Document Detail

A non-invasive procedure for early stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis.
MedLine Citation:
PMID:  25371410     Owner:  NLM     Status:  Publisher    
About two thirds of laryngeal cancers originate at the vocal cords. Early stage detection of malignant vocal fold alterations, including a discrimination of premalignant lesions, represents a major challenge in laryngology as precancerous vocal fold lesions and small carcinomas are difficult to distinguish by means of regular endoscopy only. We report a procedure to discriminate between malignant and precancerous lesions by measuring the characteristics of vocal fold dynamics by means of a computerized analysis of laryngeal high-speed videos. Ten patients with squamous cell T1a carcinoma, ten with precancerous lesions with hyperkeratosis, and ten subjects without laryngeal disease underwent high-speed laryngoscopy yielding 4,000 images per second. By means of wavelet-based phonovibrographic analysis, a set of three clinically meaningful vibratory measures were extracted from the videos comprising a total number of 15,000 video frames. Statistical analysis (ANOVA with post-hoc two-sided t-tests, P <0.05) revealed that vocal fold dynamics is significantly affected in presence of precancerous lesions and T1a carcinoma. On the basis of the three measures a discriminating pattern was extracted using a support vector machine-learning algorithm performing an individual classification in respect to the different clinical groups. By applying a leave-one-out cross-validation strategy, we could show that the proposed measures discriminate with a very high performance between precancerous lesions and T1a carcinoma (sensitivity: 100%, specificity: 100%). Although a large-scale study will be necessary to confirm clinical significance, the set of vibratory measures derived in this study may be applicable to improve the accuracy and reliability of non-invasive diagnostics of vocal fold lesions.
Jakob Unger; Jorg Lohscheller; Maximilian Reiter; Katharina Eder; Christian Betz; Maria Schuster
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-11-4
Journal Detail:
Title:  Cancer research     Volume:  -     ISSN:  1538-7445     ISO Abbreviation:  Cancer Res.     Publication Date:  2014 Nov 
Date Detail:
Created Date:  2014-11-5     Completed Date:  -     Revised Date:  2014-11-6    
Medline Journal Info:
Nlm Unique ID:  2984705R     Medline TA:  Cancer Res     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2014, American Association for Cancer Research.
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