Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours. (December 2020)

• Record Type: Journal Article
• Authors: Florkow, Mateusz C.; Guerreiro, Filipa; Zijlstra, Frank; Seravalli, Enrica; Janssens, Geert O.; Maduro, John H.; Knopf, Antje C.; Castelein, René M.; van Stralen, Marijn; Raaymakers, Bas W.; Seevinck, Peter R.
• Is Part Of: Radiotherapy and oncology. Volume 153(2020)
• Page Start: 220
• Page End: 227
• Publication Date: 2020-12
• Subjects: Synthetic CT -- Deep learning -- Paediatric -- MRI -- Wilms' Tumour -- Neuroblastoma; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.09.056
• Languages: English
• ISSNs: 0167-8140

Abstract

Graphical abstract: Highlights: Satisfactory synthetic CT images were derived from planning T1w and T2w MR images. Deep learning-based MRI-only radiotherapy is feasible in pediatric abdominal tumors. CT-sCT dose differences were clinically acceptable (<2%) for photon & proton plans. Larger differences were caused by existing interscan differences (eg bowel filling) Abstract: 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 (Ddiff ) 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 Ddiff were <0.5% for both VMAT (range [−2.5; 2.4]%) and PBS (range [−2.7; 3.7]%) dose distributions. The average gamma pass-rates 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.