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 was 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 the 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 noninvasive diagnostics of vocal fold lesions.
