Using Cancer Patient Digital Twins To Transform Cancer Care

For most of the human race, the concept of a twin is flawed. We think of identical twins as perfect carbon copies of one another, but there are many differences between them, from personalities to fingerprints and even genetics. One of a pair of identical twins could find themselves prone to cancer, while the other never even sees a single malignant cell.

One of the biggest challenges in cancer treatment is that no one-size-fits-all method works for all types. Various things cause these malignant cell mutations, so it is impossible to create a single strategy to prevent them. Cancer care and treatment is a field that is unique to each patient. Unfortunately, even in similar clinical cases, there could be variables that make it harder to guarantee a successful outcome. We never know how the treatment will work until the patient receives it.

However, the Cancer Patient Digital Twins framework could change that. Here’s what it is and how it can be used to treat cancer patients in the future.

Exploring digital twins framework

If you’re planning to rearrange a room, which sounds easier: moving all the furniture until it suits your tastes or plugging everything into a digital representation of the space to move things without lifting a finger? That is where a digital twins framework comes into play.

As its name suggests, a digital twin is a virtual representation of something in the real world. The applications for this technology are limitless. It’s found a home in workplaces, construction and now cancer treatment. A digital twin uses data inputs to craft a mimic of the subject. In building or business, you’ll see the information collected and shared by Internet of Things (IoT) sensors and beacons. In medicine, IoT focuses on a patient’s vital statistics to generate a digital twin — a computerized version of the patient.

Using digital twins to treat cancer

Unlike a biological twin, digital twins are a perfect carbon copy of the patient, down to every heartbeat and firing neuron. They can be used in real-time to “adjust treatment, monitor response and track lifestyle modifications,” according to a 2021 paper published on the subject. The current lack of real-time data can be detrimental when treating cancer patients, especially for aggressive tumors that ravage the body for weeks or months. The longer it takes to formulate a suitable treatment plan, the more likely the patient will have a poor or deadly outcome.

A digital twin framework could help predict whether a patient is predisposed to specific types of cancer in addition to monitoring them throughout their treatment. Between 5% and 10% of breast cancer cases are believed to be hereditary. Inputting that information into a digital twin framework could allow the system to predict whether the patient should be more closely monitored for breast cancer. 

The challenge of digital twin diagnostics

Digital twins are beginning to appear in various industries. However, adopting this technology for medical applications and cancer diagnostics is more challenging than it might seem at first glance. It will not mystically materialize from one lab or one programmer. It will require a concerted effort from the computational, clinical and experimental communities. The technology exists, but the programming necessary to make it work for cancer diagnostics and monitoring is still theoretical.

Digital twin frameworks will likely contribute to the rise of precision medicine in addition to improving patient prognosis after a cancer diagnosis. Real-time data creates a specific treatment plan for each patient to improve their outcomes, and it will become a standard practice rather than a relative outlier as it is today. Pairing a digital twin with a patient’s genetic information will enable these systems to generate actionable treatment options based on someone’s unique characteristics. 

One step closer to a cure?

Cancer is one of the biggest killers in the world, with more than 10 million people dying of hundreds of different varieties every year. Diagnostics are often challenging because even with regular health screenings, the signs and symptoms can lie hidden until the disease has progressed. Finding a cure might seem impossible simply because there are so many varieties and mutations cropping up every year.

Digital twin frameworks aren’t the perfect solution. However, when paired with precision medicine and advances that we’ll likely see in the next decade or two, they could move us one step closer to finding a cure for cancer. It won’t be easy, and it will require cooperation among communities that might not usually work together, but it could help save countless lives in the long run.