Deep Learning Tools Can Kickstart Cancer Radiation Therapy

29Jan - by aiuniverse - 0 - In Deep Learning


January 28, 2020 – New research from UT Southwestern has shown that deep learning technology could help providers quickly develop optimal treatment plans for cancer patients, decreasing the odds that the disease will spread.

Patients usually have to wait several days to a week to begin therapy while doctors manually develop treatment plans, which can be a tedious, time-consuming process. Providers must carefully review a patient’s imaging data and conduct several phases of feedback within the medical team.

Delaying radiation therapy for even a week can increase the chance of some cancers recurring or spreading by 12 to 14 percent, researchers noted.

“Some of these patients need radiation therapy immediately, but doctors often have to tell them to go home and wait,” said Steve Jiang, PhD, who directs UT Southwestern’s Medical Artificial Intelligence and Automation (MAIA) Lab. “Achieving optimal treatment plans in near real time is important and part of our broader mission to use AI to improve all aspects of cancer care.”

The team explored how AI and deep learning tools could improve multiple aspects of radiation therapy, from initial dosage plans required before the treatment can begin, to the dose recalculations that occur as the plan progresses.

Researchers used data from 70 prostate cancer patients to train four deep learning models. The tools learned to develop 3D renderings of how to best distribute the radiation in each patient.

Each model accurately predicted the treatment plans developed by the medical team, and the technology was able to produce optimal treatment plans within five-hundredths of a second after receiving clinical data for patients.

“Our AI can cut out much of the back and forth that happens between the doctor and the dosage planner,” Jiang said. “This improves the efficiency dramatically.”

Jiang also led a second study that showed how AI can quickly and accurately recalculate dosages before each radiation session, taking into account how a patient’s anatomy may have changed since the last therapy. A traditional, accurate recalculation can require patients to wait up to ten minutes or more, in addition to the time needed to conduct anatomy imaging before each session.

Jiang and his team developed an AI model that combined two conventional models used for dose calculation: a simple, fast model that lacked accuracy, and a complex one that was accurate but required more time.

The newly developed AI technology assessed the differences between the models, and learned how to utilize both speed and accuracy to produce calculations within one second.

UT Southwestern plans to use these new deep learning and AI capabilities in clinical care after implementing a patient interface. The MAIA Lab is also currently developing deep learning tools for several other purposes, including enhanced medical imaging and image processing, automated medical procedures, and improved disease diagnosis and outcome prediction.

Researchers have taken an interest in using AI to improve radiation therapy for patients. A team from the University of Texas MD Anderson Cancer Center recently developed a machine learning tool that could accurately predict two of the most challenging side effects of radiation therapy for patients with head and neck cancers: significant weight loss or the need for a feeding tube.

The technology could help providers deliver more proactive care for patients with cancer.

“Being able to identify which patients are at greatest risk would allow radiation oncologists to take steps to prevent or mitigate these possible side effects,” said Jay Reddy, MD, PhD, an assistant professor of radiation oncology at The University of Texas MD Anderson Cancer Center and lead author on the study. 

“If the patient has an intermediate risk, and they might get through treatment without needing a feeding tube, we could take precautions such as setting them up with a nutritionist and providing them with nutritional supplements. If we know their risk for feeding tube placement is extremely high, we could place it ahead of time so they wouldn’t have to be admitted to the hospital after treatment. We’d know to keep a closer eye on that patient.”

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