Automated analysis of multimodal fluorescence lifetime imaging and optical coherence tomography data for the diagnosis of oral cancer in the hamster cheek pouch model.

It is known that the progression of oral cancer is accompanied by changes in both tissue biochemistry and morphology. A multimodal imaging approach combining functional and structural imaging modalities could therefore provide a more comprehensive prognosis of oral cancer. This idea forms the central theme of the current study, wherein this premise is examined in the context of a multimodal imaging system that combines fluorescence lifetime imaging (FLIM) and optical coherence tomography (OCT). Towards this end, in the first part of the present study, the diagnostic advantage obtained by using both fluorescence intensity and lifetime information is assessed. In the second part of the study, the diagnostic potential of FLIM-derived biochemical features is compared with that of OCT-derived morphological features. For an objective assessment, several quantitative biochemical and morphological features from FLIM and OCT data, respectively, were obtained using signal and image processing techniques. These features were subsequently used in a statistical classification framework to quantify the diagnostic potential of different features. The classification accuracy for combined FLIM and OCT features was estimated to be 87.4%, which was statistically higher than accuracy based on only FLIM (83.2%) or OCT (81.0%) features. Moreover, the complimentary information provided by FLIM and OCT features, resulted in highest sensitivity and specificity for the combined FLIM and OCT features for discriminating benign (88.2% sens., 92.0% spec.), pre-cancerous (81.5% sens., 96.0% spec.), and cancerous (90.1% sens., 92.0% spec.) classes.