Radiomic-Based Machine Learning for Differentiating Brain Metastases Recurrence from Radiation Necrosis Post-Gamma Knife Radiosurgery: A Feasibility Study

Background: Radiation therapy is a key treatment modality for brain metastases. While providing a treatment alternative, post-treatment imaging often presents diagnostic challenges, particularly in distinguishing tumor recurrence from radiation-induced changes such as necrosis. Advanced imaging techniques and artificial intelligence (AI)-based radiomic analyses emerge as alternatives to help lesion characterization. The objective of this study was to assess the capacity of machine learning algorithms to distinguish between brain metastases recurrence and radiation necrosis. Methods: The research was conducted in two phases and used publicly available MRI data from patients treated with Gamma Knife radiosurgery. In the first phase, 30 cases of local recurrence of brain metastases and 30 cases of radiation-induced necrosis were considered. Image segmentation and radiomic feature extraction were performed on these data using MatRadiomics_1_5_3, a MATLAB-based framework integrating PyRadiomics. Features were then selected using point-biserial correlation. In the second phase, a classification was performed using a Support Vector Machine model with repeated stratified cross-validation settings. Results: The results achieved an accuracy on the test set of 83% for distinguishing metastases from necrosis. Conclusions: The results of this feasibility study demonstrate the potential of radiomics and AI to improve diagnostic accuracy and personalized care in neuro-oncology.