PAWAN KUMAR SINGH, DEEPAK TRIPATHI, ROHİT VERMA, SUKHVİR SINGH, MANİNDRA BHUSHAN, KOTHANDA RAMAN, SOUMİTRA BARIK, GOURAV KUMAR, MUNİSH GAIROLA
Turkish Journal of Oncology - 2023;38(3):333-341
OBJECTIVE The end-to-end (E2E) testing method enables understanding the difficulties and uncertainties in treating any specific case type. This study was focused on bilateral metallic implant cases. METHODS The study was performed on a cylindrical phantom of Perspex with holes for implant inserts. Two stainless steel metal rods of 7.5-8.0 g/cc mass density were inserted in the phantom. The ionization chamber CC13 was kept at a 5 cm depth in the phantom. The phantom was scanned on a computed tomography simulator in pelvis protocol with a 1mm slice thickness. The scans were imported to the contouring station without applying artifacts correction. Chamber volume was contoured as gross tumor volume (GTV); margin to GTV, clinical target volume, and planning target volume were created. Four isocentric plans (Conventional, three-dimensional conformal radiotherapy[3D-CRT], intensity-modulated radiotherapy [IMRT], and volumetric-modulated radiotherapy [VMAT]) were generated for two LinacsTruebeam (TB)-sTx and 2300-CD. The conventional plan was a single anterior field, 3D-CRT was four field box techniques, IMRT was seven field plan, and VMAT was with two complete arc. Pre-treatment verification was done using CBCT. Four plans were created on helical tomotherapy with different prescriptions and delivered using MVCT guidance. RESULTS In conventional plans, variations were -1.40%, -1.57%, and for 3DCRT, variations were -5.08% and -4.93%, for IMRT, the differences between measured and TPS doses were 1.84 % and -1.55% for VMAT plans, and the variations were 0.68% and -0.88% for TB and 2300-CD, respectively. The tomotherapy plans with gradient showed deviations more significant than 3%. Similarly, the variations for single prescription plans were within 3%. CONCLUSION The phantom design used in the test provided a comprehensive understanding of simulation and delivery problems.
