Optimization of stereotactic body radiotherapy treatment planning using a multicriteria optimization algorithm.

PURPOSE: To provide high-quality and efficient dosimetric planning for various types of stereotactic body radiotherapy (SBRT) for tumor treatment using a multicriteria optimization (MCO) technique fine-tuned with direct machine parameter optimization (DMPO).

METHODS AND MATERIALS: Eighteen patients with lung (n=11), liver (n=5) or adrenal cell cancer (n=2) were treated using SBRT in our clinic between December 2014 and June 2015. Plans were generated using the RayStation™ Treatment Planning System (TPS) with the VMAT technique. Optimal deliverable SBRT plans were first generated using an MCO algorithm to find a well-balanced tradeoff between tumor control and normal tissue sparing in an efficient treatment planning time. Then, the deliverable plan was post-processed using the MCO solution as the starting point for the DMPO algorithm to improve the dose gradient around the planning target volume (PTV) while maintaining the clinician's priorities. The dosimetric quality of the plans was evaluated using dose-volume histogram (DVH) parameters, which account for target coverage and the sparing of healthy tissue, as well as the CI100 and CI50 conformity indexes.

RESULTS: Using a combination of the MCO and DMPO algorithms showed that the treatment plans were clinically optimal and conformed to all organ risk dose volume constraints reported in the literature, with a computation time of approximately one hour. The coverage of the PTV (D99% and D95%) and sparing of organs at risk (OAR) were similar between the MCO and MCO+DMPO plans, with no significant differences (p>0.05) for all the SBRT plans. The average CI100 and CI50 values using MCO+DMPO were significantly better than those with MCO alone (p<0.05).

CONCLUSIONS: The MCO technique allows for convergence on an optimal solution for SBRT within an efficient planning time. The combination of the MCO and DMPO techniques yields a better dose gradient, especially for lung tumors.