Abstract
Low and very low radiation doses delivered outside the treatment field in radiotherapy can affect the immune system and contribute to radiation-induced lymphopenia, which is associated with poorer clinical outcomes. This is particularly relevant in radio-immunotherapy settings, where preservation of immune function may directly impact therapeutic efficacy. In this work, we propose an energy-conditioned deep learning framework for voxel-wise out-of-field (OOF) dose prediction from the planned in-field dose and whole-body mask. Beam energy is incorporated through additive conditioning, and the deterministic model is further extended to a heteroscedastic mean variance formulation for voxel-wise uncertainty estimation. Our framework is developed and evaluated on 1,519 reconstructed whole-body dose distributions from retrospective photon-based LINAC treatment records. The best-performing model, an energy-conditioned U-Net, achieved an MAE of 9.76 ± 9.28 cGy and an RMSD of 15.61 ± 12.50 cGy. Energy conditioning significantly improved the U-Net baseline, while mean variance modeling provided uncertainty estimates positively correlated with prediction errors. These results demonstrate the potential of energy-conditioned deep learning for rapid and uncertainty-aware whole-body OOF dose estimation. Code is available at https://github.com/maichi98/DoseFieldNet.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_032.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{ElMoh_UncertaintyAware_MICCAISAT2026,
author = { El Aichi, Mohammed AND Benzazon, Nathan AND M’Hamdi, Meïssane AND Allodji, Rodrigue AND de Vathaire, Florent AND Deutsch, Eric AND Diallo, Ibrahima AND Vakalopoulou, Maria AND Robert, Charlotte},
title = { { Uncertainty-Aware Out-of-Field Dose Prediction in External Beam Radiotherapy } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}