R Lee MacDonald1, Young Lee2, Jannie Schasfoort3, Hany Soliman2, Arjun Sahgal2, Mark Ruschin2. 1. Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada. Electronic address: Lee.MacDonald@sunnybrook.ca. 2. Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada. 3. Gamma Knife Center Tilburg, ETZ Hospital, Tilburg, Netherlands.
Abstract
PURPOSE: The transition from frame-based brain stereotactic radiosurgery (SRS) to frameless delivery is supported by real-time intrafraction monitoring to ensure accurate delivery. The purpose of this study is to characterize these real-time motion traces in a large cohort of patients treated with frameless gated brain SRS and to develop patient-specific predictions of tolerance violations. METHODS AND MATERIALS: SRS patients treated on the Gamma Knife Icon were immobilized using a device-specific thermoplastic head mask. A motion marker was fixed to the patient's nose, with gating and cone beam computed tomography (CBCT)-based corrections to the treatment at excursions from baseline exceeding 1.5 mm. The traces of 1446 fractions were analyzed according to magnitude (932 unique treatment plans for 462 unique individual patients), directional distribution of displacement, and stability. A neural network model was developed to predict interruptions based on a subset of trace data. RESULTS: The average displacement of the marker in the first fraction of all patients was 0.62 ± 0.25 mm with beam CBCT corrections, which would otherwise be modeled at 0.96 ± 0.96 mm without intrafraction motion correction (P < .0001). Twenty-nine percent of fractions delivered were interrupted, of which the Z-axis (superoinferior) motion was the largest contributor to excursion. Baseline corrections significantly compensated for the magnitude of motion in all 3 dimensions (P < .01). The motion relative to the first acquired CBCT was on average seen to consistently increase with treatment time, with the minimum P value occurring at 61.3 minutes. The neural network prediction model was able to predict treatment interruptions with 84% sensitivity on the first 5-minute sample of the trace. CONCLUSIONS: Corrections to marker position significantly decreased marker excursions in all 3 axes compared with a single CBCT alignment. Patient-specific modeling may aid in the optimization of cases selected for frameless radiosurgery to increase the accuracy of planned delivery.
PURPOSE: The transition from frame-based brain stereotactic radiosurgery (SRS) to frameless delivery is supported by real-time intrafraction monitoring to ensure accurate delivery. The purpose of this study is to characterize these real-time motion traces in a large cohort of patients treated with frameless gated brain SRS and to develop patient-specific predictions of tolerance violations. METHODS AND MATERIALS: SRS patients treated on the Gamma Knife Icon were immobilized using a device-specific thermoplastic head mask. A motion marker was fixed to the patient's nose, with gating and cone beam computed tomography (CBCT)-based corrections to the treatment at excursions from baseline exceeding 1.5 mm. The traces of 1446 fractions were analyzed according to magnitude (932 unique treatment plans for 462 unique individual patients), directional distribution of displacement, and stability. A neural network model was developed to predict interruptions based on a subset of trace data. RESULTS: The average displacement of the marker in the first fraction of all patients was 0.62 ± 0.25 mm with beam CBCT corrections, which would otherwise be modeled at 0.96 ± 0.96 mm without intrafraction motion correction (P < .0001). Twenty-nine percent of fractions delivered were interrupted, of which the Z-axis (superoinferior) motion was the largest contributor to excursion. Baseline corrections significantly compensated for the magnitude of motion in all 3 dimensions (P < .01). The motion relative to the first acquired CBCT was on average seen to consistently increase with treatment time, with the minimum P value occurring at 61.3 minutes. The neural network prediction model was able to predict treatment interruptions with 84% sensitivity on the first 5-minute sample of the trace. CONCLUSIONS: Corrections to marker position significantly decreased marker excursions in all 3 axes compared with a single CBCT alignment. Patient-specific modeling may aid in the optimization of cases selected for frameless radiosurgery to increase the accuracy of planned delivery.
Authors: Rodney E Wegner; Linda Xu; Zachary Horne; Alexander Yu; Matthew Goss; Yun Liang; Jason Sohn; Stephen M Karlovits Journal: Adv Radiat Oncol Date: 2020-07-01
Authors: Rodney E Wegner; Zachary D Horne; Yun Liang; Matthew Goss; Alexander Yu; Jonathan Pace; Richard W Williamson; Jody Leonardo; Stephen M Karlovits; Russel Fuhrer Journal: Adv Radiat Oncol Date: 2021-06-06
Authors: Tugce Kutuk; Rupesh Kotecha; Ranjini Tolakanahalli; D Jay J Wieczorek; Yongsook C Lee; Manmeet S Ahluwalia; Matthew D Hall; Michael W McDermott; Haley Appel; Alonso N Gutierrez; Minesh P Mehta; Martin C Tom Journal: Cancers (Basel) Date: 2022-07-13 Impact factor: 6.575