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9 March 2022 Investigating sources of FLIm data variability in head & neck cancer
Brent W. Weyers, Julien Bec, Athena K. Tam, Mohamed Hassan, Sukhkaran S. Aulakh, Dorina Gui, Andrew C. Birkeland, Arnaud F. Bewley, Marianne Abouyared, D. Gregory Farwell, Laura Marcu
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Proceedings Volume PC11949, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX; PC1194902 (2022) https://doi.org/10.1117/12.2609864
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
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.
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Brent W. Weyers, Julien Bec, Athena K. Tam, Mohamed Hassan, Sukhkaran S. Aulakh, Dorina Gui, Andrew C. Birkeland, Arnaud F. Bewley, Marianne Abouyared, D. Gregory Farwell, and Laura Marcu "Investigating sources of FLIm data variability in head & neck cancer", Proc. SPIE PC11949, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX, PC1194902 (9 March 2022); https://doi.org/10.1117/12.2609864
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KEYWORDS
Tissues
Cancer
Medical imaging
In vivo imaging
Machine learning
Statistical analysis
Neck
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