Abstract
Cancer is increasingly recognized as a dynamic biological ecosystem characterized by continuous interactions among malignant cells, immune populations, stromal components, vascular networks, extracellular matrix, molecular signaling pathways, and host physiological systems. Conventional oncology frequently analyzes these biological components independently, limiting comprehensive understanding of tumor evolution and therapeutic response. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, and generative AI, have enabled the development of predictive cancer ecosystems that integrate radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified computational frameworks. These intelligent ecosystems model tumor evolution continuously, enabling biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging innovations including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen predictive oncology by enabling collaborative, privacy-preserving, and continuously adaptive computational medicine. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of artificial intelligence for predictive cancer ecosystems, emphasizing multimodal computational modeling of tumor evolution as a transformative paradigm in precision oncology.[1]
Keywords: Predictive oncology, Artificial intelligence, Cancer ecosystems, Foundation models, Multimodal learning, Digital twins, Precision oncology, Computational oncology, Tumor evolution, Clinical decision support.
