Effects of surgical resection on tissue autofluorescence lifetime signatures in head and neck cancer: implications for intraoperative tumor margin assessment.

SIGNIFICANCE: Accurate identification of residual tumor during head and neck cancer surgery is essential for preventing local recurrences and improving patient outcomes, yet current methods for intraoperative margin assessment remain limited. Fluorescence lifetime imaging (FLIm), a label-free optical technique, has shown strong potential for distinguishing cancerous from noncancerous tissue in vivo. However, the effect of tissue resection on autofluorescence properties is not well defined, raising the question of whether diagnostic signatures establishedin vivo translate reliably to ex vivo specimens. AIM: The aim is to characterize how fluorescence lifetime properties change between in vivo and ex vivo conditions across multiple head and neck anatomical sites and to assess how these changes affect diagnostic separability between cancerous and noncancerous tissue. APPROACH: A custom fiber-based, point-scanning FLIm system with phasor-based analysis was used to characterize fluorescence lifetime features in vivo and ex vivo across oral tongue, base of the tongue, and palatine tonsil tissue from 75 patients undergoing oropharyngeal surgery. A separability index based on phasor distributions was used to quantify cancer versus noncancer contrast across multiple spectral channels. RESULTS: Resection-induced changes in decay dynamics and fluorescence lifetime values varied by anatomy: oral tongue and tonsil showed reduced contrast ex vivo, whereas the base of tongue demonstrated improved separation after excision. The base of the tongue showed a higher separability index ex vivo (0.43 versus 0.15 in vivo), whereas oral tongue and tonsil maintained superior contrast in vivo (0.27 versus 0.18 and 0.46 versus 0.28, respectively). CONCLUSIONS: Surgical resection substantially alters autofluorescence signatures in an anatomy-dependent manner, emphasizing the need to train and validate diagnostic algorithms independently for in vivo surgical guidance and ex vivo pathology assessment.