Performance assessment of a new optimization system for robotic SBRT MLC-based plans |
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Affiliation: | 1. IFCA Radiotherapy and Medical Physics Unit, Via del Pergolino, 1, 50139 Florence, Italy;2. AOU Careggi Medical Physics Unit, Largo Brambilla, 3, 50134 Florence, Italy;3. AOU Careggi Radiotherapy Unit, Largo Brambilla, 3, 50134 Florence, Italy;4. University of Florence, Department of Clinical and Experimental Biomedical Sciences “Mario Serio”, Viale Morgagni 50, 50134 Florence, Italy |
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Abstract: | PurposeTo assess the performance of a new optimization system, VOLO, for CyberKnife MLC-based SBRT plans in comparison with the existing Sequential optimizer.MethodsMLC-plans were created for 25 SBRT cases (liver, prostate, pancreas and spine) using both VOLO and Sequential. Monitor units (MU), delivery time (DT), PTV coverage, conformity (nCI), dose gradient (R50%) and OAR doses were used for comparison and combined to obtain a mathematical score (MS) of plan quality for each solution. MS strength was validated by changing parameter weights and by a blinded clinical plan evaluation. The optimization times (OT) and the average segment areas (SA) were also compared.ResultsVOLO solutions offered significantly lower mean DT (−19%) and MU (−13%). OT were below 15 min for VOLO, whereas for Sequential, values spanned from 8 to 160 min. SAs were significantly larger for VOLO: on average 10 cm2 versus 7 cm2. VOLO optimized plans achieved a higher MS than Sequential for all tested parameter combinations. PTV coverage and OAR sparing were comparable for both groups of solutions. Although slight differences in R50% and nCI were found, the parameters most affecting MS were MU and DT. VOLO solutions were selected in 80% of cases by both physicians with 88% inter-observer agreement.ConclusionsThe good performance of the VOLO optimization system, together with the large reduction in OT, make it a useful tool to improve the efficiency of CK SBRT planning and delivery. The proposed methodology for comparing different planning solutions can be applied in other contexts. |
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Keywords: | Plan quality Mathematical score Optimization algorithms Robotic SBRT Treatment planning technique comparison |
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