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SU-G-BRC-03: A Novel Multi-Criteria Optimization Method for Volumetric Modulated Arc Therapy (VMAT) Treatment Planning.
Medical Physics 2016 June
PURPOSE: To present a novel multi-criteria optimization (MCO) solution approach that generates treatment plans for VMAT.
METHODS: We demonstrate our method with generating double-arc VMAT plans for 10 locally advanced head-and-neck cancer cases retrospectively. In our MCO formulation, we defined an objective function for each structure in the treatment volume. This resulted in 9 objective functions, including 3 distinct objectives for primary, high-risk and low-risk target volumes, 5 objectives for each of the organs-at-risk (OARs) (two parotid glands, spinal cord, brain stem and oral cavity), and one for the non-target non-OAR normal tissue. Conditional value-at-risk (CVaR) constraints were utilized to ensure at least certain fraction of the PTVs receiving the prescription doses. Besides formulating the problem as MCO, our approach directly generated weight and aperture for each control point considering MLC connectivity constraints, leaf speed, gantry speed, gantry speed change and dose rate utilizing column generation algorithm. Final dose distributions for all plans were compared after a Monte Carlo kernel-superposition dose calculation.
RESULTS: We compared our method with clinical VMAT plans and segment weight optimization (SWO) of the clinical plans. At least 95% target coverage was achieved by all plans. However, the average of the conformity indices for MCO was 1.28 comparing to clinical VMAT 1.83 (30% reduction, p<0.01) and SWO 1.63 (22% reduction, p<0.01). MCO method achieved reduction in right parotid mean dose of 17% (p<0.01) and 10% (p=0.03), oral cavity mean dose of 23% (p<0.01) and 6% (p=0.34) and spinal cord maximum dose of 20% (p<0.01) and 10% (p<0.01), brain stem maximum dose of 59% (p<0.01) and 57% (p<0.01), and normal tissue maximum dose of 4% (p<0.01) and 5% (p<0.01), comparing to clinical VMAT and SWO respectively.
CONCLUSION: We demonstrated that the proposed MCO method was able to obtain VMAT plans with significant improvement in dosimetric plan quality.
METHODS: We demonstrate our method with generating double-arc VMAT plans for 10 locally advanced head-and-neck cancer cases retrospectively. In our MCO formulation, we defined an objective function for each structure in the treatment volume. This resulted in 9 objective functions, including 3 distinct objectives for primary, high-risk and low-risk target volumes, 5 objectives for each of the organs-at-risk (OARs) (two parotid glands, spinal cord, brain stem and oral cavity), and one for the non-target non-OAR normal tissue. Conditional value-at-risk (CVaR) constraints were utilized to ensure at least certain fraction of the PTVs receiving the prescription doses. Besides formulating the problem as MCO, our approach directly generated weight and aperture for each control point considering MLC connectivity constraints, leaf speed, gantry speed, gantry speed change and dose rate utilizing column generation algorithm. Final dose distributions for all plans were compared after a Monte Carlo kernel-superposition dose calculation.
RESULTS: We compared our method with clinical VMAT plans and segment weight optimization (SWO) of the clinical plans. At least 95% target coverage was achieved by all plans. However, the average of the conformity indices for MCO was 1.28 comparing to clinical VMAT 1.83 (30% reduction, p<0.01) and SWO 1.63 (22% reduction, p<0.01). MCO method achieved reduction in right parotid mean dose of 17% (p<0.01) and 10% (p=0.03), oral cavity mean dose of 23% (p<0.01) and 6% (p=0.34) and spinal cord maximum dose of 20% (p<0.01) and 10% (p<0.01), brain stem maximum dose of 59% (p<0.01) and 57% (p<0.01), and normal tissue maximum dose of 4% (p<0.01) and 5% (p<0.01), comparing to clinical VMAT and SWO respectively.
CONCLUSION: We demonstrated that the proposed MCO method was able to obtain VMAT plans with significant improvement in dosimetric plan quality.
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