Data mining of an acoustic biomarker in tongue cancers and its clinical validation

Abstract

Abstract The promise of speech disorders as biomarkers in clinical examination has been identified in a broad spectrum of neurodegenerative diseases. However, to the best of our knowledge, a validated acoustic marker with established discriminative and evaluative properties has not yet been developed for oral tongue cancers. Here we cross‐sectionally collected a screening dataset that included acoustic parameters extracted from 3 sustained vowels /ɑ/, /i/, /u/ and binary perceptual outcomes from 12 consonant‐vowel syllables. We used a support vector machine with linear kernel function within this dataset to identify the formant centralization ratio (FCR) as a dominant predictor of different perceptual outcomes across gender and syllable. The Acoustic analysis, Perceptual evaluation and Quality of Life assessment (APeQoL) was used to validate the FCR in 33 patients with primary resectable oral tongue cancers. Measurements were taken before (pre‐op) and four to six weeks after (post‐op) surgery. The speech handicap index (SHI), a speech‐specific questionnaire, was also administrated at these time points. Pre‐op correlation analysis within the APeQoL revealed overall consistency and a strong correlation between FCR and SHI scores. FCRs also increased significantly with increasing T classification pre‐operatively, especially for women. Longitudinally, the main effects of T classification, the extent of resection, and their interaction effects with time (pre‐op vs. post‐op) on FCRs were all significant. For pre‐operative FCR, after merging the two datasets, a cut‐off value of 0.970 produced an AUC of 0.861 (95% confidence interval: 0.785–0.938) for T3‐4 patients. In sum, this study determined that FCR is an acoustic marker with the potential to detect disease and related speech function in oral tongue cancers. These are preliminary findings that need to be replicated in longitudinal studies and/or larger cohorts.

Keywords

• acoustic analysis
• diagnostic tests
• quality of life
• speech biomarker
• support vector machine
• tongue cancer