Application of an automatic, uncertainty model‐guided, target‐generating algorithm to lung stereotactic body radiotherapy

Abstract

Purpose This work evaluated a new radiotherapy target‐generating framework (the αTarget algorithm) for creating internal target volumes for lung SBRT. Methods Nineteen patients previously treated with definitive intent SBRT to the lung were identified from a clinical database. For each patient's 4DCT simulation scan, deformable image registration was used between phases of the scan in order to generate voxelized models of motion for 35 individual gross tumor volumes. These motion models were then used with a new implementation of a previously described target‐generating algorithm to create new internal target volumes (αITVs). The resulting αITVs were analyzed with respect to their volume and the coverage they provided each tumor voxel per that voxel's motion model. The clinically used ITVs were similarly analyzed, and were then compared to the αITVs using paired Student's t‐tests. In addition, isotropic margins were added to the αITVs in order to determine the largest margin magnitude that could be added without exceeding the volume of the clinical ITVs. Results The αITVs increased the target coverage provided to each tumor's 5th‐percentile‐most‐covered‐voxel an average of 50.3% compared to the clinical ITVs (p < 0.0001). At the same time, the αITVs had volumes that were, on average, 31.4% smaller (p < 0.0001). The differences in volume were large enough that, on average, an extra 2 mm isotropic margin could be added to the αITV before it had a volume greater than the clinical ITV. Conclusions The αTarget algorithm can generate more effective lung SBRT internal target volumes that provide greater coverage with smaller volumes. In combination with numerous other advantages of the framework, this effectiveness makes the αTarget algorithm a powerful new method for advanced IGRT or adaptive radiotherapy techniques.