ABSTRACT
PURPOSE: Due to limited field size of Magnetic Resonance Linear Accelerators (MR-Linac), some treatments could require a dual-isocenter planning approach to achieve a complete target coverage and thus exploit the benefits of the online adaptation. This study evaluates the dosimetric accuracy of the dual-isocenter intensity modulated radiation therapy (IMRT) delivery technique for MR-Linac. MATERIAL AND METHODS: Dual-isocenter multi leaf collimator (MLC) and couch accuracy tests have been performed to evaluate the delivery accuracy of the system. A mono-isocenter plan delivered in clinical practice has then been retrospectively re-planned with dual-isocenter technique. The dual-isocenter plan has been re-calculated and delivered on a 3-dimensional (3D) ArcCHECK phantom and 2-dimensional (2D) films to assess its dosimetric accuracy in terms of gamma analysis. Clinical and planning target volume (CTV and PTV respectively) coverage robustness was then investigated after the introduction of ± 2 mm and ± 5 mm positioning errors by shifting the couch. RESULTS: MLC and couch accuracy tests confirmed the system accuracy in delivering a dual-isocenter irradiation. 2D/3D gamma analysis results occurred always to be above 95% if considered a gamma criteria 1%/2 mm and 1%/1 mm respectively for the 2D and 3D analysis. The mean variations for CTV D98% and PTV V95% were 0.2% and 1.1% respectively when positioning error was introduced separately in each direction, while the maximum observed variations were 0.9% (CTV) and 3.7% (PTV). CONCLUSION: The dosimetric accuracy of dual-isocenter irradiation has been verified for MR-Linac, achieving accurate and robust treatment strategy and improving dose conformality also in presence of targets whose extension exceeds the nominal maximum field size.
Subject(s)
Abdominal Neoplasms , Radiotherapy, Intensity-Modulated , Humans , Magnetic Fields , Magnetic Resonance Spectroscopy , Radiotherapy Dosage , Radiotherapy Planning, Computer-Assisted , Retrospective StudiesABSTRACT
AIM: Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. MATERIALS & METHODS: The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org . RESULTS: A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables. CONCLUSION: The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.
