Imagine a future where an oncologist could assess your response to multiple treatment options using a virtual simulated model of your tumor and organ systems. This is the future of cancer care — digital twins — where technology and innovation come together to pave the way for more precise and personalized treatment. 

“Digital twins hold great promise in accelerating scientific breakthroughs and transforming how we approach oncology discovery and precision medicine,” says Caroline Chung, M.D., vice president for Data Impact and Governance, and chief data and analytics officer. “While this is still emerging technology that is not yet ready for use in the clinic, digital twins have the potential to inform patients and health care providers to guide medical decisions, as well as to assist researchers who are discovering and developing better detection methods and treatments for cancer.” 

What are digital twins?

Digital twins were first introduced in aerospace engineering, but since then the term has found its way into many industries, including health care.  

So, what’s a digital twin? A digital twin is a virtual model that represents a real-world object, system or process. It continuously receives data from its real-world counterpart, allowing it to predict future outcomes and help make better decisions. The key feature of a digital twin is its ability to interact with the physical version, creating a two-way connection between the virtual and the real world.  

There are many different applications of digital twins in hospitals and health care systems – from optimizing resources and operational workflows to training and education. Digital twins also can enhance and elevate research and discovery of new drugs and treatments while deepening our understanding of diseases.  

When it comes to clinical care and cancer treatment, a digital twin could represent a patient’s tumor and organ systems based on an individual patient’s data, such as from clinic assessments, genetic results, and imaging and lab tests. This digital model could be used to simulate how a tumor may respond to various combinations of chemotherapy, radiation and immunotherapies in a virtual environment. Then the patient and care team could determine the most promising options that have the best outcomes with minimal risks to the patient. The digital twin can continue to journey with the patient and be updated as their twin to guide ongoing decisions about further treatments, follow-up visits and survivorship.   

Chung also acknowledges how digital twins could offer a new level of transparency, education and understanding for patients and caregivers, like those she sees in clinic as a professor and radiation oncologist, with detailed insights and shared considerations into clinical decisions and the treatment process. 

“With digital twins, patients could better understand their condition and the rationale behind their treatment plan,” she says. “This knowledge could foster a stronger sense of confidence and empowerment, helping patients and caregivers make informed decisions about their care.”  

Challenges and future directions

While digital twins hold significant promise for oncology treatments, there is still a lot of work to be done to realize their full potential and bring them to patients in a safe and effective way.  

“Challenges exist in all areas to mature digital twins in health care, including mathematics and modeling development, data quality and flow, operational implementation and workforce training,” Chung adds. “Beyond the technical and operational maturation, we need to consider the privacy and ethical considerations as well as better understand how these kinds of tools will influence our decision-making processes to ensure the safety, integrity and effectiveness of digital twin technology in oncology.” 

One major challenge is determining the best way to measure the quality and performance of digital twins, which can vary widely depending on the quality, availability and understandability of the data. A patient will typically receive care in multiple settings. So, for their digital twin to journey with them and be continually updated, the data from each setting will need to be able to feed into the digital twin and be of sufficient quality to ensure the predictions are meaningful. Enabling this connectivity of data while guaranteeing patient privacy and data security is a critical yet unresolved challenge.  

As co-director of MD Anderson’s Institute for Data Science in Oncology (IDSO), Chung is deeply involved in how teams at MD Anderson are pursuing this technology. 

“IDSO integrates data science methods across our organization to support the pursuit of leveraging digital twins,” she says. “We have researchers developing computational models that will help us on our journey to building digital twins to identify optimal treatments for individual patients. Others are developing the pipelines needed to allow for the necessary flow of high-quality data, and there are teams investigating how to best support human decision-making with this emerging technology.” 

To take full advantage of all the developments of digital twins, we need collaboration across disparate fields — aerospace, engineering, climate science, cognitive science, social science and others — banded together by common challenges and goals of bringing digital twins to improve the health and wellness of people. Chung, along with experts from different institutions within academia, labs and industry, have begun to foster this collaboration through the National Academies of Sciences, Engineering and Medicine and a recent consensus study report, Foundational Research Gaps and Future Directions for Digital Twins. The purpose of the report was to call out the gaps and foundational needs for digital twins’ research across domains so that promising practices from one area of expertise can be identified and translated to others. 

“As research and technology continue to advance and collaborations grow in this space, digital twins will transform the landscape of cancer care and much more,” says Chung. “I’m confident this innovation will make meaningful improvements to the journey through cancer and bring hope to the patients we’re privileged to care for and their loved ones.” 

Learn about MD Anderson’s Institute for Data Science in Oncology.

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