Long-term functional outcome and satisfaction of patients with an active middle ear implant for sensorineural hearing loss compared to a matched population with conventional hearing aids.

UNLABELLED: The objectives of the study were to review the results of an active middle ear implant for sensorineural hearing loss in patients who were unable to wear or did not benefit from conventional hearing aids in comparison to patients with a matched degree of hearing loss successfully fitted with a conventional hearing aid. A retrospective chart review of 10 patients (10 ears) after implantation of an active middle ear implant and 12 patients (13 ears) with conventional hearing aids in one tertiary referral center was performed. Intervention for sensorineural hearing loss was the implantation of an active middle ear implant in one group or fitting of conventional hearing aids in the other group. Outcome measures were pure-tone audiometry (auditory thresholds, functional gain), speech audiometry (Freiburg Monosyllabic Test in quiet and in noise) and a quality-of-life questionnaire (Glasgow Benefit Inventory). Average functional gain was 25.2 ± 8.6 and 14.6 ± 10.8 dB, speech recognition score in noise was 36.6 ± 18.4 and 31.2 ± 19.2 % and in quiet was 66.0 ± 23.2 and 61.5 ± 23.8 %, Glasgow Benefit Inventory total score was 38.3 ± 32.3 and 24.8 ± 22.2 in patients with active middle ear implants and conventional hearing aids, respectively. In two patient groups matched for degree of sensorineural hearing loss, active middle ear implants provided comparable speech recognition and superior functional gain and quality of life compared to conventional hearing aids. LEVEL OF EVIDENCE: 4.

A Four-Generational Report on Hereditary Head and Neck Paraganglioma.

Background This article investigates the inheritance, penetrance, clinical presentation, and therapeutic outcomes of hereditary head and neck paragangliomas (HNPGLs) by offering a four-generational report of an 18-member family affected by this rare condition. Methodology Information was compiled by examination of patients and a review of medical records and correspondence (retrospective case series). Results Six members of the 18-member family were diagnosed with HNPGL between 2002 and 2018. A known pathogenic point mutation in subunit D of the succinyl dehydrogenase complex (SDHD, c.317G>T, p.Gly106Val) was responsible for the tumor phenotype. The mutation could be revealed in seven family members, three diseased adults, one healthy adult, and three healthy children, out of the nine who consented to gene testing. The median age at diagnosis was 33.5 years (range: 22-50 years). Five of the eight primary tumors were glomus caroticum, two were glomus jugulare, and one was a glomus vagale tumor. The therapeutic approaches were multimodal and included embolization therapy, surgery, radiation, and watchful waiting. Follow-up was reported for five of the six patients (mean follow-up of 34.8 months after primary therapy); three showed no disease progression or recurrence. Conclusions This study exemplifies the autosomal dominant, parent-of-origin-dependent inheritance and the high disease penetrance in hereditary paraganglioma-pheochromocytoma syndromes. Six out of a total of eight adult descendants (75%) of the original SDHD mutation carrier developed tumors, and the morbidity associated with the disease as well as its therapy was especially high in late-diagnosed, advanced cases. This substantiates the necessity for early radiologic surveillance and genetic testing.

In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods.

Currently, there are no fast and accurate screening methods available for head and neck cancer, the eighth most common tumor entity. For this study, we used hyperspectral imaging, an imaging technique for quantitative and objective surface analysis, combined with deep learning methods for automated tissue classification. As part of a prospective clinical observational study, hyperspectral datasets of laryngeal, hypopharyngeal and oropharyngeal mucosa were recorded in 98 patients before surgery in vivo. We established an automated data interpretation pathway that can classify the tissue into healthy and tumorous using convolutional neural networks with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. Using 24 patients for testing, our 3D spatio-spectral Densenet classification method achieves an average accuracy of 81%, a sensitivity of 83% and a specificity of 79%.