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Virtual QA with High Predicted Accuracy for IMRT Treatment Plans



Gilmer Valdes and Timothy Solberg 


The course of radiation treatment of cancer patients has three major phases: 1) Diagnostic

and Prescription; 2) Simulation and Quality Assurance; and 3) Delivery. In Simulation

and Quality Assurance (QA), a specific plan on how to deliver the prescribed

radiation to the tumor is developed. Penn scientists have developed a software-based

virtual Intensity Modulated Radiation Therapy (IMRT) Quality Assurance model extracts

features associated with failure modes (between the treatment planning system

and the linear accelerators) and uses machine learning to learn from pass plans in order

to accurately predict the gamma passing rates. This method could save on the time and

cost of QA, and reduce the number of replans needed.



The current most widely used technique to deliver radiation to tumors is Intensity Modulated

Radiation Therapy (IMRT). Due to potential disparities between the software used

to develop the plan and the actual delivery of radiation, each plan is measured on the

linear accelerators (Linacs) using different types of detectors before the plan is delivered

to the patient. This is costly and time consuming, as each QA procedure takes hours and

uses valuable Linacs machine life.



The algorithm developed allows use of an “off-line” method that virtually builds a database

of treatment plans, collects the gamma passing rate for each plan, and extracts features

associated with failure modes between the treatment planning system and the Linacs.




• Potential for improved efficiency and high predicted passing rates over current

measurement-based IMRT QA.

• Reduced number of replans

• Data demonstrates that virtual QA can identify plans that can be delivered with

acceptable disagreements.

• Higher utilization of Linac machine life for patient treatment.



• Radiation planning 


Intellectual Property

Provisional Pending


Reference Media

  1. Smith et al. J Bio Med, 2015, 389 (2) – 15.


Desired partnerships

• License




Download PDF


Docket #  15-7504


Patent Information:
For Information, Contact:
Jeffrey James
University of Pennsylvania
Gilmer Valdes
Timothy Solberg