Navigation-Guided Transnasal Endoscopic Delineation of the Posterior Margin for Maxillary Sinus Cancers: A Preclinical Study
Bibliographic record
Abstract
Background The resection of advanced maxillary sinus cancers can be challenging due to the anatomical proximity to surrounding critical anatomical structures. Transnasal endoscopy can effectively aid the delineation of the posterior margin of resection. Implementation with 3D-rendered surgical navigation with virtual endoscopy (3D-SNVE) may represent a step forward. This study aimed to demonstrate and quantify the benefits of this technology. Material and Method Four maxillary tumor models with critical posterior extension were created in four artificial skulls (Sawbones ® ). Images were acquired with cone-beam computed tomography and the tumor and carotid were contoured. Eight head and neck surgeons were recruited for the simulations. Surgeons delineated the posterior margin of resection through a transnasal approach and avoided the carotid while establishing an adequate resection margin with respect to tumor extirpation. Three simulations were performed: 1) unguided: based on a pre-simulation study of cross-sectional imaging; 2) tumor-guided: guided by real-time tool tracking with 3D tumor and carotid rendering; 3) carotid-guided: tumor-guided with a 2-mm alert cloud surrounding the carotid. Distances of the planes from the carotid and tumor were classified as follows and the points of the plane were classified accordingly: “red”: through the carotid artery; “orange”: <2 mm from the carotid; “yellow”: >2 mm from the carotid and within the tumor or <5 mm from the tumor; “green”: >2 mm from the carotid and 5–10 mm from the tumor; and “blue”: >2 mm from the carotid and >10 mm from the tumor. The three techniques (unguided, tumor-guided, and carotid-guided) were compared. Results 3D-SNVE for the transnasal delineation of the posterior margin in maxillary tumor models significantly improved the rate of margin-negative clearance around the tumor and reduced damage to the carotid artery. “Green” cuts occurred in 52.4% in the unguided setting versus 62.1% and 64.9% in the tumor- and carotid-guided settings, respectively ( p < 0.0001). “Red” cuts occurred 6.7% of the time in the unguided setting versus 0.9% and 1.0% in the tumor- and carotid-guided settings, respectively ( p < 0.0001). Conclusions This preclinical study has demonstrated that 3D-SNVE provides a substantial improvement of the posterior margin delineation in terms of safety and oncological adequacy. Translation into the clinical setting, with a meticulous assessment of the oncological outcomes, will 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 | 0.001 | 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 | 0.000 |
| 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".