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

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. Graphical abstract

Characterization of Turbid Biobased Polyurethane Thermosets by UV–Vis–NIR Spectroscopy Combined with Multivariate Data Analysis for In-Line Process Monitoring

“…Besides the classification of the data, the DA also considers the group in which a value is assigned. This allows the identification of false-positive, false-negative, true-positive, and true-negative values, to calculate the quality parameters’ overall accuracy, sensitivity, specificity, and precision with the so-called confusion matrices. , In the DA, the confusion matrices are calculated automatically by Unscrambler software. The number of principal components (PCs) used for the DA was similar to that of the shown PCA models.…”

Improved Process Control by Using the Effective Scattering Coefficients to Determine the Fat Content in Homogenized Cow-Based Milk with Multivariate Data Modeling

“…The accuracy, sensitivity, specificity, false positive rate, and precision were calculated based on the confusion matrix terminology in the Supporting Information. 29,30 For model formation, 21 sample types (126 individually recorded spectra) and a wavenumber range of 420−1360 nm were used (Table 1).…”

“…For the comparison of the models, each PCA was combined with a Bayesian discriminant analysis with quadratic distance and two principal components (PCs). The accuracy, sensitivity, specificity, false positive rate, and precision were calculated based on the confusion matrix terminology in the Supporting Information. , For model formation, 21 sample types (126 individually recorded spectra) and a wavenumber range of 420–1360 nm were used (Table ).…”

Microscopic and Spectroscopic Study on Phase Separation in Highly Crosslinked Biobased Polyurethane Thermosets

“…Therewith, the overall accuracy, sensitivity, specificity and precision were calculated. [44,45] A partial least squares regression (PLSÀ R) model was created, based on the normalized data, with mean centering, full cross-validation, and the Kernel algorithm, to prove the correlation between the IR data, the crosslinker amount and the effective scattering coefficient μ' s . The IR-spectra were used as predictors and the μ' s values and the crosslinker amount as responses.…”