Investigating sources of FLIm data variability in head & neck cancer

The primary standard of care for Head and Neck (H&N) cancer patients is the complete surgical removal of cancer. Tissue classifiers based of autofluorescence lifetime imaging (FLIm) parameters have shown potential to differentiate healthy from cancer tissue in H&N patients and thus enhance the accuracy of this procedure. Here we report how collective autofluorescence trends (100-patient cohort, oral/oropharyngeal cancer) driving healthy vs. tumor contrast depend on anatomical location, patient medical history (e.g. tobacco use) and surgical context (in vivo vs. ex vivo). Accounting for such biological variables may further improve the accuracy of FLIm-guided H&N cancer surgery.