AI-DRIVEN PRECISION CANCER ECOSYSTEMS: FROM LIQUID BIOPSY AND RADIOMICS TO DIGITAL TWINS

Abstract

Precision oncology is undergoing a profound transformation through the convergence of artificial intelligence (AI), liquid biopsy, radiomics, digital pathology, multi-omics, and digital twin technologies into intelligent computational ecosystems capable of continuously modeling cancer biology across the patient journey. Conventional oncology frequently relies on fragmented diagnostic modalities and episodic clinical assessments that inadequately capture the dynamic evolution of tumors and their interactions with the host environment. Recent advances in foundation AI models, multimodal transformers, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified computational frameworks. These intelligent ecosystems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, and digital twin simulation while facilitating evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-driven precision cancer ecosystems, emphasizing the integration of liquid biopsy, radiomics, and digital twins as a transformative paradigm for personalized cancer medicine.