Objective: To assess the prevalence of depression, anxiety, insomnia, and somatic symptom disorder (SSD) in patients with benign and malignant sinonasal tumors.
Materials And Methods: Pretreatment patients with sinonasal tumors were prospectively recruited on the rhinology ward of a tertiary hospital from July 2021 to March 2022. The electronic questionnaire which contains the rhinological symptom scale, the 22-item Sinonasal Outcome Test (SNOT-22) Scale, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), the Insomnia Severity Index (ISI), and the Patient Health Questionnaire-15 (PHQ-15) was filled out by patients at admission. The associations between the scores of symptom/SNOT-22 and psychometric tests were assessed by the Pearson correlation coefficient () and simple linear regression. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of the SNOT-22 score in predicting psychiatric disorders.
Results: Thirteen patients with benign sinonasal tumors and 15 patients with malignant sinonasal tumors were recruited. The benign and malignant groups did not differ significantly regarding symptomatology and mental wellbeing. Of the total patients, 9 were at risk of depression (PHQ-9 > 4), 10 were at risk of anxiety (GAD-7 > 4), 11 were at risk of insomnia (ISI > 7), and 11 were at risk of SSD (PHQ-15 > 4). The overall symptom, facial pain/pressure, postnasal drip, and SNOT-22 scores were positively associated with scores of psychometric tests. Patients with a high SNOT-22 score (>18) are likely to be affected by comorbid psychiatric disorders. When interpreting the results of this study, it should be noted that screening tools, not diagnostic tools, were used to identify psychiatric risk.
Conclusion: Depression, anxiety, insomnia, and SSD are prevalent in patients with sinonasal tumors. Otolaryngologists should have a low threshold to ask the patient about psychiatric symptoms, especially for patients with an SNOT-22 score > 18.
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http://dx.doi.org/10.3389/fpsyg.2024.1444522 | DOI Listing |
Immunooncol Technol
December 2024
National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark.
Background: Despite significant advancements in the treatment of malignant melanoma, metastatic mucosal melanoma remains a therapeutic challenge due to its complex pathogenesis, distinct pathological characteristics, and limited response to immunotherapy. Combining different immunotherapeutic approaches offers a potential strategy to address these challenges. Tumor-infiltrating lymphocyte (TIL) therapy and oncolytic virus therapy represent promising treatment modalities that may synergize with each other.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Department of Otolaryngology/Head & Neck Surgery, Zucker School of Medicine, Hofstra University, New York, NY 11040, USA.
Squamous cell carcinoma is the most common malignancy affecting the sinonasal tract. Local recurrence is the main pattern of treatment failure, affecting nearly half of patients treated for primary sinonasal squamous cell carcinoma (SNSCC). Due to disease rarity and heterogeneity of practices, there are limited guidelines for how to diagnose and care for these patients.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Otorhinolaryngology, Medical University of Graz, Auenbruggerplatz 26, 8010 Graz, Austria.
This report describes a rare occurrence of benign fibrous histiocytoma in the frontal sinus of a 38-year-old male. The patient presented with acute symptoms, including sudden-onset headache, nausea, and general discomfort, although neurological, otorhinolaryngological and laboratory examinations showed no abnormalities. A cranial CT scan revealed a cystic, osteodestructive lesion measuring 2.
View Article and Find Full Text PDFObjective: Evaluate the feasibility of the midface degloving approach (MDA) in total maxillectomy without orbital exenteration (TMWOE) and reconstruction for sino-nasal neoplasms.
Study Design: Retrospective case series.
Setting: Tertiary referral center.
Int Forum Allergy Rhinol
January 2025
Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, California, USA.
Background: We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on endoscopic appearance.
Methods: A convolutional neural network-based model was constructed from nasal endoscopy images from patients evaluated at an otolaryngology center between 2013 and 2024. Images were classified into four groups: normal endoscopy, nasal polyps, benign, and malignant tumors.
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