Modeling pancreatic tumor motion using 4-dimensional computed tomography and surrogate markers.
Academic Article
Overview
abstract
PURPOSE: To assess intrafractional positional variations of pancreatic tumors using 4-dimensional computed tomography (4D-CT), their impact on gross tumor volume (GTV) coverage, the reliability of biliary stent, fiducial seeds, and the real-time position management (RPM) external marker as tumor surrogates for setup of respiratory gated treatment, and to build a correlative model of tumor motion. METHODS AND MATERIALS: We analyzed the respiration-correlated 4D-CT images acquired during simulation of 36 patients with either a biliary stent (n=16) or implanted fiducials (n=20) who were treated with RPM respiratory gated intensity modulated radiation therapy for locally advanced pancreatic cancer. Respiratory displacement relative to end-exhalation was measured for the GTV, the biliary stent, or fiducial seeds, and the RPM marker. The results were compared between the full respiratory cycle and the gating interval. Linear mixed model was used to assess the correlation of GTV motion with the potential surrogate markers. RESULTS: The average ± SD GTV excursions were 0.3 ± 0.2 cm in the left-right direction, 0.6 ± 0.3 cm in the anterior-posterior direction, and 1.3 ± 0.7 cm in the superior-inferior direction. Gating around end-exhalation reduced GTV motion by 46% to 60%. D95% was at least the prescribed 56 Gy in 76% of patients. GTV displacement was associated with the RPM marker, the biliary stent, and the fiducial seeds. The correlation was better with fiducial seeds and with biliary stent. CONCLUSIONS: Respiratory gating reduced the margin necessary for radiation therapy for pancreatic tumors. GTV motion was well correlated with biliary stent or fiducial seed displacements, validating their use as surrogates for daily assessment of GTV position during treatment. A patient-specific internal target volume based on 4D-CT is recommended both for gated and not-gated treatment; otherwise, our model can be used to predict the degree of GTV motion.
