Optimizing Colorectal Cancer Treatment with Unconventional Therapies: A Data-Driven AI Approach for Comprehensive Image-Based Evaluation and Treatment Ranking

By 2040, colorectal cancer (CRC) is expected to have increased incidence and fatality rates, making it a serious worldwide health concern. While successful, conventional therapies like chemotherapy and surgery can have severe side effects, which is why non-conventional therapies like photodynamic therapy, hyperthermia, and high-dose vitamin C are becoming more and more popular. Through image-based analysis of pre- and post-treatment data, this study uses artificial intelligence (AI) to evaluate the efficacy of these alternative treatments. AI assessed tumor size, shape, and biomarkers using imaging datasets (CT, MRI, and Colonoscopy) and a deep learning model. The model demonstrated strong performance with an accuracy of 0.92, precision of 0.90, recall of 0.88, and an F1 score of 0.89. These metrics indicate the model's robustness in accurately identifying and quantifying tumor features, thereby providing valuable insights into treatment responses. Our findings suggest that AI-driven analysis can significantly enhance the evaluation and personalization of colorectal cancer therapies.