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

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Volume MAE: 24.0 ± 32.9 ml (internal), 18.9-34.6 ml (external); Path length MAE: 0.80 ± 0.59 mm (internal), 0.52-0.67 mm (external)

• Primary Outcome: Mean absolute error for gastrointestinal gas volume and path length prediction

• Nnt Or Sensitivity: Dice similarity coefficient: 0.87 ± 0.09 (paediatric), 0.84 ± 0.16 (CYP), 0.56 ± 0.24 (TYA)

• Confidence Interval: Not reported for primary accuracy metrics

Clinical Application

Requires integration with existing radiotherapy imaging systems and establishment of clinical decision protocols Relevant for Australian paediatric cancer centres offering proton therapy; would need TGA approval for clinical implementation and integration with existing radiation oncology protocols Children and young adults (0-24 years) receiving abdominal proton beam therapy

Abstract

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.

References

1. 1.Ghica, A. C., Simard, M., Yu, S., Nisbet, A., Gains, J., Zhang, Y., Lim, P., & Veiga, C. (2026). A novel deep-learning approach for monitoring gastrointestinal air variation during radiotherapy in young patients using radiographs. Physics in Medicine and Biology. https://doi.org/10.1088/1361-6560/ae6222