Translation of an esophagus histopathological FT‐IR imaging model to a fast quantum cascade laser modality. Issue 8 (1st June 2020)

• Record Type: Journal Article
• Authors: Liberda, Danuta; Hermes, Michael; Koziol, Paulina; Stone, Nick; Wrobel, Tomasz P.
• Is Part Of: Journal of biophotonics. Volume 13:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-01
• Subjects: esophageal cancer -- histopathology -- infrared imaging -- machine learning -- quantum cascade laser; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.202000122
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: 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. Abstract : Quantum cascade laser (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.