Voice outcomes in patients with advanced laryngeal and hypopharyngeal cancer treated with chemo-radiotherapy

Impact of Cancer Localization on Symptom Burden and Quality of Life in Head and Neck Cancers: A Comparative Study

Background: Head and neck cancer (HNC) is a critical concern in oncology, with notable disparities in survival rates. While the long-term symptom burden in HNC survivors and its impact on quality of life (QoL) has been explored, there is limited understanding of the influence of cancer localizations on these aspects. This study aims to elucidate the role of cancer localizations in shaping long-term outcomes in HNC patients. Methods: A cross-sectional study was conducted at the University Hospital Erlangen's Department of Otolaryngology, exploring the impact of cancer localization on symptom burden and QoL in 138 HNC patients using the University of Washington Quality of Life Questionnaire Version 4. Results: In our study of HNC patients, we investigated symptom burden across different cancer localizations, including oral cavity, oropharyngeal, hypopharyngeal, laryngeal, and cancer of unknown primary (CUP). While we found no significant variations in parameters such as pain, appearance, and activity, notable differences emerged in swallowing, speech, and salivation. Patients with oral cavity and laryngeal carcinomas had significantly higher swallowing and salivation scores compared to those with oropharyngeal carcinoma and CUP, while speech-related symptoms were lower for oral cavity and laryngeal carcinoma patients. Importantly, these symptom differences did not significantly impact health-related and overall QoL. These findings emphasize the nuanced interplay between symptomatology and QoL in different HNC cancer localizations. Conclusion: The research highlights significant disparities in post-treatment symptoms across different HNC localizations and underscores the need for personalized treatment and management strategies to address unique challenges associated with each HNC type, ultimately aiming to enhance post-treatment QoL.

Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis

Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital laryngoscopy. Screening for laryngeal cancer solely based on voice could enhance early detection. However, identifying voice indicators specific to laryngeal cancer is challenging, especially when differentiating it from other laryngeal ailments. This study presents an artificial intelligence model designed to distinguish between healthy voices, laryngeal cancer voices, and those of the other laryngeal conditions. We gathered voice samples of individuals with laryngeal cancer, vocal cord paralysis, benign mucosal diseases, and healthy participants. Comprehensive testing was conducted to determine the best mel-frequency cepstral coefficient conversion and machine learning techniques, with results analyzed in-depth. In our tests, laryngeal diseases distinguishing from healthy voices achieved an accuracy of 0.85-0.97. However, when multiclass classification, accuracy ranged from 0.75 to 0.83. These findings highlight the challenges of artificial intelligence-driven voice-based diagnosis due to overlaps with benign conditions but also underscore its potential.

The value of dual-layer spectral detector CT in preoperative T staging of laryngeal and hypopharyngeal squamous cell carcinoma

PURPOSE

To explore the optimal kiloelectron voltage (keV) of virtual monochromatic images (VMIs) of dual-layer spectral detector computed tomography (DLSCT) to display laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) and its diagnostic performance for preoperative T staging of LHSCC.

METHODS

A total of 67 LHSCC patients were included, and the contrast between the tumor and sternocleidomastoid muscle (SM), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and image noise of 40-100 keV VMIs and conventional polyenergetic images (CIs) were evaluated. The image quality of the CI and 40-100 keV VMI was evaluated by a five-point method. The VMI with the best image quality was screened out, and the accuracy of the optimal keV VMI and CI for T staging was assessed using clinical T staging as the reference standard.

RESULTS

The contrast between the tumor and SM, SNR, CNR and subjective image quality scores of LHSCC on 40-50 keV VMIs were higher than those on CIs (P < 0.05); the image noises of 40-100 keV VMIs were lower than those of CIs (P < 0.05). The 40 keV VMI had the highest SNR, CNR and subjective score of image quality. The accuracy rates of the 40 keV VMI and CI for T staging of LHSCC were 0.86 and 0.63 (P < 0.001), respectively.

CONCLUSION

The image quality of 40-50 keV VMI is higher than that of CI, and the diagnostic accuracy of 40 keV VMI is better than that of CI, which is most suitable for preoperative T staging of LHSCC.