Diagnostic techniques based on optical spectroscopy have the potential to link the biochemical and morphological properties of tissues. Light-induced fluorescence (LIF) spectroscopy as a noninvasive "optical biopsy" method has been used to detect small lesions in vivo using an Hg arc lamp as excitation light. The potential of LIF has been evaluated to improve the accuracy of conventional white light nasal endoscopy. More work has been carried out to improve the existing measuring system with lasers as excitation light....[ Read more ]

Diagnostic techniques based on optical spectroscopy have the potential to link the biochemical and morphological properties of tissues. Light-induced fluorescence (LIF) spectroscopy as a noninvasive "optical biopsy" method has been used to detect small lesions in vivo using an Hg arc lamp as excitation light. The potential of LIF has been evaluated to improve the accuracy of conventional white light nasal endoscopy. More work has been carried out to improve the existing measuring system with lasers as excitation light.

A novel classification method, Support Vector Machines (SVM), has been developed for extracting diagnostic information from autofluorescence spectral signals obtained with nasopharyngeal carcinoma (NPC) and normal tissue. In addition, the possibility to build a simpler algorithm and improve the diagnostic accuracy with the combination of PCA and SVM methods was investigated. It's found that PCA can substantially reduce the complexity of a SVM algorithm without sacrificing the performance of the algorithm. In brief, the classifying performance based on the data in both the spectrum and principal component domains are compatible and excellent; with RBF kernel function, the sensitivity and total predictive accuracy are up to 95.3% and 97.7%, respectively. In the right perspective, the method combining SVM and PCA outperforms other PCA methods.

In addition, in order to tracing the autofluorescence spectral signals of tissue layer by layer, a confocal fluorescent spectroscopy system has been set-up. Experiments have been carried out with fluorescent phantom and animal model. With an axial resolution of l0um in tissue, this confocal spectral system observed the spectral differences in spectral shape and spectral peak position among different layers of tissue.

In conclusion, light-induced autofluorescence spectroscopy accompanied with robust classification algorithms based on support vector machines provides a noninvasive and feasible tool in the diagnosis of cancer. Confocal fluorescence spectroscopy can provide more diagnostic information due to its ability of optical sectioning. It's hopeful that a confocal spectral system can detect cancer at much earlier stage.