Digital Twins in Oncology

From Computational Modeling to Precision Cancer Care
Erscheinungsjahr 2027
ISBN: 978-0-443-55755-2
Verlag: Elsevier Science

Buch, Englisch, 330 Seiten, Format (B × H): 191 mm x 235 mm

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.

Autoren/Hrsg.

Liu, Fei

He, Jie

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

Part I. Conceptual and Technical Foundations
1. Digital Twins and the Future of Precision Oncology
2. Defining the Cancer Digital Twin: Concepts, Boundaries, and Clinical Use Cases
3. Multimodal Data Foundations: Imaging, Pathology, Omics, Liquid Biopsy, EHR, and Longitudinal Outcomes
4. Computational Modeling and AI Approaches: Mechanistic, Data-Driven, Hybrid, and Foundation Models
5. Building Dynamic Patient-Specific Twins: Data Integration, Updating, Feedback, and Uncertainty

Part II. Biological and Clinical Applications
6. Modeling Tumor Growth, Evolution, Metastasis, and the Tumor Microenvironment
7. Digital Twins for Screening, Diagnosis, and Risk Stratification
8. Digital Twins for Radiotherapy, Surgery, and Image-Guided Adaptive Local Treatment
9. Digital Twins for Systemic Therapy, Immuno-Oncology, and Resistance Prediction
10. Response Monitoring, Recurrence Prediction, Clinical Trials, and Survivorship Care

Part III. Translation, Implementation, and Governance
11. Validation, Clinical Utility, Reporting, and Regulatory-Grade Evidence
12. Workflow Integration, Ethics, Governance, Equity, and Future Directions

Über Autor(innen)

He, Jie
Professor Jie He is an Academician of the Chinese Academy of Sciences, Director of the National Cancer Center (China), President of the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. A leading thoracic oncologic surgeon, he has made major contributions to the surgical and comprehensive management of lung cancer, esophageal cancer, and other thoracic malignancies, as well as to national cancer prevention, clinical research, quality control, and guideline development in China. In 2025, he was elected an International Member of the U.S. National Academy of Medicine (NAM). His leadership provides this book with a strong clinical oncology foundation and ensures that the discussion of digital twins remains connected to real cancer-care workflows, national implementation needs, and the practical translation of computational models into precision cancer care.

Liu, Fei
Professor Fei Liu is a physician-scientist at the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. His research lies at the intersection of artificial intelligence and oncology, with a focus on multimodal medical AI, tumor microenvironment biology, and digital twin approaches for precision cancer care. He has published work in leading journals, including Cell, Nature Medicine, Nature Genetics, Nature Reviews Cancer and Cell Reports Medicine.