Method for end-user validation of deformable dose accumulation uncertainty modelling tools

Abstract

Background

Deformable dose accumulation (DDA) uncertainty models can inform treatment decisions by communicating the dosimetric impact of deformable image registration (DIR) errors over multiple fractions. Currently there is limited guidance on how end-users can validate such models in the clinic.

Purpose

We propose an end-user validation sequence for DDA uncertainty modelling tools, akin to an acceptance test, using existing patient data and a clinical treatment planning system (TPS) as the evaluation platform.

Methods

The proposed test sequence begins with a single “fixed” image (planning CT with associated contours and treatment plan) and a “moving” image (e.g., fractional synthetic CT with calculated dose) and uses the TPS to simulate multiple fractional DIRs as input to a DDA uncertainty model. Outputs of the uncertainty tool—including volumetric images of DIR spatial uncertainties, and associated uncertainties on propagated dose—are imported back to the TPS and a series of visual and semi-quantitative (point-based and DVH-based) cross-checks are carried out. Emphasis is placed on the use of standard dose and distance measurement tools, in conjunction with vendor-provided equations, to evaluate correctness of the uncertainty tool outputs at contour boundaries and in bulk tissue, considering variable DIR quality for both targets and organs at risk.

Results

The test sequence is demonstrated for a non-clinical uncertainty tool using a clinical bladder case. Agreement within 2 voxels (for spatial uncertainties) and up to 5% of the prescribed dose (for dose uncertainties) is shown to be achievable for regions of plausible deformation and stable dose gradient (e.g., < 5%/voxel).

Conclusions

As the use of DDA in adaptive treatment becomes more commonplace, use of DDA uncertainty tools will become increasingly important to inform adaptive treatment decisions. This work represents an important effort to formalize an end-user validation process using standardly available clinical tools.