Lymphepithelial carcinoma - a rare tumor of the larynx. Case Report.

Lymphoepithelioma was described in 1921 separately by Regaud and Schmincke as nests of non-keratinizing squamous cells embedded in a lymphoid stroma (Regaud) and isolated transitional cells scattered in lymphoid tissue resembling sarcoma (Schmincke). Lymphoepithelial tumors are the most common lesions of the nasopharynx, although they have also been reported in other localizations, such as the nasal cavity, maxillary sinus, the base of the tongue, parapharyngeal area, tonsils and thymus. Lymphoepithelioma of the larynx is extremely rare. We present a case of a 55-year-old patient treated due to this type of lesion to share our experience in the management of this type of malignancy and contribute to the field of rare laryngeal tumors diagnosis and treatment.

Intracranial otogenic complications in adults: new factors that influenced its onset, frequency and nature.

BACKGROUND: To compare the clinical features of two time cohorts of patients: "pre-COVID-19" and "COVID-19"-admitted as emergency with intracranial otogenic complications, with special regard to sigmoid sinus thrombosis (CVST). METHODS: Retrospective analysis of patients documentation concerning urgent procedures of intracranial otogenic complications at tertiary-referral otolaryngology department. Analysed database-pre-COVID-19 cohort (January-February 2019/2020): 1434 otological outpatient visits, 509 planned otosurgeries and 17 urgent otological procedures; COVID-19 cohort (March-April 2020/2021): 1150, 566 and 20 respectively. Overall intracranial complications: 5 and 9 respectively. Analysed outcome measures: incidence proportion of otogenic intracranial complications in relation to planned and urgent otosurgical procedures; incidence proportion of intracranial complications in relation to the total number of emergency and planned outpatient consultations and the total number of planned surgical procedures. RESULTS: There were 14 intracranial complications, 5 in the pre-COVID and 9 in the COVID cohort, including 1 and 5 sigmoid sinus thrombosis, respectively. Out of them, 3 and 5 patients reported a prior history of chronic otitis media, respectively. In COVID period, CVST was more prevalent, with 2 cases (22.2%) presenting solitary CVST, and 3 cases (33.3%) CVST and a simultaneous brain abscess or meningitis. CVST was much more frequent in the COVID period (p < 0.01). CONCLUSIONS: Despite the published data which suggest that CVST is a rare event associated with COVID-19 infection, based on our experience, CVST can be expected as a frequent component of intracranial otogenic complications during COVID-19 pandemic time. Trial registration This research study was conducted retrospectively from data obtained for clinical purposes. We consulted extensively with the Bioethics Committee at Poznan University of Medical Sciences who determined that our study did not need ethical approval. An official waiver of ethical approval was granted from the Bioethics Committee at Poznan University of Medical Sciences.

Endoluminal larynx anatomy model - towards facilitating deep learning and defining standards for medical images evaluation with artificial intelligence algorithms.

The pioneering nature of this work covers the answers to two questions: (1) Is an up-to-date anatomical model of the larynx needed for modern endoscopic diagnostics, and (2) can such a digital segmentation model be utilized for deep learning purposes. The idea presented in this article has never been proposed before, and this is a breakthrough in numerical approaches to aerodigestive videoendoscopy imaging. The approach described in this article assumes defining a process for data acquisition, integration, and segmentation (labeling), for the needs of a new branch of knowledge: digital medicine and digital diagnosis support expert systems. The first and crucial step of such a process is creating a digital model of the larynx, which has to be then validated utilizing multiple clinical, as well as technical metrics. The model will form the basis for further artificial intelligence (AI) requirements, and it may also contribute to the development of translational medicine.