Clinical Factors That Affect Fiducial Tracking in Robotic SABR for Lung Tumors

PURPOSE: SABR is a treatment option for patients with lung tumors that employs fiducials to track tumors during the breathing cycle. Currently, there is a paucity of data on how relative fiducial location and patient clinical characteristics affect fiducial tracking and clinical outcomes. This study aimed to identify factors that reduce the number of fiducials tracked with respiratory motion management during SABR. METHODS AND MATERIALS: An institutional review board–approved retrospective review was performed of patients receiving robotic SABR for lung tumors at our institution from 2016 to 2019. Clinical data including demographics, medical history, treatment data, and follow-up were collected. Fiducial geometries were obtained with Velocity contouring software and MATLAB. Mann-Whitney U, χ(2), and t tests were completed using MedCalc. RESULTS: A total of 73 patients with 77 treatments were identified. The χ(2) analysis revealed that chronic obstructive pulmonary disease was associated with having 3 or more fiducials tracked (P = .034). Tumors in lower lobes were associated with higher rates of uncertainty errors (P = .015). The number of fiducials tracked had no effect on local tumor control or overall survival, with a median of 36 months of follow-up. A total of 28 treatments had fiducial centroid data available for geometric analysis. The most common tracking errors were rigid body error (RBE; 57%) and spacing errors (36.4%). Spacing errors had a shorter average minimum interfiducial distance than nonspacing errors (1.0 cm vs 1.7 cm, respectively; P = .017). RBE treatments had a longer average maximum distance than non-RBE treatments (4.0 cm vs 3.0 cm; P = .022). CONCLUSIONS: Greater motion in lower lobes can contribute to certain tracking errors that prevent more fiducials from being tracked. Maintaining interfiducial distance between experimentally determined guidelines may limit spacing errors and RBEs, the 2 most common tracking errors. An increased number of patients in a data set may result in stronger correlations between patient and tumor factors and outcomes.