TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Bassler, Miriam
A1  - Stefanakis, Mona
A1  - Sequeira, Inês
A1  - Ostertag, Edwin
A1  - Wagner, Alexandra
A1  - Bartsch, Jörg
A1  - Roeßler, Marion
A1  - Mandic, Robert
A1  - Reddmann, Eike
A1  - Lorenz, Anita
A1  - Rebner, Karsten
A1  - Brecht, Marc
T1  - Comparison of Whiskbroom and Pushbroom darkfield elastic light scattering spectroscopic imaging for head and neck cancer identification in a mouse model
JF  - Analytical and bioanalytical chemistry
N2  - 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.
KW  - mie elastic light scattering spectroscopy
KW  - chemometrics/statistics
KW  - clinical/biomedical analysis
KW  - head and neck cancer
KW  - mouse tumor model
KW  - microspectroscopy
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-35367
SN  - 1618-2642
SS  - 1618-2642
U6  - https://doi.org/10.1007/s00216-021-03726-5
DO  - https://doi.org/10.1007/s00216-021-03726-5
VL  - 413
SP  - 7363
EP  - 7383
S1  - 21
PB  - Springer
CY  - Berlin
ER  -