Deformable Tissue Modelling and Augmented Reality Based Guidance for Head and Neck Tumor Re-Resection Task

NCT07686211 | Recruiting

• 1 other identifier: 251090
• Study Type: observational
• Target: 51
• Locations: 1 country
• Sites: 1

Brief Summary

Head and neck cancers have one of the highest recurrence rates among solid malignancies, and recurrence is strongly correlated with overall survival. Reducing recurrence rates depends, in part, on the surgeon's ability to accurately re-resect areas of positive or close margins during surgery. Currently, margin status is communicated primarily through verbal descriptions between the surgeon and pathologist, which can be imprecise. This challenge is further compounded by the deformable nature of soft tissues, as once the specimen is resected, the shape and size of the specimen change, making it difficult to accurately map the specimen's margins back onto the surgical site. Emerging technologies -such as augmented reality (AR), 3D scanning, and advanced soft tissue modeling- offer promising solutions for improving surgical navigation and precision. Building on these advances, an AR-based surgical navigation system was developed specifically for head and neck tumor resections. The system uses a 3D scanner to generate virtual models of both the resected specimen and the patient's surgical site, as demonstrated in prior work. A soft tissue modeling algorithm is then applied to account for specimen shrinkage and deformation, enabling accurate tracking of positive tumor margins. This guidance information is visualized through an AR headset, which overlays the margin data directly onto the patient's surgical site, providing surgeons with real-time visual guidance during re-resection. In this study, the goal is to evaluate the benefits and usability of this novel navigation software, compared to the standard of care. By assessing surgeon performance and user experience in cadaveric tasks with and without the AR system to identify strengths, limitations, and opportunities for refinement of the system, ultimately advancing surgical precision and improving patient outcomes by reducing recurrence rates.

Conditions

Surgeon; Surgical; Fellow; Resident Doctor; Resident Surgeon; Physician

Keywords

physician; surgeon; resident doctor; doctor; surgical; fellow; resident surgeon

Outcome Measures

Primary Outcomes (3)

• Performance Task accuracy (e.g., resection precision)

  Surgeon performance of target re-localization compared with and without the AR-headset.

  within 90 minutes of AR-guided use

• User Experience

  Assess AR usability, ease of use, and comfort, through surgeon feedback surveys

  immediately after the AR-guided task.

• Accuracy of overlay alignment

  This will validate the accuracy of overlay alignment through landmark-based (tumor margin relocation) error metrics, which support precision of re-resection tasks.

  within 90 minutes of completing the AR-guided task.

Study Arms (1)

Augmented Reality (AR)

Participants will be asked to localize simulated margins on tissue resection beds on a fresh-frozen cadaver head. Specimens of skin, buccal, or tongue tissue will be resected by the research team beforehand. Participants will be asked to place pins or stitches where the indicated targets are located. These positions will be recorded by the research team. Participants will first receive oral guidance only, corresponding to common descriptions between pathologists and surgeons. Participants will then reproduce the same task with AR guidance. In this case, the target will be displayed in the see-through AR headset. The target will be overlaid on the resection bed site and follow your head's movements.

Other: Augmented Reality

Interventions

Augmented Reality OTHER

Task accuracy will be evaluated by measuring distances between the points identified with and without AR guidance, and the pathologist-intended target locations. Participants will then complete post-tasks surveys and interviews.

Augmented Reality (AR)

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Surgeon-physician, surgical fellow, or post-graduate year 1, 2, 3, 4 and 5 (PGY2-5) resident physicians

You may qualify if:

• Post-graduate year 1, 2, 3, 4 and 5 (PGY2-5) resident physicians. (no age limit)
• Surgical fellows.
• Attending physicians.
• Prior cadaver lab or surgical experience.
• Any surgeon, regardless of training and experience, who has been involved in the surgeon-pathologist interaction during surgical resection for frozen section and margin clearance assessment.

You may not qualify if:

• \. Non-physician surgery providers.

Sponsors & Collaborators

• Vanderbilt University Medical Center: lead
• Vanderbilt University: collaborator

Study Sites (1)

Vanderbilt University Medical Center

Nashville, Tennessee, 37232, United States

RECRUITING

Study Officials

• Michael Topf, MD

  Vanderbilt University Medical Center

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jie Ying Wu Assistant Professor of Computer Science, PhD

CONTACT

615-343-4996; JieYing.Wu@vanderbilt.edu

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor of Otolaryngology-Head and Neck Surgery

Study Record Dates

• First Submitted: June 30, 2026
• First Posted: July 7, 2026
• Study Start: February 18, 2026
• Primary Completion (Estimated): June 1, 2029
• Study Completion (Estimated): June 1, 2029
• Last Updated: July 8, 2026

Record last verified: 2026-06

Locations

US (1)