Deep-learning for rapid estimation of the out-of-field dose in external beam photon radiotherapy - A proof of concept.

BACKGROUND AND PURPOSE: The dose deposited outside of the treatment field during external photon beam radiotherapy treatment, also known as out-of-field dose, is the subject of extensive study as it may be associated with a higher risk of developing a second cancer, and could have deleterious effects on the immune system which compromise the efficiency of combined radio-immunotherapy treatments. Out-of-field dose estimation tools developed today in research, including Monte Carlo simulations and analytical methods, are not suited to the requirements of clinical implementation because of their lack of versatility and their cumbersome application. We propose a proof of concept based on deep learning for out-of-field dose map estimation that addresses the above limitations.

MATERIALS AND METHODS: 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. The cohort used for learning and performance evaluation included 3151 pediatric patients from the [XXXX] database, treated in 5 clinical centers, whose whole-body dose maps were previously estimated with an empirical analytical method. The test set, composed of 433 patients, was split into 5 subdatasets, each containing patients treated with devices unseen during the training phase. Root mean square deviation (RMSD) evaluated only on non-zero voxels located in the out-of-field areas was computed as performance metric.

RESULTS: RMSD of 0.28 and 0.41 cGy.Gy-1 were obtained for the training and validation datasets, respectively. Values of 0.27, 0.26, 0.28, 0.30 and 0.45 cGy.Gy-1 were achieved for the 6 MV linac, 16 MV linac, Alcyon cobalt irradiator, Mobiletron cobalt irradiator, and betatron devices test sets, respectively.

CONCLUSION: This proof-of-concept approach using a convolutional neural network has demonstrated unprecedented generalizability for this task, although it remains limited, and brings us closer to an implementation compatible with clinical routine.