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Comparison of Whiskbroom and Pushbroom darkfield elastic light scattering spectroscopic imaging for head and neck cancer identification in a mouse model

  • 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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Metadaten
Author of HS ReutlingenBassler, Miriam; Stefanakis, Mona; Ostertag, Edwin; Wagner, Alexandra; Reddmann, Eike; Lorenz, Anita; Rebner, Karsten; Brecht, Marc
URN:urn:nbn:de:bsz:rt2-opus4-35367
DOI:https://doi.org/10.1007/s00216-021-03726-5
ISSN:1618-2642
eISSN:1618-2650
Erschienen in:Analytical and bioanalytical chemistry
Publisher:Springer
Place of publication:Berlin
Document Type:Journal article
Language:English
Publication year:2021
Tag:chemometrics/statistics; clinical/biomedical analysis; head and neck cancer; microspectroscopy; mie elastic light scattering spectroscopy; mouse tumor model
Volume:413
Page Number:21
First Page:7363
Last Page:7383
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:570 Biowissenschaften, Biologie
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International