An approach for estimating dosimetric uncertainties in deformable dose accumulation in pencil beam scanning proton therapy for lung cancer.

Abstract

Deformable image registration (DIR) is an important component for dose accumulation and associated clinical outcome evaluation in radiotherapy. However, the resulting deformation vector field (DVF) is subject to unavoidable discrepancies when different algorithms are applied, leading to dosimetric uncertainties of the accumulated dose. We propose here an approach for proton therapy to estimate dosimetric uncertainties as a consequence of modeled or estimated DVF uncertainties. A patient-specific DVF uncertainty model was built on the first treatment fraction, by correlating the magnitude differences of five DIR results at each voxel to the magnitude of any single reference DIR. In the following fractions, only the reference DIR needs to be applied, and DVF geometric uncertainties were estimated by this model. The associated dosimetric uncertainties were then derived by considering the estimated geometric DVF uncertainty, the dose gradient of fractional recalculated dose distribution and the direction factor from the applied reference DIR of this fraction. This estimated dose uncertainty was respectively compared to the reference dose uncertainty when different DIRs were applied individually for each dose warping. This approach was validated on seven NSCLC patients, each with nine repeated CTs. The proposed model-based method is able to achieve dose uncertainty distribution on a conservative voxel-to-voxel comparison within ±5% of the prescribed dose to the 'reference' dosimetric uncertainty, for 77% of the voxels in the body and 66%-98% of voxels in investigated structures. We propose a method to estimate DIR induced uncertainties in dose accumulation for proton therapy of lung tumor treatments.

• Authors: Florian Amstutz, Lena Nenoff, Francesca Albertini, Cássia O Ribeiro, Antje C Knopf, Jan Unkelbach, Damien C Weber, Antony J Lomax, Ye Zhang
• Journal: Physics in medicine and biology (Phys Med Biol) Vol. 66 Issue 10 (05 10 2021) ISSN: 1361-6560 [Electronic] England
• PMID: 33862616 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © 2021 Institute of Physics and Engineering in Medicine.

Topics

• Algorithms
• Humans
• Image Processing, Computer-Assisted
• Lung Neoplasms (diagnostic imaging, radiotherapy)
• Proton Therapy
• Radiometry
• Radiotherapy Dosage
• Radiotherapy Planning, Computer-Assisted
• Uncertainty