Integrating extended reality into preoperative neurosurgical planning: exploratory evaluation of planning-related behavior in an instrumented hybrid framework

Background Extended reality (XR) has increasingly been investigated for visualization and preoperative planning in neurosurgery. However, evaluation has often focused on visualization, usability, technical performance, and task outcomes, while comparatively less attention has been given to quantifying observable planning-related behavior. Methods We developed and evaluated an instrumented hybrid desktop–XR framework integrating conventional image-based planning with immersive exploration of patient-specific neurosurgical anatomy. The framework synchronized selected visualization and interaction states between a 3D Slicer-based desktop environment and an XR workspace and captured spatial, anatomical, workflow, interaction, and subjective measures. Twenty participants with different levels of clinical experience evaluated two patient-specific tumor cases. Results All participants completed both evaluation cases without session-ending technical interruption. Entry–tumor-center directions showed substantial convergence, with directional convergence of 0.881 for the GBM case and 0.965 for the LGG case, while anatomical structure selection showed fair overall agreement (Fleiss’ κ = 0.397 ). Workflow, interaction, and subjective measures provided complementary information on task execution and user experience. Conclusion The instrumented hybrid desktop–XR framework demonstrated technical and procedural feasibility under the study conditions for capturing complementary quantitative measures of spatial planning, anatomical selection, workflow, interaction, and subjective experience. These findings demonstrate that selected components of preoperative planning can be made observable and quantifiable within an XR-supported workflow. The measures should currently be regarded as descriptive behavioral measures rather than validated indicators of planning quality, expertise, or clinical correctness. The framework provides a methodological basis for subsequent validation using larger cohorts, more diverse cases, conventional-workflow comparisons, and expert or clinical reference criteria.