Accounting for uncertainties in the position of anatomical barriers used to define the clinical target volume

The definition of the clinical target volume (CTV) is becoming the weakest link in the radiotherapy chain. CTV definition consensus guidelines include the geometric expansion beyond the visible gross tumor volume, while avoiding anatomical barriers. In a previous publication we described how to implement these consensus guidelines using deep learning and graph search techniques in a computerized CTV auto-delineation process. In this paper we address the remaining problem of how to deal with uncertainties in positions of the anatomical barriers. The objective was to develop an algorithm that implements the consensus guidelines on considering barrier uncertainties. Our approach is to perform multiple expansions using the fast marching method with barriers in place or removed at different stages of the expansion. We validate the algorithm in a computational phantom and compare manually generated with automated CTV contours, both taking barrier uncertainties into account.

Automated summary

This paper addresses the issue of handling uncertainties in positions of anatomical barriers in defining the clinical target volume (CTV). An algorithm was developed to implement consensus guidelines by performing multiple expansions using the fast marching method with barriers in place or removed at different stages of the expansion. The algorithm was validated in a computational phantom, comparing manually generated with automated CTV contours while considering barrier uncertainties.