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Discrimination of vocal folds lesions by multiclass classification using autofluorescence spectroscopy: An ex vivo study.
Gaiffe, Olivier; Mahdjoub, Joackim; Ramasso, Emmanuel; Mauvais, Olivier; Lihoreau, Thomas; Pazart, Lionel; Wacogne, Bruno; Tavernier, Laurent.
Afiliación
  • Gaiffe O; Department of Otolaryngology - Head and Neck Surgery, CHU de Besançon, Besançon, France.
  • Mahdjoub J; Laboratoire de Nanomédecine, Imagerie et Thérapeutiques, EA4662, Université de Franche-Comté, Besançon, France.
  • Ramasso E; Department of Otolaryngology - Head and Neck Surgery, CHU de Besançon, Besançon, France.
  • Mauvais O; Institut FEMTO-ST UMR61742, Université de Franche-Comté, ENSMM, CNRS, Besançon, France.
  • Lihoreau T; Department of Otolaryngology - Head and Neck Surgery, CHU de Besançon, Besançon, France.
  • Pazart L; Inserm CIC 1431, CHU Besançon, Besançon, France.
  • Wacogne B; Inserm CIC 1431, CHU Besançon, Besançon, France.
  • Tavernier L; Institut FEMTO-ST UMR61742, Université de Franche-Comté, ENSMM, CNRS, Besançon, France.
Head Neck ; 46(5): 1136-1145, 2024 05.
Article en En | MEDLINE | ID: mdl-38299429
ABSTRACT

BACKGROUND:

Autofluorescence spectroscopy is effective for noninvasive detection but underutilized in tissue with various pathology analyses. This study evaluates whether AFS can be used to discriminate between different types of laryngeal lesions in view of assisting in vocal fold surgery and preoperative investigations.

METHODS:

A total of 1308 spectra were recorded from 29 vocal fold samples obtained from 23 patients. Multiclass analysis was performed on the spectral data, categorizing lesions into normal, benign, dysplastic, or carcinoma.

RESULTS:

Through an appropriate selection of spectral components and a cascading classification approach based on artificial neural networks, a classification rate of 97% was achieved for each lesion class, compared to 52% using autofluorescence intensity.

CONCLUSIONS:

The ex vivo study demonstrates the effectiveness of AFS combined with multivariate analysis for accurate classification of vocal fold lesions. Comprehensive analysis of spectral data significantly improves classification accuracy, such as distinguishing malignant from precancerous or benign lesions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Lesiones Precancerosas / Neoplasias Laríngeas / Laringe Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Head Neck Asunto de la revista: NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Estados Unidos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Lesiones Precancerosas / Neoplasias Laríngeas / Laringe Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Head Neck Asunto de la revista: NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Estados Unidos