Integrated IS Speeds Workflow, Delivers Quality Care in Radiation Oncology

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HIIT070805In the wake of an explosion of oncology-related imaging, many hospitals have now set up separate radiation oncology information systems that not only store images but also keep track of dosage, planning and treatment data.

Traditionally, PACS have only stored medical images. But the kind of medical objects that radiation oncologists deal with are more than just images; they also include dose distributions, treatment summaries and details of the treatment delivery.

The original DICOM specification was for images only; however, the standard was designed so that it could be extended for other data objects. One of the first extensions, DICOM-RT, was designed to accommodate radiation therapy—it was adapted to store information about doses, plans and anatomical outlines.

The implementation of DICOM-RT and imaging-based simulation tools, using modalities such as CT and PET/CT, has eliminated many of the manual inefficiencies that used to plague radiation oncology departments.

Sam Brain, PhD, a senior research associate in the department of radiation oncology at Stanford University’s School of Medicine in Stanford, Calif., says CT scans ordered by radiation oncologists are kept in-house and stored on the department’s radiation oncology information system (ROIS).

Automation integration
According to Brain, Stanford’s Aria (Varian Medical Systems) ROIS stores the multiple parameters required for each patient’s scan: gantry angle, jaw settings and table settings.

“There’s maybe a dozen or so knobs to be twirled before you actually deliver the treatment to the patient. So if you’re doing 100 patients per day and each patient has 30 fractions, that’s a lot of knob twirling to be done, and people make mistakes,” says Brain.

“One of the first things they did when they computerized this process was set up a Record and Verify system,” he says. “The first time you set up a patient it would record the knob settings, and subsequently when you gave the next fraction of treatment, it would check that you set the knobs correctly.”

Stanford’s ROIS also can control multileaf collimators, which aids radiation oncologists in delivering the proper accelerator treatment.

“People started using the computer system for more things,” Brain observes. “It has now morphed into a complete ROIS where we schedule on it, store the CTs in it and store all the anatomical information.”

CT images are used to outline the internal structures of the body, locate tumors and help determine treatment volumes. Prior to the automation of this function, radiation oncologists were drew their treatment contours and calculate treatment fractions by hand. With Varian’s treatment planning software, these processes are optimized at the workstation and the treatment details are stored in the ROIS.

All radiation oncology information is stored on a central server, which contains medical images that can be imported from the radiology department, including details of internal anatomy, dose details, treatment plans and galleries of the radiation of the tumor. Radiation oncologists look at these so-called “portal” images to determine if they are “hitting” the tumor, Brain says.

Stanford’s Aria system has greatly helped to unite radiation oncology care, he asserts. “It means that a physician can go to a terminal anywhere in the dept, sometimes from home or on the road, and by using properly protected communications protocols, can check some details of the [treatment plan for a] patient,” Brain notes. “So the fact that it’s all in one place is fantastic and has really helped our workflow.”

It’s a big improvement over former piecemeal systems made my different vendors, he says. Aria is able to communicate with other systems in the department—such as its PET/CT simulator or CT simulator—and pull out the details needed to make treatment calculations, Brain says.

Clinical flows are quite complicated, Brain says, and involve scanning, outlining, planning and treating. Data flow is a multistep process: CT images are fed into the central server; the outlining workstation pulls those images out and a specialist will outline tumor margins; this data are then saved on the central server; the planning system then pulls the images and outlined tumor margins and a plan is calculated by an oncologist; finally, at the accelerator, the plan is downloaded and used for treatment.

“Everything to and from a central server is the optimal way of doing things,” states Brain, adding that having all components