PhD defence A. (Alba) Magallón Baro

Summary:

Pancreatic cancer is the fourth leading cause of cancer-related deaths worldwide, with a 5-year survival rate below 10%, and increasing incidence. At diagnosis, 30-35% of patients present an unresectable locally advanced pancreatic carcinoma, due to the large venous and arterial encasement involved, which endangers curative-intent surgery. Local progression (33%) and metastasis (67%) are the main causes of death for patients with LAPC. The combination of systematic chemotherapy and highly precise localized treatment, like stereotactic body radiotherapy, may contribute to achieve a durable local control and increase resectability rates. However, day-to-day organ (interfraction) variations occurring in the abdominal region cause a major uncertainty around the tumor, and may hamper the adequate delivery of radiation doses to cover the target and the surrounding healthy organs as intended, eventually causing undesired side-effects and toxicity. Online adaptive radiotherapy has emerged as a paradigm to compensate for interfraction organ variations. However, understanding the magnitude and importance of these interfraction organ variations, is the key to be able to deliver safer radiotherapy treatments, and to determine the actual need and benefit of adaptive radiotherapy on pancreatic cancer patients. In this thesis, we have used high quality diagnostic 3D images acquired with an in-room CT scanner in the treatment room, to quantify organ motion and dosimetric consequences for patients with LAPC, and subsequently, to explore different adaptive methods to mitigate the chance of organs-at-risk toxicity. Overall, we offer a set of possible solutions to implement an adaptive RT workflow using the CyberKnife robotic system. Throughout the whole thesis, we have used the planning CT and up to three pre-treatment CT scans from 35 patients with locally advanced pancreatic cancer (a total of 133 CT scans) that were retrospectively collected in our institute for the LAPC-1 phase II clinical trial (NL49643.078.14). All scans were thoroughly manually contoured by the same experienced radiation oncologist, constituting the main ground truth dataset used in this thesis.