Head & neck · 2024
Discrimination of vocal folds lesions by multiclass classification using autofluorescence spectroscopy: An ex vivo study.
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Gaiffe Olivier, Mahdjoub Joackim, Ramasso Emmanuel, Mauvais Olivier, Lihoreau Thomas, Pazart Lionel, Wacogne Bruno, Tavernier Laurent
Аннотация
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.
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.