Endogenous Fluorescence Lifetime Imaging (FLIM) Endoscopy for Early Detection of Oral Cancer and Dysplasia∗

Javier A. Jo, Shuna Cheng, Rodrigo Cuenca-Martinez, Elvis Duran-Sierra, Bilal Malik, Beena Ahmed, Kristen Maitland, Yi Shing Lisa Cheng, John Wright, Terry Reese

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

We have performed a pilot clinical study, in which multispectral endogenous fluorescence (or autofluorescence) lifetime imaging (FLIM) was performed on clinically suspicious oral lesions of 73 patients undergoing tissue biopsy for oral dysplasia and cancer diagnosis. The results from this pilot study indicated that mild-dysplasia and early stage oral cancer could be detected from benign lesions using a computed aided diagnosis system developed based on biochemical and metabolic biomarkers derived from the endogenous FLIM images. The diagnostic performance of this novel FLIM clinical tool was estimated using a leave-onepatient-out cross-validation approach, which reported levels of sensitivity >90%, specificity >85%, and Area Under the Receiving Operating Curve (ROC-AUC) >0.9.

Original languageEnglish (US)
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3009-3012
Number of pages4
ISBN (Electronic)9781538636466
DOIs
StatePublished - Oct 26 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2018-July
ISSN (Print)1557-170X

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Country/TerritoryUnited States
CityHonolulu
Period7/18/187/21/18

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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