نبذة مختصرة : International audience ; Doses deposited outside the treatment field during external photon-beam radiotherapy, known as out-of-field doses, appear to favor the appearance of radiation-induced cancer and hematological toxicities. These low doses are not currently estimated on a routine clinical basis, due to the lack of a suitable solution. With this proof of concept, we demonstrate the value of deep learning for the development of a tool enabling rapid, routine-compatible out-of-field dose estimation. For this purpose, a 3D U-Net, considering as inputs the in-field dose, as computed by the treatment planning system, and the patient's anatomy, was trained to predict out-of-field dose maps, using a dataset of 3151 pediatric whole-body dose maps estimated with analytical methods as ground truth. Consistent results in line with the literature were obtained (RMSD of 0.28, 0.41 and 0.32 cGy.Gy -1 for the training, validation and testing), arguing in favor of the use of neural networks for out-of-field dose estimation.
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