Digital Twins in Oncology

Computational Biology in Oncology: From Computational Modeling to Precision Cancer Care presents a comprehensive exploration of cancer digital twins as dynamic, patient-specific computational models that support precision oncology. The book examines how computational biology, artificial intelligence, and multimodal clinical data can be integrated to improve cancer diagnosis, treatment planning, and patient outcomes. The book covers the conceptual foundations of oncology digital twins, including data integration, computational modeling, machine learning, radiomics, digital pathology, multi-omics, and longitudinal patient monitoring. It further explores applications across the cancer care continuum, from risk stratification and treatment simulation to response monitoring, resistance prediction, and survivorship care. Dedicated chapters address validation, uncertainty, ethics, governance, and clinical implementation. Readers gain a clear understanding of how computational models can be translated into clinically meaningful decision-support tools. The book serves as a valuable resource for researchers, clinicians, and students seeking to bridge computational innovation and real-world precision cancer care.

• Guides translation from computational modeling to clinical implementation, validation, and decision support
• Integrates multimodal clinical, molecular, imaging, and longitudinal data within a unified computational framework
• Defines and clarifies the emerging field of oncology digital twins and their relationship to precision medicine