VIRTUAL REALITY FOR PATIENT-SPECIFIC MULTIDISCIPLINARY PLANNING OF COMPLEX ORTHOPAEDIC ONCOLOGICAL SURGERY
Bibliographic record
Abstract
Surgical planning for complex orthopaedic oncology cases requires thorough understanding of anatomy and relationships to critical structures. Current imaging regimens include 2D planar CT and MRI, multiplanar reformations, 3D surface enhanced series and 3D printed models. However, Virtual Reality (VR) visualization and planning is a dynamic, immersive experience offering enhanced understanding of complex anatomical relationships, the ability manipulate and layer 3D models and images in real time and allows for multiple participants to engage simultaneously across remote sites. This pilot describes our initial experience with a novel VR planning system. Six patients with complex anatomic tumors were reviewed preoperatively by a multidisciplinary team of orthopaedic oncology surgeons, neurosurgeons, thoracic surgeons, spine surgeons along with a musculoskeletal radiologist. Interactive, on demand modelling of anatomical structures was performed in real time with input from all team members. Three cases involved the chest wall and spine and three cases arose from the pelvis. Pathology included Ewings sarcoma, neuroblastoma, and chondrosarcoma. Three-dimensional models of relevant bony and soft tissue anatomy were derived directly from diagnostic CT and MR imaging using 3D modeling tools in the virtual environment while noting the length of time and cost to prepare cases. The VR system was also validated for model and measurement accuracy via direct comparisons of with a reference system, and for overall ease of use and clinical utility. The Dice-Srensen Coefficient (DSC) was used to score similarity of models generated in VR to those made in the reference platform. On multidisciplinary debrief, each case reviewed in VR provided added information to the surgical team compared to standard imaging. This included better understanding of the tumor margins and relationships to critical structures and, in three cases, modified surgical approach. The platform successfully supported multiple reviewers sharing the same VR environment and allowed for dynamic changes to 2D and 3D visualizations. The mean time to prepare cases was 70 minutes (range 25 −90) dependent on number of anatomical structures to be modeled, representing a mean cost per case of $233 USD (range $85 −$300). DSC values for 3D structures created in VR were 0.97 or higher, confirming geometric accuracy of models relative to a reference system. Measurements of length, cross-sectional area, and angle on clinical CT scans were within 0.22 mm (0.3%), 0.16 mm2 (0.02%), and 0.04 deg (0.07%) or less of expected results, respectively. Lastly, pre-defined usability tests were successfully conducted by 15 volunteer end-users, all of whom yielded accurate measurements and models, and reported high confidence in their use of the platform. VR planning of complex multidisciplinary cases is dynamic, feasible and cost effective providing enhanced appreciation of complex anatomical relationships, leading to increased surgeon confidence and impacting on surgical approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".