Can AI Reinvent Radiation Therapy for Cancer Patients?

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Of all the advances in health care artificial intelligence (AI), medical imaging is probably the most remarkable success story. Two prominent examples come to mind: Machine learning has helped improve the screening and diagnosis of retinal disease and is making inroads in skin cancer detection. Given these developments, it’s not surprising to find researchers and clinicians developing the digital tools to improve radiotherapy, which combines imaging technology with high doses of ionizing radiation, delivered through a device called a linear accelerator.

Radiotherapy is one of the most common cancer treatments, used to treat more than half of cancers, yet this labor-intensive expertise is in short supply. 1 The digital tools can meet unmet patient needs for the treatment and increase the accuracy of the delivered therapy.  

To fully appreciate the impact that AI-enhanced algorithms have on radiotherapy, it helps first to understand how the equipment and technology used to deliver radiation to a patient’s tumor functions. Ionizing radiation achieves its purpose by disrupting cellular DNA, which prevents cancer cells from growing and dividing, which in turn causes solid tumors to shrink in size. Unfortunately, the same radiation that disrupts tumor growth can also have a detrimental effect on nearby healthy tissue, resulting in various of complications.

To minimize this risk, computerized programs are employed to outline all the anatomical structures closest to the tumor to be irradiated so that the electron beam will more precisely target the tumor and spare the healthy tissues — a procedure called contouring. But there is significant disagreement among providers on how to perform the procedure. Diana Lin, with the Department of Radiation Oncology, along with several of her colleagues, point out that such variation is common and “can affect the resulting plan quality and patient outcomes.” 2Their systematic review also found that variations in target volume delineation was responsible for greater treatment toxicity and decreased survival. The medical literature also reveals that major deviations in target delineation occur in up to 13% of radiation therapy plans.

Although computer programs are available to help reduce inconsistencies and improve contouring, these digital tools are far from perfect. Chris Beltran, Ph.D., chair of the Division of Medical Physics at Mayo Clinic, Florida, points out that the relevant organs and tumors “are critical inputs for the computer models that are currently used to generate radiation dose plans. If organs are not properly identified, the radiation plan may not protect these critical structures or adequately treat the tumor.” While this computational modeling reduces the risk to healthy tissue, machine learning is now being investigated to make contouring more accurate.

Mayo Clinic and Google Health recently announced a joint initiative focusing on research into applying AI to radiation therapy planning. Radiation therapy experts from Mayo Clinic, including radiation oncologists, medical physicists, dosimetrists and service design, are collaborating with Google Health’s experts in applying AI to medical imaging. In this first stage of the initiative, Mayo Clinic and Google Health teams are using deidentified data to develop and validate an algorithm to automate the contouring of healthy tissue and organs from tumors and develop adaptive dosage and treatment plans for patients undergoing radiation therapy for cancers in the head and neck area. The goal of the IRB-approved project is to develop an algorithm that will improve the quality of radiation plans and patient outcomes while reducing treatment planning times and improving the efficiency of radiotherapy practice.

Because the head and neck contain several sensitive organs that are in close proximity to one another, the Mayo Clinic/Google project began its investigation in this area of the body. “Radiation oncologists today painstakingly draw lines around sensitive organs like eyes, salivary glands and the spinal cord to make sure radiation beams avoid these areas. And while this works well, it takes a really long time to get it exactly right,” says Cían Hughes, M.B., Ch.B., informatics lead at Google Health. “We see huge potential in using AI to augment parts of the contouring workflow, and hope that this work will ultimately enable a better patient experience and help patients get the treatment they need sooner.”

The potentially revolutionary impact of this new initiative becomes obvious when one considers the fact that virtually all linear accelerators are equipped with an open-source API, which means it may be possible for hospitals around the world to use this new technology to dramatically improve the radiological contouring and making these treatments available to underserved patient populations.


1.Thomadsen B. The shortage of radiotherapy physicists. J Am Coll Radiol. 2004 Apr;1(4):280-2. doi: 10.1016/j.jacr.2003.12.036. PMID: 17411581.

2. Lin D., Lapen, K, Sherer MV et al. A Systematic Review of Contouring Guidelines in Radiation Oncology: Analysis of Frequency, Methodology, and Delivery of Consensus Recommendations. Int J Radiology Oncol. 2020; 107:828-835.

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