Publication

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

Florkow, M. C., Guerreiro, F., Zijlstra, F., Seravalli, E., Janssens, G. O., Maduro, J. H., Knopf, A. C., Castelein, R. M., van Stralen, M., Raaymakers, B. W. & Seevinck, P. R., 7-Oct-2020, In : Radiotherapy and Oncology. 33 p.

Research output: Contribution to journalArticleAcademicpeer-review

  • Mateusz C Florkow
  • Filipa Guerreiro
  • Frank Zijlstra
  • Enrica Seravalli
  • Geert O Janssens
  • John H Maduro
  • Antje C Knopf
  • René M Castelein
  • Marijn van Stralen
  • Bas W Raaymakers
  • Peter R Seevinck

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.

Original languageEnglish
Number of pages33
JournalRadiotherapy and Oncology
Publication statusE-pub ahead of print - 7-Oct-2020

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