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SU‐E‐T‐208: Automated Routine 3D Secondary Patient Dose Calculation Prior to and During Fractionated Treatment
Author(s) -
Schillemans W,
Seppenwoolde Y,
Akhiat H,
Doorn X,
Kanis B,
Linton N,
Heijmen B,
Dirkx M
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4814643
Subject(s) - radiation treatment planning , dosimetry , dicom , fraction (chemistry) , nuclear medicine , computer science , medical physics , medicine , radiation therapy , radiology , artificial intelligence , chemistry , organic chemistry
Purpose: Development and evaluation of a fully automated secondary 3D patient dose calculation system to replace the conventional point‐based MU‐check and to ensure correct plan transfer to the record‐and‐verify system (RVS) and plan integrity during patient treatment. Methods: Basis is a fully automatic workflow in the RVS (MOSAIQ), developed in collaboration with the vendor. Treatment parameters (3DCRT/IMRT) in the RVS are converted into a DICOM RTPlan object. A dose engine (Oncentra), independent of the original TPS (XiO/Monaco), is then used to recalculate the 3D patient dose. The system is used for two scenarios. To verify the originally calculated dose and plan transfer, the secondary dose is compared to the TPS dose prior to the first fraction. In between fractions the dosimetric impact of changes in treatment parameters in the RVS, made deliberately (e.g., addition of imaging field) or accidentally (i.e., erroneous parameter change) is determined. In this scenario, the daily recalculated dose is compared to the secondary dose from the first scenario. In both scenarios a report is automatically generated and stored in the patient's chart. The physicist is notified in case of non‐negligible errors. Results: Although for the first scenario small differences are generally observed due to differences in dose calculation algorithms and inaccuracies in their commissioning, clinically relevant errors from our clinic or described in literature could easily be detected. With the second scenario even the smallest dosimetric differences are detected, since the same secondary dose engine is used. This leads to an improved understanding and overview of the impact of parameter changes in the RVS with minimal user interaction. Conclusion: The new automated 3D QA system replaces the MU‐check, checks the integrity of RVS treatment parameters on a daily basis, and quantifies the dosimetric impact of any changes. Without increasing workload, this contributes to increased patient safety.