The impact of Velocity at University of Virginia

October 22 2010  |  News Article 

VelocityAI is instrumental in a research project at the University of Virginia designed to treat these patients much faster 

The University of Virginia’s (UVA) radiation oncology department initially acquired VelocityAI™ for a research project to plan palliative treatments faster and bring quicker relief to patients in pain. Now that UVA has the ability to sum doses from multiple plans, contour tumors more accurately, and consolidate data, VelocityAI is having a far greater impact on the practice.

Precontouring using diagnostic images
Radiotherapy is very effective at relieving pain, but the time it typically takes to plan palliative treatment can be excruciatingly long for patients who are suffering. VelocityAI is instrumental in a research project at the University of Virginia designed to treat these patients much faster—on the same day as their first radiation oncology appointment, in fact.

“Using the diagnostic images, we precontour the tumors and organs at risk,” explains Ke Sheng, PhD, DABR, associate professor in the UVA radiation oncology department. “When we see the patient for first time, we take a new CT scan, fuse the data sets, migrate the precontours into the new CT image, create a very fast plan, and treat the patient the same day to provide the fastest possible pain relief. Velocity is indispensable in this process.” So far, 25 patients have been treated under this research protocol, and many of them have reported significant symptom relief after the first fraction.

Solving key RT problems
As word spread about VelocityAI within the UVA radiation oncology department, more doctors began to use it to solve other problems. For example, they are using VelocityAI to calculate composite doses from multiple plans. “To accurately sum the radiation doses, we need to perform a deformable registration of images taken in different positions on different imagers at different times,” says Sheng. “Velocity can do that for us.” According to Sheng, the ability to sum doses and quantify the highest dose to normal tissue has caused UVA doctors to change planning objectives in some cases.

UVA radiation oncologists are using VelocityAI’s deformable registration capability to contour tumors more accurately based on PET scans of metabolic activity. “PET scans are taken in Radiology on a curved couch, and our CT simulation scans are taken on a flat table,” explains Sheng. “Without Velocity, we used to force these images to agree with each other at the midpoint. With Velocity, we can fuse these images more accurately, which gives us a much better idea of the tumor size and location.”

Doctors are also discovering that VelocityAI saves contouring time when they need to replan treatments. “For certain patients, it becomes harder after a few fractions to align them to the treatment planning CT because they have changed so much,” says Sheng. When this happens, the daily kV images or cone-beam CTs are exported to VelocityAI and compared to the original treatment planning CT. In one case, a head and neck patient required a new plan due to swelling. “Using Velocity saved a lot of time,” Sheng recalls. “We transferred the original contours over to the new CT and created a new plan with minimal modification.”

In the 10 months since UVA acquired VelocityAI for its research project, interest in the software has grown rapidly. It has already been used in the treatment of more than 300 patients. “Velocity definitely has made a great impact on our practice,” says Sheng.
 
Integrated information management
Velocity may have an even greater impact as an information management solution for radiation oncology. “We use different images for different purposes, but eventually we want to understand what’s the total dose delivered to the patient,” explains Sheng. The data is scattered and often difficult to locate, however, and the department needs a system for managing all the different data sets. Says Sheng, “We think Velocity could become that system.”

“Velocity definitely has made a great impact on our practice.”

Ke Sheng, UVA

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