Mitigating Chemotherapy Side Effects through Targeted Gamma-Ray Delivery and Convolutional Neural Networks: A Step Toward Precision Oncology
The systemic nature of chemotherapy results in widespread side effects that severely impact patients quality of life. This study presents a novel framework combining convolutional neural networks (CNNs) with precision gamma-ray delivery systems to selectively target malignant cells, minimizing collateral damage to healthy tissues. A ResNet-50-based CNN was trained on 12,000 annotated imaging datasets and integrated with a robotic radiation system for real-time targeting. Experimental validation on synthetic tissue models demonstrated a 92% reduction in healthy tissue damage and a 78% decrease in reported side effects. Statistical analyses confirmed model sensitivity (97.2%), specificity (94.8%), and improved treatment accuracy. This research provides a foundation for advancing personalized oncology and reducing the physical and emotional toll of chemotherapy.