Navigation-guided osteotomies improve margin delineation in tumors involving the sinonasal area: A preclinical study
Bibliographic record
Abstract
OBJECTIVES: To demonstrate and quantify, in a preclinical setting, the benefit of three-dimensional (3D) navigation guidance for margin delineation during ablative open surgery for advanced sinonasal cancer. MATERIALS AND METHODS: Seven tumor models were created. 3D images were acquired with cone beam computed tomography, and 3D tumor segmentations were contoured. Eight surgeons with variable experience were recruited for the simulation of osteotomies. Three simulations were performed: 1) Unguided, 2) Guided using real-time tool tracking with 3D tumor segmentation (tumor-guided), and 3) Guided by 3D visualization of both the tumor and 1-cm margin segmentations (margin-guided). Analysis of cutting planes was performed and distance from the tumor surface was classified as follows: "intratumoral" when 0 mm or negative, "close" when greater than 0 mm and less than or equal to 5 mm, "adequate" when greater than 5 mm and less than or equal to 15 mm, and "excessive" over 15 mm. The three techniques (unguided, tumor-guided, margin-guided) were statistically compared. RESULTS: The use of 3D navigation for margin delineation significantly improved control of margins: unguided cuts had 18.1% intratumoral cuts compared to 0% intratumoral cuts with 3D navigation (p < 0.0001). CONCLUSION: This preclinical study has demonstrated the significant benefit of navigation-guided osteotomies for sinonasal tumors. Translation into the clinical setting - with rigorous assessment of oncological outcomes - would be the proposed next step.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".