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Stereotactic ablative radiotherapy (SABR) delivers a high dose of radiation to a small area and is frequently used to treat cancer patients with metastatic lesions in the lung. Selecting the appropriate prescription is a balance of delivering enough radiation to lesions to prevent recurrence, and minimizing the radiation delivered to organs at risk (OARs) to limit side effects. After a radiation oncologist (RO) selects a prescription, treatment planning software is used to create the dose distribution, and calculate radiation delivered to the lesions and OARs. If dose constraints are not met, a different prescription must be selected, and the process is repeated. Planning SABR treatments is resource intensive, and repeated iterations can lead to treatment delays. Recently, machine learning techniques have been used to create a dose distribution for a given SABR prescription. Thus far, these techniques only target single lesions and are not commonly implemented clinically. In this work, we create a conditional generative adversarial network (GAN) with a U-NET backbone to estimate the dose distribution of SABR treatments to 2-6 lesions in the lung. The GAN is conditioned on contours of the OARs and lesions, CT images, and an initial dose estimation. A novel loss function is used during training. Through the mean squared error and dose metrics used by ROs, the output of the GAN demonstrates good agreement with the ground truth dose. The model will allow ROs to efficiently compare prescriptions options, reduce departmental workload by the multidisciplinary team, and circumvent treatment delivery delays for patients.
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