"AI
(artificial intelligence) can help treatment planners and dosimetrists by
saving a lot of time doing simpler and more repetitive tasks...,"
explained Steve Jiang, Ph.D., director of the medical artificial intelligence
and automation lab of the Dept. of Radiation Oncology, University of Texas
Southwestern.
For
those unfamiliar with AI, we are referring to computer or machine intelligence
systems that can perform tasks that usually require human intelligence, such as
visual perception, speech recognition, decision-making abilities.
Discussions
about how AI will impact humanity have been occurring for many years in many
industries; however, AI has been making headway into the radiation therapy and
oncology fields within the past few years.
Two
such companies making the technological leap in AI for radiation oncology and
treatment planning are Varian and
RaySearch – both have developed machine-learning technologies to automate
treatment plans.
"The
fully automated system takes in the patient imaging and the target defined by
the physician, and out on the other end comes a fully deliverable therapy
plan," said Kevin Moore, Ph.D., DABR, deputy director of medical physics
and associate professor, University of California San Diego.
Dr.
Moore said that "The comparisons were very good," about tests that
were made when SCSD began using the software in tandem with traditional
treatment planning. After a human plan was developed, they ran the AI, and it only
took 5-20 minutes to complete depending on the complexity of the plan. UCSD has
now treated well over 1,000 patients with its AI-assisted planning.
RaySearch
has incorporated machine learning clinically since 2019. The system is trained
to take the treatment planning computed tomography (CT) scans and automatically
segment the anatomy and auto-contour to help speed the planning process.
"The
automated treatment planning system works by training the algorithm with
curated sets of similar treatment plans, and it is able to detect the patients
who are most similar to a novel patient and create a new treatment
plan...," explained Leigh Conroy, Ph.D., physics
resident, at Princess Margaret Cancer Center, who has been working on the AI
implementation.
RaySearch
is developing several other machine learning applications, including target
volume estimation and large-scale data extraction and analysis.
Other
highlights of AI technologies are adaptive AI-driven onboarding planning within
the radiotherapy system, auto contouring for treatment plans, and creating
MRI-derived CT scans for planning. To read more on these exciting
technologies, read the full article here.
