TU-E-213-01: Remote Supervision

Abstract

Purpose: To develop the first 4D robust optimization (RO) method accounting for respiratory motion and evaluate its potential to improve plan robustness and optimality compared to 3D RO and PTV-based optimization. Methods: A set of 4D CT images are used to track respiratory motion and deformation of tumors and organs. For each of 10 respiration phases, dose distributions for nine different uncertainty scenarios including the nominal one, those incorporating ±5mm setup uncertainties long x, y and z directions and ±3.5% range uncertainties are calculated. All 90 dose distributions are simultaneously optimized to achieve full dose coverage of 10 CTVs and sparing of normal structures. ITV-based 3D RO and PTV-based optimization based on the average CT are also carried for the same patient using same dose volume constrains. After optimization, 4D robustness evaluation was performed for all resulting plans. The CTV coverage and the sparing of normal tissue in 10 phases are evaluated and compared among the three methods. The widths of DVH bands represent the robustness of dose distributions in the structures. Results: For one patient studied so far, the worst case CTV coverage by the prescription dose among all 90 scenarios is: 99% for 4D RO; 88.9% for 3D RO, and 85.2% for PTV based optimization. 4D RO also results in best robustness with the narrowest DVH’ bandwidths for the CTV. 4D and 3D RO have similar organ sparing while PTV based optimization results in worst organ sparing. Conclusion: 4D robust optimization which accounts for anatomy motion and deformation in the optimization process, significantly improves plan robustness and achieves higher quality treatment plans for lung cancer patients. The method is being evaluated for multiple patients with different tumor and motion characteristics.