Simple SummaryPancreatic cancer is a devastating disease with more than 60,000 new cases each year and a less than 10 percent 3-year overall survival rate. Radiation therapy is an effective treatment for locally advanced pancreatic cancer. The current clinical RT workflow, however, is lengthy and involves separate image acquisition for diagnostic CT and planning CT, which imposes a huge burden on patients and their caretakers. Moreover, studies have shown a reduction in mortality rate from expeditious radiotherapy treatment courses. Here, we proposed an innovative deep learning solution to adapt the shape of a patient’s body in diagnostic CT to the treatment delivery setup, and consequently, reduce the time to treatment initiation by half. As a result, our method also reduces the time to surgery and greatly decreases the risk of progression for pancreatic cancer.AbstractMajor sources of delay in the standard of care RT workflow are the need for multiple appointments and separate image acquisition. In this work, we addressed the question of how we can expedite the workflow by synthesizing planning CT from diagnostic CT. This idea is based on the theory that diagnostic CT can be used for RT planning, but in practice, due to the differences in patient setup and acquisition techniques, separate planning CT is required. We developed a generative deep learning model, deepPERFECT, that is trained to capture these differences and generate deformation vector fields to transform diagnostic CT into preliminary planning CT. We performed detailed analysis both from an image quality and a dosimetric point of view, and showed that deepPERFECT enabled the preliminary RT planning to be used for preliminary and early plan dosimetric assessment and evaluation.