Navigation-Guided Transnasal Endoscopic Delineation of the Posterior Margin for Maxillary Sinus Cancers: A Preclinical Study

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 and within the tumor or 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

Projected cutting guides using an augmented reality system to improve surgical margins in maxillectomies: A preclinical study

Background: Positive margins have been reported up to 80% in advanced maxillary cancers. Intraoperative navigation (IN) aims to improve margins, but provides a two-dimensional view of a registered instrument without anticipating any cutting directions, and the information is displayed in monitors outside surgical field. Augmented Reality (AR) can delineate margins while addressing the gaze-toggling drawback of IN. In a preclinical setting, we implemented preoperative-planned osteotomies needed for maxillectomies and projected this information on the surgical field using AR. We aimed to improve negative margin rates while retaining the benefits of AR. Methods: Five maxillary tumor models were built. Five fellowship-trained surgeons completed virtual unguided and AR-guided maxillectomies. Comparisons in terms of intratumoral cuts, close, adequate, and excessive distances from the tumor were performed. Differences between “ideal” cutting-plan and the AR-guided virtual osteotomies was obtained. Workload questionnaires to evaluate the technology were completed. Results: 115 virtual osteotomies were analyzed. Intra-tumoral and “close” margins were lower for the AR-assisted osteotomies (0.0% vs 1.9% p < 0.0001 and 0.8% vs 7.9% p < 0.0001). Proportion of “adequate” margins were higher in the AR simulations (25.3% vs 18.6%, p = 0.018). The AR osteotomies had high similarity with the pre-planned with interclass correlation index close to 1 in “adequate” margins 0.893 (95% CI: 0.804–0.949). Workload scores were better for AR-guided simulations for the domains of mental demand, performance, effort and frustration. Conclusion: The projector-based AR method improved margin delineation, and preoperative planning was accurately translated to the simulations. Clinical translation will aim to consolidate our preclinical findings to improve outcomes