Chinese journal of otorhinolaryngology head and neck surgery最新文献

Objective: To summarize the clinical characteristics of patients who experienced fatal hemorrhage following percutaneous dilatational tracheostomy (PDT). Methods: Among 2 560 patients who undergoing PDT in Tianjin Huanhu Hospital from January 2020 to January 2025, 4 patients(3 males and 1 female,age between 37 to 71 years old,with a median age of 50.5)developed fatal hemorrhage. Clinical data, including age, gender, type of disease, bleeding time, bleeding site, and final outcome were retrospectively reviewed. Additionally, potential causes of fatal hemorrhage following PDT were analyzed. Results: Three patients experienced fatal subclavian artery hemorrhage. Two of them (one with obesity and the other with an elongated neck) died of massive hemorrhage within one month postoperatively. The third patient with subclavian artery injury succumbed to bleeding six months after PDT. The fourth patient died from thyroid artery bleeding secondary to tracheostomy stoma infection. Conclusion: Fatal bleeding following PDT carries a grave prognosis and a extremely high mortality. Given its lethality, primary prevention through meticulous patient selection, anatomical assessment, and procedural technique remains paramount.

Objective: To summarize the clinical features and diagnostic/management experience of diagnosis and treatment of IgG4-related disease (IgG4-RD) presenting with initial head-and-neck organ involvement. Methods: A retrospective analysis was conducted of 9 IgG4-RD patients (7 males, 2 females; aged 54-65 years, with a median of 59 years) treated in department of Otorhinolaryngology Head and Neck Surgery of the Second Affiliated Hospital of Shanxi Medical University from May 2018 to January 2025. Descriptive statistics were used to analyze involved organs, clinical manifestations, imaging findings, histopathology, therapy and outcome. Results: The initial head-and-neck involvement comprised bilateral submandibular glands in 4 cases, nasal cavity/paranasal sinuses in 2, unilateral parotid gland in 1, larynx in 1, and both nasal septum and subglottis in 1. Serum IgG4 levels were elevated in all cases, the range was 2 380-17 000 mg/L. All pathological findings fulfilled the diagnostic criteria for IgG4-RD. All patients were treated with systemic corticosteroids. During 6-80 months of follow-up, one case of relapse occurred and was controlled by increasing steroid dose and prolonging treatment course. Conclusions: IgG4-RD is rare in clinic and their presentations are highly variable due to different organs involved. The head-and-neck region is one of the common initial sites. Obtaining a pathological diagnosis is particularly critical. Glucocorticoids remain the first-line therapy for IgG4-RD.

Objective: To develop a deep learning-assisted diagnostic model based on white light imaging (WLI) and narrow band imaging (NBI) endoscopic images for nasopharyngeal carcinoma (NPC), and to further explore its potential clinical application in remote diagnostic systems. Methods: Endoscopic image data were retrospectively collected from 1 262 subjects who underwent nasopharyngeal screening at the Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University between July 2018 and September 2021. A total of 9 370 WLI and 5 558 NBI images were included. NPC recognition models were developed and validated separately for WLI and NBI using the InceptionResNetV2 deep neural network architecture. Results: An artificial intelligence-based diagnostic model for NPC (NPC-CAD) was successfully established, achieving diagnostic accuracies of 96.03% and 91.16% in the WLI and NBI test sets, with AUC of 0.99 and 0.96 respectively. A cloud-based intelligent diagnostic platform was subsequently developed and demonstrated preliminary feasibility and performance in remote healthcare scenarios. Conclusion: This study presents a novel AI-assisted diagnostic model for NPC that integrates deep learning with nasal endoscopic image analysis and introduces a prototype cloud-based diagnostic platform. The model shows strong potential to improve early screening efficiency and to reduce missed diagnoses, particularly in resource-limited settings.