Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours

Purpose: To assess the feasibility of magnetic resonance imaging (MRI)-only treatment planning for photon and proton radiotherapy in children with abdominal tumours.Materials and methods: The study was conducted on 66 paediatric patients with Wilms' tumour or neuroblastoma (age 4 +/- 2 years) who underwent MR and computed tomography (CT) acquisition on the same day as part of the clinical protocol. MRI intensities were converted to CT Hounsfield units (HU) by means of a UNet-like neural network trained to generate synthetic CT (sCT) from T1- and T2-weighted MR images. The CT-to-sCT image similarity was evaluated by computing the mean error (ME), mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and Dice similarity coefficient (DSC). Synthetic CT dosimetric accuracy was verified against CT-based dose distributions for volumetric-modulated arc therapy (VMAT) and intensity-modulated pencil-beam scanning (PBS). Relative dose differences (D-diff) in the internal target volume and organs-at-risk were computed and a three-dimensional gamma analysis (2 mm, 2%) was performed.Results: The average +/- standard deviation ME was -5 +/- 12 HU, MAE was 57 +/- 12 HU, PSNR was 30.3 +/- 1. 6 dB and DSC was 76 +/- 8% for bones and 92 +/- 9% for lungs. Average D-diff were 99% (range [85; 100]%) for VMAT and >96% (range [87; 100]%) for PBS.Conclusion: The deep learning-based model generated accurate sCT from planning T1w- and T2w-MR images. Most dosimetric differences were within clinically acceptable criteria for photon and proton radiotherapy, demonstrating the feasibility of an MRI-only workflow for paediatric patients with abdominal tumours. (C) 2020 The Authors. Published by Elsevier B.V.