The First AI-Powered Personalized Cancer Care Delivery System in Radiation Oncology is Here

Ethos by Varian

“Personalized care” – it’s a phrase that began to bubble up in conversation among healthcare professionals and entities in the late ‘90s. It has since begun to dictate how we approach patients before, during, and after the point of care. We’ve seen it make its rounds through various disciplines and healthcare models. Examples range from Federally Qualified Health Centers (FQHCs) in rural communities seeking to make personalized care a standard of value-based care to the most specialized clinics that focus on a few very selective diseases and conditions – longing to treat patients without implementing a one-size-fits-all approach.

Cancer, across its various forms, is no different in its plea for personalized care – and now with a new breakthrough delivery care model built on personalization and powered by artificial intelligence (AI), the healthcare industry is poised to transform radiation therapy treatment as we know it.

AI: A Healthcare and Economic Revolution

Do you remember when, in 1997, reigning world chess champion Garry Kasparov was defeated by IBM’s chess playing computer program, Deep Blue? This event, among other key moments in time, gave the public a taste of what would transform into fully-formed AI. It is founded in data and ready to equip multiple industries – including those mission critical in nature – with powerful tools to enable more efficient and productive business.  

Healthcare, recognizing the potential for developing a more robust ecosystem built on improving patient treatment and experience, has gravitated towards implementing AI in both administrative and clinical functions, eagerly making room for virtual assistants, fresh diagnostic capabilities, and workflow improvements.

It certainly doesn’t hurt that combined key AI applications in healthcare – treatment-focused and otherwise – can potentially create $150 billion in annual savings for the U.S. healthcare economy by 2026. It’s no surprise then that growth in AI applications in healthcare is expected to reach $6.6 billion by 2021, a compound annual growth rate of 40%.

History in the Making

Radiation therapy is a treatment steeped in history, the seeds of its inception beginning in 1895 with the discovery of the X-ray. Fast forward over a century later, the treatment has undergone a vast number of facelifts (improvements in imaging modalities, powerful computers, and software and delivery systems with enabled technologies such as Intensity Modulated Radiotherapy, Image Guided Radiotherapy, Volumetric Arc Therapy, and Stereotactic Body Radiotherapy) leading us to where we stand today: about 60% of cancers are treated with radiation therapy.

While radiation therapy has proven to be effective, indisputable challenges have been brought to light, as the promise of better treatment approaches and outcomes via new technologies come into view. How do we better avoid affecting normal, healthy tissue during radiation therapy? What if we could more effectively tailor – or personalize – a patient’s treatment on a daily basis rather than rely on a single plan designed a few days before the first treatment? And is it possible to do it more quickly?

Enter Ethos™ therapy*, an AI-driven holistic solution designed to improve the capability, flexibility, and efficiency of adaptive radiotherapy. This new solution is ushering in the era of adaptive therapy by delivering an entire adaptive treatment in a typical 15-minute timeslot.

With Ethos, a physician can define their clinical intent from predefined templates, and the initial treatment plan is generated based on the physician’s intent. The treatment plan is then adapted in response to the variability of the tumor’s shape, along with fluctuations in the tumor’s position due to changes in nearby organs. This ability for Ethos to deliver on-couch adaptive treatment puts the patient at the center of care. 

Ethos also offers the use of multimodality images (MR, PET, CT) and daily iterative CBCT images at the console, which allows for fast imaging and treatment delivery without compromising quality. By providing an up-to-date view of the patient’s anatomy, Ethos provides clinicians the confidence to make more informed adaptive treatment decisions. 

Future of Personalized Care

The call for personalized care has grown with time, as healthcare professionals in a number of clinical settings recognize the value in tailored care for the patients they serve, whether visits are regular or intermittent. As we know, cancer patients navigating symptoms that vary with the physical changes common to the stage and location of their tumors simply cannot receive blanketed treatment. It is here that the use of AI-driven solutions like Ethos therapy are transforming radiation therapy, paving the way for more targeted treatment to improve patient outcomes, as well as streamline workflow to enable better operations and the ability to care for more patients.

Yves Archambault is a strategic initiative director at Varian, where he leads advancements in adaptive therapy through intelligent, accessible technological developments. Archambault focuses on bringing together technology, data, processes, and people to make the most complex radiotherapy solutions accessible and appealing to as many clinics as possible around the world. It’s a passion of his to not only make the most complex technology available, but to integrate it in easy-to-use products so that clinicians can use them with confidence and focus on what’s most important, the patient. 

Archambault has worked in the field of oncology for 26 years, the last 19 at Varian. Archambault has an exceptional track record in bringing novel technology to the radiotherapy market, including the introduction of the RapidArc® system in 2008 and RapidPlan® knowledge-based planning in 2014, Varian products that have transformed the way cancer treatments are planned and delivered globally.  

Archambault has a bachelor’s degree in physics and a master’s degree in biomedical engineering from Université de Montréal. 


This article was created in collaboration with the sponsoring company and our sales and marketing team. The editorial team does not contribute.