Pre-Stage Diagnoses Cancer (REDMOD, EHR, PET-CT) Leveraging Pan-Cancer AI Architectures for the Prediction of Metastatic Risk and Pharmacological Response Across Diverse Malignancies: AI in Cancer Therapy

Abstract

AI-enabled pan-cancer architectures using REDMOD, EHR, and PET-CT have shown potential for pre-staging, metastasis risk, and treatment prediction in cancers. Through a combination of multimodal inputs, machine learning algorithms, transformer technology, graph network approaches, and federated learning, the research evaluates imaging biomarkers, genetic alterations, and longitudinal clinical information. These advances led to improvements in the performance metrics compared to traditional oncology approaches. AI radiomics helped in recognizing metabolic diversity and imaging patterns associated with metastasis, while EHR analytics facilitated better risk assessment and response to treatments over time. The research highlights the role of multimodal AI in precision oncology in informing decision-making, minimizing uncertainties, and enabling proactive management. Though the quality of datasets, ethical considerations, explainability, and clinical implementation remain challenges, there is promise for AI-based predictive oncology.