A framework for voxel‐based assessment of biological effect after proton radiotherapy in pediatric brain cancer patients using multi‐modal imaging. Issue 7 (10th June 2021)

• Record Type: Journal Article
• Authors: Skaarup, Mikkel; Lundemann, Michael Juncker; Darkner, Sune; Jørgensen, Morten; Marner, Lisbeth; Mirkovic, Dragan; Grosshans, David; Peeler, Christopher; Mohan, Radhe; Vogelius, Ivan Richter; Appelt, Ane
• Is Part Of: Medical physics. Volume 48:Issue 7(2021)
• Page Start: 4110
• Page End: 4121
• Publication Date: 2021-06-10
• Subjects: diffusion MRI -- MRI response assessment -- multi‐modality registration -- proton therapy -- radiobiology of protons; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14989
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Introduction: The exact dependence of biological effect on dose and linear energy transfer (LET) in human tissue when delivering proton therapy is unknown. In this study, we propose a framework for measuring this dependency using multi‐modal image‐based assays with deformable registrations within imaging sessions and across time. Materials and Methods: 3T MRI scans were prospectively collected from 6 pediatric brain cancer patients before they underwent proton therapy treatment, and every 3 months for a year after treatment. Scans included T1‐weighted with contrast enhancement (T1), T2‐FLAIR (T2) and fractional anisotropy (FA) images. In addition, the planning CT, dose distributions and Monte Carlo‐calculated LET distributions were collected. A multi‐modal deformable image registration framework was used to create a dataset of dose, LET and imaging intensities at baseline and follow‐up on a voxel‐by‐voxel basis. We modelled the biological effect of dose and LET from proton therapy using imaging changes over time as a surrogate for biological effect. We investigated various models to show the feasibility of the framework to model imaging changes. To account for interpatient and intrapatient variations, we used a nested generalized linear mixed regression model. The models were applied to predict imaging changes over time as a function of dose and LET for each modality. Results: Using the nested models to predict imaging changes, we saw a decrease in the FA signal as a function of dose; however, the signal increased with increasing LET. Similarly, we saw an increase in T2 signal as a function of dose, but a decrease in signal with LET. We saw no changes in T1 voxel values as a function of either dose or LET. Conclusions: The imaging changes could successfully model biological effect as a function of dose and LET using our proposed framework. Due to the low number of patients, the imaging changes observed for FA and T2 scans were not marked enough to draw any firm conclusions.