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Translation of an esophagus histopathological FT‐IR imaging model to a fast quantum cascade laser modality
Author(s) -
Liberda Danuta,
Hermes Michael,
Koziol Paulina,
Stone Nick,
Wrobel Tomasz P.
Publication year - 2020
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202000122
Subject(s) - quantum cascade laser , preprocessor , computer science , workflow , laser , cascade , artificial intelligence , biomedical engineering , biological system , materials science , optics , chemistry , physics , medicine , database , biology , chromatography
The technical progress in fast quantum cascade laser (QCL) microscopy offers a platform where chemical imaging becomes feasible for clinical diagnostics. QCL systems allow the integration of previously developed FT‐IR‐based pathology recognition models in a faster workflow. The translation of such models requires a systematic approach, focusing only on the spectral frequencies that carry crucial information for discrimination of pathologic features. In this study, we optimize an FT‐IR‐based histopathological method for esophageal cancer detection to work with a QCL system. We explore whether the classifier's performance is affected by paraffin presence from tissue blocks compared to removing it chemically. Working with paraffin‐embedded samples reduces preprocessing time in the lab and allows samples to be archived after analysis. Moreover, we test, whether the creation of a QCL model requires a preestablished FTIR model or can be optimized using solely QCL measurements.