Neuro-oncological augmented reality planning for intracranial tumor resection - Van Gestel, Frederick; Frantz, Taylor; Buyck, Felix; Geens, Wietse; Neuville, Quentin; Bruneau, Michaël; Jansen, Bart; Scheerlinck, Thierry; Vandemeulebroucke, Jef; Duerinck, Johnny (Frontiers in Neurology, 2023)
BACKGROUND Before starting surgery for the resection of an intracranial tumor, its outlines are typically marked on the skin of the patient. This allows planning of the optimal skin incision, craniotomy and angle of approach. Conventionally, the surgeon determines tumor borders using neuronavigation with a tracked pointer. However, interpretation errors can lead to important deviations, especially for deep-seated tumors, potentially resulting in a suboptimal approach with incomplete exposure. Augmented reality (AR), allows displaying the tumor and critical structures directly on the patient, which can simplify and improve surgical preparation. METHODS We developed an AR-based workflow for intracranial tumor resection planning deployed on the Microsoft HoloLens II that exploits the built-in infrared-camera for tracking of the patient. We initially performed a phantom study to assess the accuracy of the registration and tracking. Following this, we evaluated the AR-based planning step in a prospective clinical study for patients undergoing resection of a brain tumor. This planning step was performed by 12 surgeons with varying degrees of experience. After patient registration, tumor outlines were marked on the patient’s skin by different investigators, consecutively using a conventional neuronavigation system and an AR-based system. Performance of both registration and delineation was measured in terms of accuracy and duration and compared between both systems. RESULTS During phantom testing, registration errors remained below 2.0mm and 2.0° for both AR-based navigation and conventional neuronavigation, with no significant difference between both systems. In the prospective clinical trial, 20 patients underwent tumor resection planning. Registration accuracy was independent of user experience for both AR-based navigation and the commercial neuronavigation system. AR-guided tumor delineation was deemed superior in 65% of cases, equally good in 30% of cases, and inferior in 5% of cases when compared to the conventional navigation system. Overall planning time (AR=119±44s, conventional=187±56s) was significantly reduced through the adoption of the AR workflow (p<0.001), with an average time reduction of 39%. CONCLUSION By providing more intuitive visualization of relevant data to the surgeon, AR navigation provides an accurate method for tumor resection planning that is quicker and more intuitive than conventional neuronavigation. Further research should focus on intraoperative implementations.