Tissue discrimination in head and neck cancer using image fusion of IR and optical microscopy

A regression-based fusion algorithm has been used to merge hyperspectral Fourier transform infrared (FTIR) data with an H&E image of oral squamous cell carcinoma metastases in cervical lymphoid nodal tissue. This provides insight into the success of the ratio of FTIR absorbances at 1252 cm(−1) a...

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Detalles Bibliográficos
Autores principales: Al Jedani, Safaa, Smith, Caroline I., Ingham, James, Whitley, Conor A., Ellis, Barnaby G., Triantafyllou, Asterios, Gunning, Philip J., Gardner, Peter, Risk, Janet M., Shaw, Richard J., Weightman, Peter, Barrett, Steve D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2023
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440831/
https://www.ncbi.nlm.nih.gov/pubmed/37529901
http://dx.doi.org/10.1039/d3an00692a
Descripción
Sumario:A regression-based fusion algorithm has been used to merge hyperspectral Fourier transform infrared (FTIR) data with an H&E image of oral squamous cell carcinoma metastases in cervical lymphoid nodal tissue. This provides insight into the success of the ratio of FTIR absorbances at 1252 cm(−1) and 1285 cm(−1) in discriminating between these tissue types. The success is due to absorbances at these two wavenumbers being dominated by contributions from DNA and collagen, respectively. A pixel-by-pixel fit of the fused spectra to the FTIR spectra of collagen, DNA and cytokeratin reveals the contributions of these molecules to the tissue at high spatial resolution.

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