribution

It introduces a novel application of radiomics combined with FOX-optimized machine learning models for primary tumor prediction from BM MRI data.

Findings

XGBoost with FOX optimization achieved 99% accuracy.

SHAP analysis identified key radiomic features influencing predictions.

Models significantly outperformed baseline classifiers.

Abstract

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.