To develop and evaluate a deep learning model for predicting brain tumor recurrence following Gamma Knife radiotherapy using multimodal MRI images, radiation therapy details, and clinical parameters.

Brain metastases are common, with over 150,000 new cases annually in the U.S. Gamma Knife radiotherapy is a widely used treatment for brain tumors. However, recurrence is a concern, requiring early detection for timely intervention. Previous studies using AI in brain tumor prognosis have primarily focused on glioblastomas, leaving a gap in research regarding metastatic brain tumors post-Gamma Knife therapy. This study aims to address this by developing predictive models for recurrence risk.

The study utilized the Brain Tumor Radiotherapy GammaKnife dataset from The Cancer Imaging Archive (TCIA), including MRI images, lesion annotations, and radiation dose details. Data preprocessing involved normalizing MRI images, extracting lesion-specific radiation doses, and applying data augmentation. A 3D Convolutional Neural Network was designed with multiple convolutional layers and trained using stratified sampling. The model was trained for 50 epochs with a batch size of 16 and optimized using the Adam optimizer.

The proof-of-concept model successfully integrated multimodal data and identified stable tumors with accuracy of 79.5% and specificity of 84.4%. However, true negative rates were low indicating difficulty in predicting recurrence. To reduce overfitting, techniques such as augmentation, dropout layers, model checkpoints, and cross validation have been employed to improve generalization. Further steps include feature extraction from complex radiomic profiles to enhance model robustness and accuracy prediction.

Our study demonstrates the feasibility of using AI to predict brain tumor recurrence post-Gamma Knife radiotherapy. While initial results are promising, further refinement, including the addition of radiomic features and model tuning, is set to improve recurrence prediction and aid in clinical decision-making.