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Multimodal nonlinear microscopy of head and neck carcinoma : toward surgery assisting frozen section analysis

Title data

Heuke, Sandro ; Chernavskaia, Olga ; Bocklitz, Thomas ; Legesse, Fisseha Bekele ; Meyer, Tobias ; Akimov, Denis ; Dirsch, Olaf ; Ernst, Günther ; von Eggeling, Ferdinand ; Petersen, Iver ; Guntinas-Lichius, Orlando ; Schmitt, Michael ; Popp, Jürgen:
Multimodal nonlinear microscopy of head and neck carcinoma : toward surgery assisting frozen section analysis.
In: Head & Neck. Vol. 38 (2016) Issue 10 . - pp. 1545-1552.
ISSN 1097-0347
DOI: https://doi.org/10.1002/hed.24477

Official URL: Volltext

Abstract in another language

Background
Treatment of early cancer stages is deeply connected to a good prognosis, a moderate reduction of the quality of life, and comparably low treatment costs.
Methods
Head and neck squamous cell carcinomas were investigated using the multimodal combination of coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second-harmonic generation (SHG) microscopy.
Results
An increased median TPEF to CARS contrast was found comparing cancerous and healthy squamous epithelium with a p value of 1.8·10−10. A following comprehensive image analysis was able to predict the diagnosis of imaged tissue sections with an overall accuracy of 90% for a 4-class model.
Conclusion
Nonlinear multimodal imaging is verified objectively as a valuable diagnostic tool that complements conventional staining protocols and can serve as filter in future clinical routine reducing the pathologist's workload.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: nonlinear microscopy; spectral histopathology; head and neck cancer imaging; coherent anti-Stokes Raman scattering (CARS); second-harmonic generation (SHG); two-photon excited fluorescence (TPEF); image analysis
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Result of work at the UBT: No
DDC Subjects: 500 Science > 530 Physics
Date Deposited: 17 May 2023 12:18
Last Modified: 17 May 2023 12:18
URI: https://eref.uni-bayreuth.de/id/eprint/76322