AI-Powered Simulation of Non-Uniform Radiation Dose Impact on BRCA-Mutated Tumors Using Deep Learning for Toxicity Prediction in Breast Cancer

Abstract

Purpose: This study aimed to develop a comprehensive deep learning framework for predicting radiation toxicity in breast cancer patients by integrating heterogeneous radiation dose distributions with clinical and genetic factors, with a particular focus on BRCA1/2 mutation status.

Methods and Materials:A comprehensive computer simulation was developed and conducted for a hypothetical cohort of 200 cases based on physical and biological data from scientific literature. The simulation was based on heterogeneous radiation dose distribution, clinical parameters (age, tumor stage), and genetic markers (BRCA1/2 mutation status) and the effect of BRCA mutations on DNA repair. Analyzing feature importance analysis was performed using random forest regression, and correlation matrices were generated to evaluate the relationships between features.

Results:The CNN model achieved a prediction accuracy of 87.5% in predicting radiation toxicity. BRCA mutation status emerged as the most significant prognostic factor (48.6% importance), associated with a 40.0% increased risk of toxicity. Tumor stage (34.4%) and age (13.2%) were also significant contributors. Dose distribution features showed moderate predictive value. Correlation analysis revealed strong positive relationships between BRCA status and the risk of toxicity (r=0.45, p<0.001), tumor stage and toxicity (r=0.35, p<0.01), and age and toxicity (r=0.25, p<0.05).

Conclusion:This study demonstrates the critical importance of integrating genomic information with data for accurate toxicity prediction. The results support personalized radiotherapy planning based on individual genomic profiles and provide a robust framework for identifying high-risk patients who require modified treatment protocols. The study highlights the potential of deep learning methods in advancing precision radiation oncology