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5 March 2021 Intraoperative margin assessment in head and neck cancer using label-free fluorescence lifetime imaging, machine learning and visualization
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Abstract
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..
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark Marsden, Brent W. Weyers, Takanori Fukazawa, Tianchen Sun, Julien Bec, Regina F. Gandour-Edwards, Dorina Gui, Andrew C. Birkeland, Arnaud F. Bewley, Marianne Abouyared, D. Gregory Farwell, and Laura Marcu "Intraoperative margin assessment in head and neck cancer using label-free fluorescence lifetime imaging, machine learning and visualization", Proc. SPIE 11631, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIX, 116310N (5 March 2021); https://doi.org/10.1117/12.2577051
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