Ex vivo intraoperative surgical basal margin analysis in head and neck cancer resection: clinical
validation
In a clinical collaboration with the University Medical Center Groningen (UMCG), Netherlands—led by Oral and
Maxillofacial Surgeon, Dr. Witjes—we will advance and clinically test an intraoperative dual-aperture
fluorescence ratio (dAFR) imaging system and an automated data analysis (ADA) approach that can analyze an
entire resected-tissue basal margin in <1 min for head and neck squamous cell cancer (HNSCC) resection
surgery. This is a significant improvement upon currently used frozen-section histopathology that requires >30
min and can only be done for a limited number of resected-tissue locations. The project will specifically focus on
improving detection and localization of inadequate (positive and close) basal surgical margins, critical for patient
survival. Over 3 million individuals across the globe are diagnosed with head and neck squamous cell carcinoma
(HNSCC) each year. At present, surgical intervention is the primary treatment; however, owing to the complexity
of the anatomy in the head and neck, >20% of procedures may fail to attain tumor-free surgical basal margins
(defined as greater than 5 mm of healthy tissue surrounding the deep edge of the resected tumor). Unfortunately,
because of slow resected-tissue processing, patients are often sent home before margins are fully assessed.
While some patients can return for re-resection, for many, health concerns or substantial post-resection
reconstruction surgeries make re-resection infeasible, and radiotherapy and/or chemoradiotherapy is needed.
Patients with inadequate margins have <15% chance of surviving for 5 years post-surgery, compared to a >80%
chance of survival for patients with free margins and no metastatic disease. Before dAFR imaging and surgery,
patients are injected with a fluorescent epidermal growth factor receptor (EGFR)-targeted antibody, and
intraoperative dAFR images of resected basal margins are taken and analyzed. Our preliminary results from a
proof-of-concept dAFR imaging system indicate almost perfect detection rate of inadequate as well as clear
margins. In this project, we propose necessary imaging system upgrades and to develop an automated data
analysis to remove the need for clinical expert manual segmentation of images. The proposed approach will be
validated and tested against state-of-the-art surgical guidance methods in an 80-patient clinical study at UMCG,
in terms of detection and localization of inadequate basal margins through ROC and LROC assessments.
