@article{BasslerStefanakisSequeiraetal.2021,
  author    = {Bassler, Miriam and Stefanakis, Mona and Sequeira, In{\^e}s and Ostertag, Edwin and Wagner, Alexandra and Bartsch, J{\"o}rg and Roeßler, Marion and Mandic, Robert and Reddmann, Eike and Lorenz, Anita and Rebner, Karsten and Brecht, Marc},
  title     = {Comparison of Whiskbroom and Pushbroom darkfield elastic light scattering spectroscopic imaging for head and neck cancer identification in a mouse model},
  journal   = {Analytical and bioanalytical chemistry},
  volume    = {413},
  issn      = {1618-2642},
  doi       = {10.1007/s00216-021-03726-5},
  institution = {Life Sciences},
  pages     = {7363 -- 7383},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}