Markerless pancreatic tumor target localization enabled by deep learning.

Abstract

PURPOSE/OBJECTIVES\nDeep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being. For its anatomical characteristics and location, on-board target verification for radiation delivery to pancreatic tumors is a challenging task. Our goal was to employ deep neural network to localize the pancreatic tumor target on kV X-ray images acquired using on-board imager (OBI) for image-guided radiation therapy (IGRT).\n\n\nMATERIALS/METHODS\nThe network is setup up in such a way that the input is either a digitally reconstructed radiograph (DRR) image or a monoscopic X-ray projection image acquired by the OBI from a given direction, and the output is the location of the planning target volume (PTV) target in the projection image. To produce sufficient amount of training X-ray images reflecting vast possible clinical scenarios of anatomy distribution, a series of changes were introduced to the planning CT (pCT) images, including deformation, rotation and translation, to simulate different inter- and intra-fractional variations. After model training, the accuracy of the model was evaluated by retrospectively studying patients who underwent pancreatic cancer radiotherapy. Statistical analysis using mean absolute differences (MADs) and Lin s concordance correlation coefficient were used to assess the accuracy of the predicted target positions.\n\n\nRESULTS\nMADs between the model-predicted and the actual positions were found to be less than 2.60 mm in anterior-posterior, lateral, and oblique directions for both two axes in the detector plane. For comparison studies with and without fiducials, MADs are less than 2.49 mm. For all cases, Lin s concordance correlation coefficients between the predicted and actual positions were found to be better than 93%, demonstrating the success of the proposed deep learning for IGRT.\n\n\nCONCLUSIONS\nWe demonstrated that markerless pancreatic tumor target localization is achievable with high accuracy by using a deep learning technique approach.