PALO ALTO, Calif. - Treatment planning for radiotherapy and radiosurgery can be significantly expedited and improved using new software for storing and accessing clinical knowledge based on best planning practices. This was the finding of several research teams that compared conventional plans with plans generated using knowledge-based treatment planning software like the RapidPlan™ tool from Varian Medical Systems (NYSE: VAR). The researchers from diverse institutions recently presented their findings during the 2015 American Association of Physicists in Medicine (AAPM) annual meeting.
Plan generation that typically took 30 to 180 minutes was completed in just 15 to 20 minutes using a model based on 48 spine tumor cases, created by Joy Foy, MSE, and colleagues from the University of Michigan. "RapidPlan knowledge-based planning greatly decreased the amount of time required to achieve high quality treatment plans with minimal human intervention and could feasibly be used to standardize plan quality between institutions," the researchers found.1
RapidPlan enables clinicians to extract information from past clinical experience and use it to generate mathematical models that expedite the creation of new treatment plans. The software helps the planner quickly generate a new treatment plan that achieves the radiation oncologist's tumor coverage and normal tissue sparing goals, greatly reducing the need for time-consuming, manual trial-and-error processes. Knowledge-based treatment planning is becoming a routine practice for quality control, according to researchers from the University of Texas MD Anderson Cancer Center in Houston and Duke University Medical Center in Durham, NC. 2
Munther Ajlouni, MD; Karen Snyder, MS; and their colleagues at the Henry Ford Health System in Detroit, MI, developed a RapidPlan model based on 105 manually created SBRT lung cancer treatment plans. They found that RapidPlan generated treatment plans with comparable quality to manually created plans but with increased consistency and greater efficiency.3
Jason Pawlowski, PhD, medical physicist at Sarah Cannon, presented work from Sarah Cannon radiation oncology site colleagues across the nation aimed to develop and validate a knowledge-based planning model for treating locally advanced non-small cell lung cancer. The Sarah Cannon radiation oncology team found that the RapidPlan model more quickly achieved treatment plans that were equivalent, or superior to, previously-created manually optimized plans of the same patients.4
Changsheng Ma, MD and Yong Yi, MD from the Shandong Tumor Hospital in Jinan, Shangdong Province, China, used RapidPlan and data from 20 patient cases to develop a RapidPlan model for treating cervical cancer with IMRT. This approach "can generate clinically acceptable treatment plans of high quality, while improving the efficiency of the treatment planning process." they reported. 5
Funding Support, Disclosures, and Conflict of Interest: The Zhang, Foy, and Snyder presentations were based on work supported by Varian Medical Systems.
About Varian Medical Systems
Varian Medical Systems, Inc., of Palo Alto, California, focuses energy on saving lives by equipping the world with advanced technology for fighting cancer and for X-ray imaging. The company is the world's leading manufacturer of medical devices and software for treating cancer and other medical conditions with radiation. The company provides comprehensive solutions for radiotherapy, radiosurgery, proton therapy and brachytherapy. The company supplies informatics software for managing comprehensive cancer clinics, radiotherapy centers and medical oncology practices. Varian is also a premier supplier of X-ray imaging components, including tubes, digital detectors, and image processing software and workstations for use in medical, scientific, and industrial settings, as well as for security and non-destructive testing. Varian Medical Systems employs approximately 6,800 people who are located at manufacturing sites in North America, Europe, and China and approximately 70 sales and support offices around the world. For more information, visit www.varian.com or follow us on Twitter.
1 Foy J, et al. An analysis of knowledge based planning for stereotactic body radiation therapy of the spine [abstract. Poster presentation at: American Association of Physicists in Medicine (AAMP) 57th Annual Meeting Exhibition; July 12-16, 2015; Anaheim, CA.
2 Zhang X, Wu Q. Knowledge-based treatment planning automation [abstract. Presented at: American Association of Physicists in Medicine (AAMP) 57th Annual Meeting Exhibition; July 12-16, 2015; Anaheim, CA.
3 Snyder K, et al. Development and evaluation of a knowledge-based model for treatment planning of lung cancer patients using stereotactic body radiotherapy (SBRT) [abstract. Presented at: American Association of Physicists in Medicine (AAMP) 57th Annual Meeting & Exhibition; July 12-16, 2015; Anaheim, CA.
4 Liu Z, et al. Development and validation of a knowledge based planning model for external beam radiation therapy of locally advanced non-small cell lung cancer [abstract. Poster presentation at: American Association of Physicists in Medicine (AAMP) 57th Annual Meeting & Exhibition; July 12-16, 2015; Anaheim, CA.
5 Ma C, Yin Y. The feasibility of using a knowledge base of prior treatment plans in cervical cancer: a dosimetric comparision with original plans [abstract. Poster presentation at: American Association of Physicists in Medicine (AAMP) 57th Annual Meeting & Exhibition; July 12-16, 2015; Anaheim, CA.
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