Despite the fact that the oral cavity is easily accessible, only ~30% of oral cancers are diagnosed at an early stage, which is the main factor attributed to the low 5-year survival rate (63%) of oral cancer patients. Several screening tools for oral cancer have been commercially available; however, none of them have been demonstrated to have sufficient sensitivity and specificity for early detection of oral cancer and dysplasia. We hypothesized that an array of biochemical and metabolic biomarkers for oral cancer and dysplasia can be quantified by endogenous fluorescence lifetime imaging (FLIM), thus enabling levels of sensitivity and specificity adequate for early detection of oral cancer and dysplasia. Our group has recently developed multispectral FLIM endoscopes to image the oral cavity with unprecedented imaging speed (>2fps). We have also performed an in vivo pilot study, in which endogenous multispectral FLIM images were acquired from clinically suspicious oral lesions of 52 patients undergoing tissue biopsy. 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 (CAD) system developed based on biochemical and metabolic biomarkers that could be quantified from endogenous multispectral FLIM images. The diagnostic performance of this novel FLIM clinical tool was estimated using a cross-validation approach, showing levels of sensitivity and specificity >80%, and Area Under the Receiving Operating Curve (RO- AUC) >0.9. Future efforts are focused on developing cost-effective FLIM endoscopes and validating this novel clinical tool in prospective multi-center clinical studies.
Javier A. Jo, Shuna Cheng, Rodrigo Cuenca, Elvis Duran, Bilal Malik, Kristen Maitland, Beena Ahmed, Lisa Cheng, and John Wright, "Computer aided diagnosis system for automated label-free early detection of oral epithelial cancer and dysplasia based on autofluorescence lifetime imaging endoscopy (Conference Presentation)," Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750Y (Presented at SPIE Medical Imaging: February 13, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2293813.5751496043001.
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