Predicting Gamma Passing Rates for Portal Dosimetry-Based IMRT QA Using Deep Learning

Abstract

generated with the same planning parameters as the original pCT-based plan. The dosimetric evaluation was performed by a quick dose recalculation on sCT relying on gamma analysis and the dose-volume histogram (DVH) parameters. The automatically delineating CTV on sCT which was rigidly registered to pCT was compared with manually delineating CTV on pCT to obtain DSC-CTV. The relationship between the ΔD95, ΔV95 and DSC-CTV was assessed to quantify the clinical impact of the geometric changes of CTV. Results: The range of the DSC and HD95 were 0.73-0.97, 2.22-9.36mm for pCT, 0.63-0.95, 2.30-19.57mm for sCT from institution A, 0.70-0.97, 2.10-11.43mm for pCT from institution B respectively. The quality of sCT was excellent with an average mean absolute error (MAE) of 71.58 § 8.78HU. The mean gamma pass rate (3%/3 mm criterion) by comparing the dose on sCT with that on original pCT was 91.46 § 4.63%. DSC-CTV down to 0.65 accounted for a variation of more than 6% of V95 and 3Gy of D95. DSC-CTV up to 0.80 accounted for a variation of less than 4% of V95 and 2Gy of D95. The mean ΔV95 of CTV was less than 6%. The mean ΔV95 of TB was more than 8%. The mean ΔD90/ΔD95 of CTV and TB were less than 2Gy/4Gy, 4Gy/5Gy for all the patients. The cardiac dose difference in left breast cancer was bigger than that in right breast cancer. Conclusion: This study demonstrates that highly accurate multi-target delineation and dose calculation are achievable using the daily CBCT image via deep learning. The results show that dose distribution needs to be considered to evaluate the clinical impact of geometric variations to decide whether to re-plan during breast cancer radiotherapy.