Intraoperative margin assessment in head and neck cancer using label-free fluorescence lifetime imaging, machine learning and visualization

Accurate cancer margin assessment prior to surgical resection is a key factor influencing the long-term survival of oral and oropharyngeal cancer patients. This leads to the need for additional guidance tools for real-time delineation of cancer margins. In this work, fiber-based fluorescence lifetime Imaging (FLIm) was combined with machine learning to perform intraoperative tumor identification. The developed classifier achieved a measurement-level ROC-AUC of 0.89±0.03 on an N=62 patient dataset. A transparent overlay of classifier output was augmented onto the surgical field and updated through tissue motion correction, ensuring co-registration between tissue and spectroscopic data/classifier output was maintained during imaging..