Digital Twins in Renal Cell Carcinoma: Toward Personalized Therapy

Abstract

Background: Renal cell carcinoma (RCC) is the most common primary malignancy of the kidney and accounts for approximately 90% of all renal cancers. Despite remarkable advances in molecular diagnostics, targeted therapies, immune checkpoint inhibitors, minimally invasive surgery, and precision medicine, considerable heterogeneity in tumor biology and therapeutic response continues to challenge individualized patient management. Renal cell carcinoma encompasses multiple histological and molecular subtypes, each characterized by distinct genomic alterations, metabolic reprogramming, immune microenvironment interactions, angiogenesis, and variable sensitivityto systemic therapies. Recent developments in artificial intelligence (AI), digital twin technology, computational oncology, and multimodal biomedical data integration have introduced transformative opportunities for personalized renal cancer care. A digital twin is a continuously evolving virtual representation of an individual patient that integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, laboratory biomarkers, physiological monitoring, electronic health records, and longitudinal clinical information to simulate tumor progression and therapeutic response. Unlike conventional predictive models based on static datasets, digital twins continuously update as new clinical information becomes available, enabling individualized diagnosis, prognostic prediction, treatment optimization, toxicity assessment, recurrence monitoring, and survivorship management. Advances in machine learning, deep learning, transformer architectures, graph neural networks, foundation models, reinforcement learning, and explainable artificial intelligence have significantly accelerated the development of digital twin technology in renal oncology. These intelligent computational systems demonstrate growing applications in diagnostic imaging, radiogenomics, immunotherapy prediction, nephronsparing surgery, adaptive systemic therapy, clinical trial optimization, and precision drug development. Nevertheless, important challenges remain regarding data interoperability, computational scalability, ethical governance, cybersecurity, regulatory validation, and widespread clinical implementation. This review provides a comprehensive overview of digital twin technologies in renal cell carcinoma, highlighting computational foundations, clinical applications, emerging innovations, and future perspectives toward truly personalized therapy.