Journal
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
Volume 413, Issue 30, Pages 7363-7383Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00216-021-03726-5
Keywords
Mie elastic light scattering spectroscopy; Chemometrics; statistics; Clinical; biomedical analysis; Head and neck cancer; Mouse tumor model; Microspectroscopy
Funding
- King's College London Biological Services Unit
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The study utilized microspectroscopic techniques, with a focus on hyperspectral imaging, to distinguish lingual healthy from altered tissue areas in a mouse model. By comparing two different HSI detection principles and utilizing statistical models on elastic light scattering spectra, a high overall accuracy of 98% was achieved. The Pushbroom PCA-DA model was found to be more suitable for tissue type differentiations and tissue classification.
The early detection of head and neck cancer is a prolonged challenging task. It requires a precise and accurate identification of tissue alterations as well as a distinct discrimination of cancerous from healthy tissue areas. A novel approach for this purpose uses microspectroscopic techniques with special focus on hyperspectral imaging (HSI) methods. Our proof-of-principle study presents the implementation and application of darkfield elastic light scattering spectroscopy (DF ELSS) as a non-destructive, high-resolution, and fast imaging modality to distinguish lingual healthy from altered tissue regions in a mouse model. The main aspect of our study deals with the comparison of two varying HSI detection principles, which are a point-by-point and line scanning imaging, and whether one might be more appropriate in differentiating several tissue types. Statistical models are formed by deploying a principal component analysis (PCA) with the Bayesian discriminant analysis (DA) on the elastic light scattering (ELS) spectra. Overall accuracy, sensitivity, and precision values of 98% are achieved for both models whereas the overall specificity results in 99%. An additional classification of model-unknown ELS spectra is performed. The predictions are verified with histopathological evaluations of identical HE-stained tissue areas to prove the model's capability of tissue distinction. In the context of our proof-of-principle study, we assess the Pushbroom PCA-DA model to be more suitable for tissue type differentiations and thus tissue classification. In addition to the HE-examination in head and neck cancer diagnosis, the usage of HSI-based statistical models might be conceivable in a daily clinical routine.
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