(Bloomberg Opinion) — Glioblastoma is one of the most common, aggressive and deadly forms of brain cancer. The prognosis – with a median survival of 15 to 18 months after diagnosis – has not changed in more than half a century.
A major challenge in treating glioblastoma is that tumors vary greatly between patients and the cells within them are not uniform. Too often, selecting the right combination of invasive surgery, radiation, and chemotherapy comes down to high-stakes guesswork.
A 'digital twin' of the brain tumor could help. Thanks to leaps forward in artificial intelligence and mathematical modeling over the past decade, doctors can now create a virtual copy of the affected area in a specific patient using genetic information, medical records, blood tests and scans. With a digital twin, doctors can detect abnormalities before physical symptoms appear, project how tumors will develop, and determine which combination of surgeries and treatments will produce the best results. The first results were promising.
Digital twins are leading the way in a long-awaited era of precision medicine – a major evolution from the one-size-fits-all approach to disease prevention and treatment. Today, technology may still seem closer to science fiction than reality, but with the right support, investment and safeguards, this can change.
The concept of a digital twin is not new. Digital twins have been used for decades in industrial sectors such as aerospace engineering and more recently in automotive manufacturing. Using blueprints and real-time data from sensors, engineers can approximate performance under extreme conditions (for example, a jet engine in severe weather) and predict maintenance needs. Although definitions evolve, digital twins typically require a two-way flow of information: real-world data from a physical object is put into a model, and the model predicts outcomes for the real-world object.
The potential applications of digital twins in medicine are broad. In addition to cancer, digital twins are also used to treat potentially fatal heart rhythm disorders and control type 1 diabetes. Last year, Blackstone Inc. deployed a digital twin pilot to reduce spending on expensive weight-loss medications for employees; costs fell by half for 160 people who were enrolled for at least three months. Scientists also hope to create a digital twin for clinical trials of rare diseases and improve the diversity of patient pools. Simply reducing the size of the placebo group would speed up trials, significantly reduce costs and – hopefully – bring treatments to market more quickly.
According to some estimates, the digital dual healthcare market could grow to over $20 billion by 2028, up from $1.6 billion today. But there are challenges. Unlike a jet engine, the human body has no blueprint. And while physicians collect large amounts of patient data, few electronic record systems are interconnected.
The first step towards making digital twins mainstream will be acquiring more and better data. For example, Europe is moving towards a standard health database. While replicating such efforts in the US will be challenging given the fragmentation of the market, medical engineers should build digital twins with the goal of making them interoperable across different data systems.
The computing power required to run such models will require investments. Public-private partnerships, in line with the European Commission's program to promote collaboration between industry and academia, could help cover costs. In addition, technology requires new skills that bridge the gap between engineering and medicine. Earlier this year, the Chinese government named digital twins one of six “future industries” and urged labs and research institutions to spend more than half of their core funding on scientists under the age of 35.
Privacy issues will arise. Existing data protection will have to be reexamined and adapted. Questions will arise about who owns or controls a digital twin, and how diagnoses and treatments using the technology will be priced and paid for. These numerous uncertainties should not hinder development. The potential benefits are enormous.
. from Bloomberg's opinion:
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