Room: AAPM ePoster Library
Purpose: Comparison of traditional whole brain Field-in-Field (FiF) treatment plans with RADformation’s EZFluence software.
Methods: A retrospective study on dosimetric comparison and feasibility of FiF plans created by EZFluence for 22 Whole Brain plans was conducted. Treatment plans included mixed energy fields of 6 and 18 MV in the Eclipse TPS. EZFluence, an embedded script in Eclipse, allows the planner to automate the FiF process. The target and critical structures are based on user specification with the desired coverage reviewed prior to creation of a FiF plan in the software. Comparison to the original plan’s prescription dose coverage, maximum dose to the target (brain), lens, globe of the eye and total MU of each field was evaluated. Time required to create an EZFluence plan, subfield merging, and normalization was additionally documented. Independent dosimetric verification was made with RadCalc and MapCHECK2. Finally, the quality of each plan was reviewed by a physician.
Results: EZFluence produced comparable plans in a relatively shorter time. When normalized to produce the same coverage of the original plan, the dose distribution, hotspot and dose to normal tissue structures were on the average within 1% of the original plan. Total MUs increased, on average, 4.6% (14MUs). Average hotspot to homogenous plans was 106%. RadCalc was within 5% and MapCHECK2 demonstrated agreement of a passing rate of 95% (using 2%/2cm/10). Average time commitment for the creation of FiF plans through traditional steps was 7-20 minutes. A significant reduction in planning time was observed with EZFluence, with a range between 4-8 minutes. Physician reviews were comparable.
Conclusion: EZFluence generates comparable FiF whole brain plans (within 1%) to traditional planning and demonstrates a significant reduction of time. Dosimetric verification also demonstrates the feasibility of the software in the clinic. Physician plan evaluations were found to be comparable.
Dose Uniformity, Treatment Planning
TH- External Beam- Photons: Treatment planning using machine learning/Knowledge Based Planning/automation