A novel deep-learning approach for monitoring gastrointestinal air variation during radiotherapy in young patients...

Objective.To develop deep-learning (DL) predictive models of internal gastrointestinal (GI) gas from radiographs, a surrogate for dose degradation in abdominal proton beam therapy (PBT).Approach.DL architectures (convolutional neural networks and U-Nets) were trained to predict GI air local path length and/or global volume from radiographs. Frontal/lateral radiographs with paired volume and path length labels were simulated from CTs and GI gas segmentations using the Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox. CTs from The Cancer Imaging Archive Paediatric-CT-SEG dataset (N= 349, 0-16 year, North America) were used for model development and internal evaluation; variation in GI filling, organ motion and body size were simulated for augmentation. External evaluation utilised data from two international radiotherapy institutions: one consisting mostly of children and young people (CYP) (N= 21, 2-19 year, Europe); the other predominantly of teenagers and young adults (TYAs) (N= 14, 10-24 year, Asia). Finally, real-life use case was demonstrated on two subjects treated with PBT.Main results.The optimal configuration was the dual U-Net, accurately predicting both volume (mean absolute error (MAE) = 24.0 ± 32.9 ml) and path length (MAE = 0.80 ± 0.59 mm) on the held-out Paediatric-CT-SEG dataset, while also generalising well to external radiotherapy datasets (volume MAE = {18.9 ± 22.4, 34.6 ± 35.2} ml, and path length MAE = {0.52 ± 0.38, 0.67 ± 0.55} mm on the CYP and TYA datasets, respectively). We found a lower Dice similarity coefficient for gas segmentations in TYA vs Paediatric-CT-SEG and CYP (0.56 ± 0.24 vs 0.87 ± 0.09 and 0.84 ± 0.16, respectively), reflecting cohort-specific gas morphology differences. When applied longitudinally, the network detected both monotonic and realistic intrasubject GI gas variation. Proof-of-concept evaluation on clinical radiographs showed promising performance (volume MAE = 29.5 ± 25.9 ml, and path length MAE = 1.10 ± 0.75 mm).Significance.DL-powered radiographs quantified internal GI gas changes with promising performance and generalisability to diverse ages and demographics. This was the first step towards a novel personalised image-guided radiotherapy traffic-light workflow for abdominal PBT with the goal of reducing the need for CBCT to required fractions.