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Future of radiotherapy: Efficiency and accessibility

September 13, 2019
Rad Oncology Proton Therapy
By Dr. Deepak “Dee” Khuntia

While the future of radiotherapy will continuously evolve through the diligence of passionate physicians and researchers, one thing remains constant: advancements in radiation oncology will be designed and developed to allow clinicians to put patients’ needs first, always. Two such approaches — adaptive radiotherapy and FLASH therapy — will, when commercialized, prioritize treatment efficiency and accessibility to do just that.

Where are we today?
We define FLASH therapy as a noninvasive treatment that relies on external beam radiation delivered in ultra-high doses, at ultra-high speeds (less than one second), typically in one to three sessions. It will empower clinicians to treat and target cells in seconds, versus minutes. Initial preclinical research on the value of ultra-high dose rate FLASH therapy conducted by Varian and two of its clinical partners (Maryland Proton Treatment Center and Cincinnati Children’s/UC Health Proton Therapy Center), has been promising. To further study such ultra-high dose rate cancer treatments with protons, the Varian FlashForward Consortium was announced last year.

If we look at adaptive therapy, it is a treatment approach that evolves over time in response to the temporal and spatial variability of the patient’s anatomy. On an increasingly regular basis, clinicians can look at physiological changes (e.g., bladder fluctuations, changes in weight, changes in tumor size) in the patient to account for macro fluctuations in the tumor’s shape and position due to changes in nearby organs. The approach allows clinicians to reassess, adjust, and adapt the treatment plan. Today, this can be completed for certain patients, but the treatment adjustment process is a lengthy and laborious one. Aggressive and innovative work is currently being done to turn this 2 – 3-day long process into a treatment designed to enable adaptive treatments in a standard radiotherapy timeslot. If we can achieve this, the process will be easier on the patient and clinics will be able to treat more patients in the abbreviated period of time. The future of adaptive radiotherapy leverages artificial intelligence in order to segment and map out the anatomy, target where the tumor is, and optimize quality assurance of the treatment plan.

Accessibility + intelligent advancement = adoption
Once these modalities leap relevant regulatory hurdles, treatment and real-world scalability still will be imperative to clinical adoption of these approaches, and the modern-day reality is that the time, resources, and training needed to implement new approaches also are obstacles. Currently, just “airdropping” linear accelerators (LINACs) to countries around the world won’t achieve meaningful outcomes. The technology is appreciated, but without the appropriately trained personnel to manage the machines — which can be sparse in developing countries and communities — the ability to treat patients will still be limited. The industry is being challenged to develop treatment tools that decrease the time it takes to learn and understand the technology. This will streamline training and ultimately boost access to care. Knowledge-based learnings tools, or artificial intelligence, has the power to move the needle as well.

Dr. Deepak "Dee" Khuntia
Artificial intelligence tools have been in the market for several years and, looking ahead, the next step is to bring this type of technology into radiation oncology even more. Leveraging AI to better understand where the tumor is, which tissue needs to be avoided, etc., is an example of the near-term applications of intelligent radiation technology.

The future of radiotherapy will offer a personalized, flexible, and dynamic long-term treatment strategy that can offer better experiences to clinics large and small — and to patients around the globe.


About the author: Deepak “Dee” Khuntia, M.D., is the senior vice president and chief medical officer at Varian Medical Systems.

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