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Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary. [ABSTRACT FROM AUTHOR]

Diagnosing any of the more than 30 types of T-cell lymphomas is considered a challenging task for many pathologists and currently requires morphological expertise as well as the integration of clinical data, immunophenotype, flow cytometry and clonality analyses. Even considering all available information, some margin of doubt might remain using the current diagnostic procedures. In recent times, the genetic landscape of most T-cell lymphomas has been elucidated, showing a number of diagnostically relevant mutations. In addition, recent data indicate that some of these genetic alterations might bear prognostic and predictive value. Extensive genetic analyses, such as whole exome or large panel sequencing are still expensive and time consuming, therefore limiting their application in routine diagnostic. We therefore devoted our effort to develop a lean approach for genetic analysis of T-cell lymphomas, focusing on maximum efficiency rather than exhaustively covering all possible targets. Here we report the results generated with our small amplicon-based panel that could be used routinely on paraffin-embedded and even decalcified samples, on a single sample basis in parallel with other NGS-panels used in our routine diagnostic lab, in a relatively short time and with limited costs. We tested 128 available samples from two German reference centers as part of our routine work up (among which 116 T-cell lymphomas), which is the largest routine diagnostic series reported to date. Our results showed that this assay had a very high rate of technical success (97%) and could detect mutations in the majority (79%) of tested T-cell lymphoma samples. [ABSTRACT FROM AUTHOR]

Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.