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A Comprehensive Patient Specific, Structure Specific, Pre-Treatment 3D QA Protocol for IMRT, SBRT and VMAT - Clinical Experience

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G Gueorguiev

G Gueorguiev1,2*, C Cotter1,2 , M Young1,2 , D Toomeh1,2 , F Khan1 , B Crawford1 , J Turcotte1 , M Mah'D2 , G Sharp1 , (1) Massachusetts General Hospital , Boston , MA, (2) University of Massachusetts Lowell , Lowell, MA,


SU-F-T-227 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall

To present a 3D QA method and clinical results for 550 patients.

Five hundred and fifty patient treatment deliveries (400 IMRT, 75 SBRT and 75 VMAT) from various treatment sites, planned on Raystation treatment planning system (TPS), were measured on three beam-matched Elekta linear accelerators using IBA's COMPASS system. The difference between TPS computed and delivered dose was evaluated in 3D by applying three statistical parameters to each structure of interest: absolute average dose difference (AADD, 6% allowed difference), absolute dose difference greater than 6% (ADD6, 4% structure volume allowed to fail) and 3D gamma test (3%/3mm DTA, 4% structure volume allowed to fail). If the allowed value was not met for a given structure, manual review was performed. The review consisted of overlaying dose difference or gamma results with the patient CT, scrolling through the slices. For QA to pass, areas of high dose difference or gamma must be small and not on consecutive slices. For AADD to manually pass QA, the average dose difference in cGy must be less than 50cGy. The QA protocol also includes DVH analysis based on QUANTEC and TG-101 recommended dose constraints.

Figures 1-3 show the results for the three parameters per treatment modality. Manual review was performed on 67 deliveries (27 IMRT, 22 SBRT and 18 VMAT), for which all passed QA. Results show that statistical parameter AADD may be overly sensitive for structures receiving low dose, especially for the SBRT deliveries (Fig.1). The TPS computed and measured DVH values were in excellent agreement and with minimum difference.

Applying DVH analysis and different statistical parameters to any structure of interest, as part of the 3D QA protocol, provides a comprehensive treatment plan evaluation.

Funding Support, Disclosures, and Conflict of Interest: Author G. Gueorguiev discloses receiving travel and research funding from IBA for unrelated to this project work. Author B. Crawford discloses receiving travel funding from IBA for unrelated to this project work.

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