Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours
Filipa Guerreiro, Enrica Seravalli, Geert O. Janssens, John H. Maduro, Antje Knopf, Johannes A. Langendijk, Bas W. Raaymakers, C Kontaxis
Abstract
Objective: Dose prediction using deep learning networks prior to radiotherapy might lead tomore efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess their clinical usage for paediatric abdominal tumours. Material and methods: Data from 80 patients with neuroblastoma or Wilms' tumour was included. Pencil beam scanning (PBS) (5 mm/ 3%) and volumetric-modulated arc therapy (VMAT) plans (5 mm) were robustly optimized on the internal target volume (ITV). Separate 3-dimensional patch-based U-net networks were trained to predict PBS and VMAT dose distributions. Doses, planning-computed tomography images and relevant optimization masks (ITV, vertebra and organs-at-risk) of 60 patients were used for training with a 5-fold cross validation.