Stereotactic MRI-guided online adaptive radiotherapy (SMART) is an effective treatment for the pancreas and other upper abdominal cancers. SMART allows precise delivery of escalated prescription dose to the abdominal tumor targets while avoiding the complications of radiation toxicity to the mobile gastrointestinal (GI) organs surrounding the tumor target. In the clinical workflow of SMART, manual segmentation of the GI orangs at risk (OARs) is one of the most important but also the most labor-intensive steps. Manual segmentation takes 10 minutes on average but ranges from 5 to 22 minutes. The slow and costly manual segmentation step directly decreases the accessibility and affordability of online SMART and indirectly reduces the effectiveness of SMART due to intra-fractional body and organ movement of the patients. In this study, we will develop a deep-learning based interactive and semi-automatic procedure to accurately and quickly segment the GI OARs to make SMART more efficient and affordable.
Stereotactic MRI-guided online adaptive radiotherapy (SMART) has been demonstrated as an effective treatment for the pancreas and other upper abdominal cancers. For nonresectable pancreatic cancer, SMART increased the overall survival at 36 months from 18% to 55% compared to conventional radiation therapy (RT) treatment. In this study, we will develop a deep-learning based interactive and semi-automatic method to accurately and quickly segment the organs-at- risk (OAR) in the abdomen to support SMART. The method to be developed will significantly expedite the OAR segmentation step and make SMART more efficient and affordable.
