Emerging Areas

Biomedical Digital Twins, an innovative fusion of medical science and information technologies, is about to transform medicine and improve patient care. These new approaches to medicine are beginning to impact the fundamental understanding of biology, influence technologies available for preventing, detecting, diagnosing, and treating disease, and open ways to explore exciting new avenues to bring ever more personalized attention to the health, wellness, and well-being of individual patients.

 

The full realization of human digital twins can only succeed with contributions from the experimental, computational, and clinical communities.

 

Why a Digital Twin for Cancer and Why Now?

 

Today, cancer care teams offer patients a limited personalized view of their health trajectories, particularly when faced with varying treatment options. In the future, a patient’s digital twin (also known as an avatar or virtual patient) could be used as a holistic in silico model for cancer research, pre-clinical development, clinical trials, and other clinical settings to guide more effective and personalized treatment choices.

 

Creating digital twin technology stands as a grand challenge for the convergence of advanced computing technologies and oncology. It involves bridging spatiotemporal scales as never before—from the molecular, cellular, and tissue levels to the individual, population, and environmental levels. At each scale, agents interact with each other, and it will be necessary to identify the multitude of variables—many not currently captured systematically—that allow scales to be bridged and connected.

 

Advancing digital twin technology will benefit from dynamic, large-scale, interdisciplinary collaboration and is a major opportunity for co-design efforts integrating cancer research with artificial intelligence and advanced computing technologies.

 

Community Events and Projects are Shaping the Future for Medical Digital Twins

 

2023: The first Virtual Human Global Summit (VHGS)

The two-day event was held in New York City in October 2023 and brought together nearly 80 thought leaders from across the globe, such as the United Kingdom, Norway, Spain, India, and the United States including Native Americans. Participants shared early experiences, innovative ideas, proven approaches, and future opportunities to collaboratively advance the frontier for translating biomedical digital twin research to sustainable use in healthcare systems worldwide.

 

The Virtual Human Global Summit was co-organized by members of the Community, Kerstin Kleese van Dam (Brookhaven National Laboratory), Eric A. Stahlberg (Frederick National Laboratory for Cancer Research), and Peter Coveney (University College London).

 

The Summit has catalyzed the community to advance the development of virtual human models and medical digital twins. Several follow-on efforts are underway, including:

• Consolidation of Summit discussions and insights into a report to benefit all communities represented.
• Organizational efforts to form a Virtual Human Global Alliance, bringing the communities and stakeholders together to advance medical digital twins.
• Exploration and pursuit of new research infrastructure to facilitate the development and translation of medical digital twin approaches.
• Planning future meetings, globally, and for the next Virtual Human Global Summit.

 

Additional developments and opportunities will be announced in the coming weeks and months.

 

2022: Project Reports from the Ideas Lab

On March 4, 2022, principal investigators from the cancer patient digital twin project teams reported on their project results, challenges, and future work. Watch their presentations.

 

2020: Ideas Lab, a Five-day Interactive Workshop with Senior Mentors and Seed Funding for Five Projects

In July 2020, we held a 5-day virtual Ideas Lab, Toward Building a Cancer Patient “Digital Twin" to develop innovative cross-disciplinary collaborations and shape the future of predictive modeling across scales from biology to clinical care. Selected from over 130 applicants, 30 scientists from various career stages participated and created new, collaborative research projects with guidance from interdisciplinary mentors. In late 2020, five project teams were selected to receive seed funding—made possible by DOE and NCI through Frederick National Laboratory for Cancer Research. Three of those teams were also invited to apply for additional DOE funding.

 

Related Resources

Foundational Research Gaps and Future Directions for Digital Twins, published in December 2023 by the National Academies of Sciences, Engineering, and Medicine (NASEM). This Consensus Study Report identifies cross-sector challenges and recommendations to support this potentially transformative approach for biomedical research. Also, see the Report Highlights and other resources on the NASEM website.

 

The book, Virtual You: How Building Your Digital Twin Will Revolutionize Medicine and Change Your Life (Princeton University Press, 2023) offers context, history, and a scientific vision that biomedical researchers are exploring worldwide. Financial Times named Virtual You one of the best books of 2023! Peter Coveney, the author, is a member of the Computational Cancer Community.

 

Digital Twin Publications

Impact of Blockchain-Digital Twin Technology on Precision Health, Pharmaceutical Industry, and Life Sciences 2023 Report, Blockchain® in Healthcare Today*

 

Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation, Frontiers in Digital Health* (2022)

 

Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports, Frontiers* (2022)

 

Digital twins for predictive oncology will be a paradigm shift for precision cancer care, Nature Medicine* (2021)

* written by members of the Computational Cancer Community

 

Why is Predictive Radiation Oncology Important?

 

Radiation oncology is an area of cancer care that employs rich four-dimensional (4D) data to design and deliver highly personalized and technologically advanced treatments. Emerging approaches in physics, AI, advanced computing, and mathematical modeling can be informed by the growing wealth of 4D data. New synergies can be created to predict response at various time scales and support new treatment strategies with the potential for direct translation to the radiation oncology clinic.  

 

The typical course of radiation treatment for cancer patients takes between one day and eight weeks. This timespan creates opportunities to analyze dynamic changes and anticipate adaptive processes in cancer cells (such as radiation resistance) or to identify sensitivities of normal tissues to radiation damage.

 

The development of personalized, predictive models for these events enables adaptive, fine-tuned treatment and offers capabilities to leverage potentially vast amounts of diverse data to improve outcomes. The range of data includes areas such as circulating biomaterials, quantitative 3D imaging, and patient-reported outcomes.

 

Innovative multidisciplinary approaches that leverage advances in computing and measurement offer tremendous, untapped potential to shape the future of radiation treatments and oncology in general.

 

Moreover, radiation oncology clinical practice translates to many other areas of scientific discovery and societal impact. These include drug development, surgical practice, patient survivorship research, prevention of late effects, aeronautics and space travel, radiation safety, radiation biology, mitigation of radiation events, and disaster management.

 

Community Involvement

 

In March 2021, we held a series of interactive workshops with global experts in artificial intelligence, computing, and radiation. The Accelerating Precision Radiation Oncology through Advanced Computing and Artificial Intelligence series offered participants an opportunity to determine a roadmap for cutting-edge, multidisciplinary research that will drive the development of new paradigms in radiation oncology.

 

Read the meeting report published in Radiation Research: Predictive Radiation Oncology – A New NCI–DOE Scientific Space and Community (2022).

New Administrative Supplements for Digital Twins in Radiation Oncology

The NCI Division of Cancer Treatment and Diagnosis and the Center for Biomedical Informatics and Information Technology announce the NOSI: Administrative Supplements to Support the Development of Digital Twins in Radiation Oncology (DTRO). The goal is to support collaborative, multidisciplinary research in radiation oncology in the development of digital twins. 

 

This administrative supplement funding opportunity seeks to leverage the existing infrastructure in radiation oncology, radiation biology, and data science to facilitate new high-quality collaborative opportunities that integrate across disciplines and data scales. Read more about the grant announcement on the NIH website.

 

National Artificial Intelligence Research Resource Pilot (NAIRR)

The National Artificial Intelligence Research Resource (NAIRR) is a vision for a shared national research infrastructure for responsible discovery and innovation in AI. Launched in January 2024, the NAIRR Pilot aims to connect U.S. researchers and educators to computational, data, and training resources needed to advance AI research and research that employs AI.

 

Led by the U.S. National Science Foundation (NSF) in partnership with 10 other federal agencies (including NIH and DOE) and 25 non-governmental partners, the pilot makes available government-funded, industry and other contributed resources to support the nation's research and education community.  

 

Visit nairrpilot.org to explore opportunities for researchers, educators, and students, including AI-ready datasets, pre-trained models, and other resources associated with the NAIRR pilot.

 

You can apply now for access to NSF and Department of Energy high-performance computing facilities that have been made initially available through the NAIRR pilot for AI-related research projects. Visit nairrpilot.org/allocations for more information and the application deadline. A broader call for proposals in the spring of 2024 will provide access to the full breadth of NAIRR pilot-contributed resources.