Predictors of Treatment Interruption During Frameless Gamma Knife Icon Stereotactic Radiosurgery

Abstract Objective The Gamma Knife (GK) Icon allows for the delivery of stereotactic radiosurgery using a thermoplastic mask in combination with intrafraction motion monitoring (IFMM) using high definition motion management (HDMM). The system pauses treatment if the magnitude of motion in all directions exceeds 1-1.5 mm, causing a break in treatment and prolongation of the session. We reviewed the records of patients treated in a frameless manner on our GK Icon system, to determine predictors for treatment interruption. Methods We reviewed the records of patients treated between May 2019 and May 2020 on the GK Icon using a frameless technique for brain metastases, gliomas, schwannomas, and meningiomas. We recorded treatment time as noted in the plan document, actual treatment delivery time, and any pauses in treatment. We tabulated baseline characteristics including age, gender, diagnosis, performance status, and shifts at time of treatment. We used a receiver operating curve (ROC) analysis to determine a timepoint corresponding with treatment interruption. We then conducted a logistic regression analysis to generate odds ratios for likelihood of treatment. Results We identified 150 patients meeting inclusion criteria. The majority (82%) were patients with brain metastases. The median age was 63 and the median dose was 27 Gy (16-30 Gy) in 3 fractions (1-5). The median treatment time was 23 minutes (4-108). Sixty-nine patients (46%) had at least one pause in treatment (range: 1-7). ROC analysis revealed treatment time > 19 minutes and rotation >0.47 degrees to be associated with interruption. Multivariable logistic regression revealed rotation > 0.47 degrees and treatment time>19 minutes as predictive of interruption. Conclusions For patients with rotations exceeding 0.47 degrees or an extended treatment time, physicians should expect treatment interruptions, consider fractionation to lessen table time, or utilize a frame-based approach.