Hyperspectral Raman Imaging for Automated Recognition of Human Renal Amyloid.
J Histochem Cytochem
; 71(11): 643-652, 2023 11.
Article
en En
| MEDLINE
| ID: mdl-37833851
In the clinical setting, routine identification of the main types of tissue amyloid deposits, light-chain amyloid (AL) and serum amyloid A (AA), is based on histochemical staining; rarer types of amyloid require mass spectrometry analysis. Raman spectroscopic imaging is an analytical tool, which can be used to chemically map, and thus characterize, the molecular composition of fluid and solid tissue. In this proof-of-concept study, we tested the feasibility of applying Raman spectroscopy combined with artificial intelligence to detect and characterize amyloid deposits in unstained frozen tissue sections from kidney biopsies with pathologic diagnosis of AL and AA amyloidosis and control biopsies with no amyloidosis (NA). Raman hyperspectral images, mapped in a 2D grid-like fashion over the tissue sections, were obtained. Three machine learning-assisted analysis models of the hyperspectral images could accurately distinguish AL (types λ and κ), AA, and NA 93-100% of the time. Although very preliminary, these findings illustrate the potential of Raman spectroscopy as a technique to identify, and possibly, subtype renal amyloidosis.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Placa Amiloide
/
Amiloidosis
Límite:
Humans
Idioma:
En
Revista:
J Histochem Cytochem
Asunto de la revista:
HISTOCITOQUIMICA
Año:
2023
Tipo del documento:
Article