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Prediction of Gamma-Passing Rate Based On Dose Uncertainty Accumulation Model for IMRT

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E Shiba

E Shiba1,2*, A Saito3 , M Tsuneda2,4 , M Furumi1 , K Yahara5 , T Ohguri5 , Y Murakami2 , T Nishio2,4 , Y Korogi5 , Y Nagata2 , (1) Hospital of the University of Occupational and Environmental Health, Kitakyusyu, Fukuoka, (2) Hiroshima University, Hiroshima, (3) Hiroshima University Hospital, Hiroshima, (4) Tokyo Women's Medical University, Shinjuku-ku, Tokyo, (5) University of Occupational and Environmental Health, Kitakyushu, Fukuoka


SU-I-GPD-T-280 (Sunday, July 30, 2017) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: Intensity-Modulated Radiation Therapy (IMRT) produces a uniform dose distribution by patching many small fields. Therefore, there are many field junctions in the target region. It is known that such junctions require special attentions in 3DCRT since the dose uncertainty (DU) around the junction is the largest. However, quantitative evaluation of the DU has not been implemented in the quality assurance of IMRT. The purpose of this study is to develop a method to predict Gamma Passing Rate (GPR) by using the DU accumulation model.

Methods: Four head-and-neck IMRT plans (9 fields, SMLC) were used. The intensity-modulated beams were created using XiO and were irradiated by ONCOR to MapCHECK. DU distribution was generated by an in-house software which imported DICOM RT Plan and accumulated segmental-MU weighted leaf-edge positions followed by Gaussian folding. Average GPR (aGPR(DU)) for each DU was calculated for two plans (Group A) used for calculating predicted GPR (pGPR) for another two plans (Group B). The pGPR of each beam in Group B was calculated by a DU-frequency weighted mean of aGPR(DU). Measured GPR (mGPR) was then compared with pGPR. Predicting GPR accuracy was evaluated for each tolerance from 1mm (distance-to-agreement, DTA)/1% (dose difference, DD) to 3mm/3%.

Results: The frequency of the low DU was dominant in all beams. Therefore, aGPR(DU) for low DU was essential to calculate pGPR. The predicting power of GPR, (SD for pGPR–mGPR) was approximately 8%, 6%, and 4%, for DD tolerance of 1%, 2%, and 3%, respectively. It was found that the predicting accuracy did not depend on DTA tolerance.

Conclusion: We developed a method to predict GPR using DU histogram by our in-house software. It was proven that this DU-based method can provide accurate GPR-prediction for IMRT. This method also provides plan complexity prior to QA measurements.

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