Binary Logistic Regression Modeling of Voice Impairment and Voice Assessment in Iranian Patients with Nonlaryngeal Head-and-Neck Cancers after Chemoradiation Therapy: Objective and Subjective Voice Evaluation

Abstract

Background: Laryngeal damages after chemoradiation therapy (RT) in nonlaryngeal head?and?neck cancers (HNCs) can cause voice disorders and finally reduce the patient’s quality of life (QOL). The aim of this study was to evaluate voice and predict laryngeal damages using statistical binary logistic regression (BLR) models in patients with nonlaryngeal HNCs. Methods: This cross?section experimental study was performed on seventy patients (46 males, 24 females) with an average age of 50.43 ± 16.54 years, with nonlaryngeal HNCs and eighty individuals with assumed normal voices. Subjective and objective voice assessment was carried out in three stages including before, at the end, and 6 months after treatment. Eventually, the Enter method of the BLR was used to measure the odds ratio of independent variables. Results: In objective evaluation, the acoustic parameters except for F0 increased significantly (P < 0.001) at the end treatment stage and decreased 6 months after treatment. The same trend can be seen in the subjective evaluations, whereas none of the values returned to pretreatment levels. Statistical models of BLR showed that chemotherapy (P < 0.05), mean laryngeal dose (P < 0.05), V50 Gy (P = 0.002), and gender (P = 0.008) had the greatest effect on incidence laryngeal damages. The model based on acoustic analysis had the highest percentage accuracy of 84.3%, sensitivity of 87.2%, and the area under the curve of 0.927. Conclusions: Voice evaluation and the use of BLR models to determine important factors were the optimum methods to reduce laryngeal damages and maintain the patient’s QOL.