Evaluation of Radiation Treatment Planning Algorithms in IMRT and VMAT: A Comparative Dosimetric Study in Lung Equivalent Heterogeneous Medium

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Background
In Radiotherapy, computation of dose is important since in a small field with heterogeneity, dose is usually computed with discrepancies.
Objective
The present study was aimed to evaluate the dosimetry of treatment planning algorithms in lung equivalent heterogeneous medium for Volumetric Modulated Arc Therapy (VMAT) with step and shoot Intensity-Modulated Radiation Therapy (ss-IMRT), and dynamic Intensity-Modulated Radiation Therapy (d-IMRT).
Material and Methods
In this experimental study, Computerized Imaging Reference System (CIRS) phantom was used with an inhomogeneous Racemosa wood cylinder for two types of tumors, namely, Left Lung Central Tumor (LCT) and Left Lung Peripheral Tumor (LPT) in the CIRS left lung cavity. The computed tomography (CT) datasets were employed with the generation of VMAT, d-IMRT and ss-IMRT plans for the LCT and LPT irradiated with 6 MV photon beams. In this study, the accuracy and efficacy of two algorithms: Monte Carlo (MC) and the Pencil Beam (PB), from the Monaco treatment planning system (TPS), were tested by using Gafchromic EBT3 films and CIRS thorax phantom.
Results
Regardless of treatment techniques, both algorithms exhibited higher divergence in LPT than LCT. In both LCT and LPT, the highest deviation was near the tumor-lung junction. However, the deviation was higher in the PB algorithm than MC algorithm, with a minimally acceptable variation of -0.8%. 
Conclusion
The MC algorithm shows more consistency for EBT3 measured dose in lung equivalent heterogeneous medium. However, accurate dose predictions are complicated due to electronic disequilibrium within and at the interface of inhomogeneity. These constraints may cause variations from the anticipated outcomes of the treatments.
Language:
English
Published:
Journal of Biomedical Physics & Engineering, Volume:13 Issue: 6, Nov-Dec 2023
Pages:
503 to 514
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