Approximation of dose quality indicator values in multi-criteria optimized (MCO) volumetric modulated arc therapy...

To approximate dose-volume histogram (DVH) based quality indicators in volumetric modulated arc therapy (VMAT) planning using multi-criteria optimization (MCO) with a low number of composite optimization parameters.

The solution space for VMAT optimization with a low number of composite optimization parameters is approximated by trilinear dose inter- polation and prediction of dose-volume-histogram (DVH) based plan quality indicator values. To assess the approximation quality a diverse dataset of 44 cranial and 18 spine patient geometries was chosen. Optimization results are governed by three composite parameters focusing on target-organ-at-risk- (OAR)-trade-off, overall healthy tissue sparing, and delivery/quality assurance complexity. 21,266 optimized dose distributions were pre-calculated and the numerical values for a choice of 10 DVH points, referred to as plan quality indicators, were stored to serve as ground truth. Using a subset of 8 and 27 pre-calculated optimization results, dose distributions for unknown parameter values were approximated by trilinear interpolation. The resulting quality indicator values were compared to the previously obtained exact solutions.

The magnitude of the deviation between exact and approximated values varied largely with respect to patient geometry and the criterion under investigation. Approximation with 27 pre-calculated results yielded lower deviations than approximation with 8 results, at the cost of a higher pre-calculation workload.

Solution space approximation via trilinear dose interpolation in VMAT treatment planning governed by composite optimization parameters is possible without further knowledge of the internal implementation of the underlying optimizer. Maximum average deviations between approxi- mation and actual values of characteristic dose quality indicators below 1% (cranial) and 8% (spine) allow for a quick qualitative assessment of the possible solution landscape.