Enhancing accuracy in proton therapy: The impact of geometric uncertainty models in head and neck cancer treatment

Abstract

Background

Anatomical changes present a major source of uncertainty in head and neck (H&N) cancer treatment. Accurate modeling of these changes is important for enhancing treatment precision and supporting better outcomes.

Purpose

The purpose of this study is to assess different anatomical uncertainty modeling methods in robust optimization for H&N cancer proton therapy.

Methods

This retrospective study involved five nasopharynx radiotherapy patients. We compared conventional robust optimization with anatomical robust optimization (aRO): (1) conventional robust optimization (cRO-3 mm), which used 3 mm setup shift and 3% range uncertainty. (2) aRO_AM which used three predicted images from an AM capturing systematic anatomical changes, with a 1 mm setup shift and 3% range uncertainty. (3) aRO_PM, which used three predicted images from a probability model (PM) capturing the most probable deformations, also with a 1 mm setup shift and 3% range uncertainty. We assessed weekly dose coverage of the clinical target volumes (CTVs). Normal tissue complication probability (NTCP) for grade $\ge$ 2 xerostomia and grade $\ge$ 2 dysphagia were calculated using the accumulated nominal dose (without errors).

Results

aRO_PM outperformed cRO-3 mm and aRO_AM, consistently achieving V94${}_{\text{voxmin}}$ $\ge$ 95% for all cases across treatment weeks. Additionally, aRO_PM reduced the NTCP for grade $\ge$2 xerostomia by an average of 4.88 %, with a maximum reduction of 8.03%, and reduced the NTCP for grade $\ge$2 dysphagia by an average of 1.80%, with a maximum reduction of 4.23 %.

Conclusion

The PM demonstrates potential for improving robust optimization by effectively managing anatomical uncertainties in H&N cancer proton therapy, thereby enhancing treatment effectiveness.