Quantitative analysis of radiographic images and its application to automated diagnosis to improve healthcare efficiency

Brain tumors, particularly malignant types, pose significant challenges in diagnosis and treatment due to their rapid progression and variability among patients. Magnetic Resonance Imaging (MRI) is widely used for diagnosis, but traditional visual assessments have limitations in accurately identifying subtle features and predicting disease progression. This study aims to leverage artificial intelligence (AI) and machine learning to analyze MRI images, identifying key tumor characteristics that correlate with genomics and clinical outcomes such as treatment response, progression free survival, and overall survival. By integrating multi-source imaging datasets, including CBTN’s data, this research seeks to develop more precise diagnostic tools and predictive models for personalized treatment strategies. Access to CBTN’s pediatric brain tumor dataset is crucial, as it provides a diverse and high-quality dataset for validating the proposed AI-based analytical framework. This will facilitate the discovery of imaging biomarkers that improve diagnostic accuracy and optimize treatment decisions for pediatric brain tumor patients.