English

Implications of Clinical Target Distribution Weighted Radiotherapy Optimization

Medical Physics 2023-04-26 v1 Optimization and Control

Abstract

Delineating and planning with respect to regions suspected to contain microscopic tumor cells is an inherently uncertain task in radiotherapy. The recently proposed \textit{clinical target distribution} (CTD) is an alternative to the conventional \textit{clinical target volume} (CTV), with initial promise. Previously, using the CTD in planning has primarily been evaluated in comparison to a conventionally defined CTV. We propose to compare the CTD approach against CTV margins of various sizes, dependent on the threshold at which the tumor infiltration probability is considered relevant. First, a theoretical framework is presented, concerned with optimizing the trade-off between the probability of sufficient target coverage and the penalties associated with high dose. From this framework we derive conventional CTV-based planning and contrast it with the CTD approach. The approaches are contextualized further by comparison with established methods for managing geometric uncertainties. Second, for both a one- and a three-dimensional phantom, we compare a set of CTD plans created by varying the target objective function weight against a set of plans created by varying both the target weight and the CTV margin size. The results show that CTD-based planning gives slightly inefficient trade-offs between the evaluation criteria for a case in which near-minimum target dose is the highest priority. However, in a case when sparing a proximal organ at risk is critical, the CTD is better at maintaining sufficiently high dose toward the center of the target. We conclude that CTD-based planning is a computationally efficient method for planning with respect to delineation uncertainties, but that the inevitable effects on the dose distribution should not be disregarded.

Keywords

Cite

@article{arxiv.2210.06049,
  title  = {Implications of Clinical Target Distribution Weighted Radiotherapy Optimization},
  author = {Ivar Bengtsson and Anders Forsgren and Albin Fredriksson},
  journal= {arXiv preprint arXiv:2210.06049},
  year   = {2023}
}
R2 v1 2026-06-28T03:25:13.733Z