The preservation of residual hearing after conventional CI is possible. Young age seems to have a positive impact on hearing preservation.

In carbon dioxide (CO2) laser surgery of the larynx, the potentially dangerous combination of laser-induced heat in an oxygen-enriched atmosphere typically occurs when jet ventilation is used or due to an insufficiently blocked endotracheal tube. Until now, no limitations for safe oxygen concentrations or laser intervals have been established. The aim of this study was to investigate and quantify the factors that may contribute to an airway fire in laryngeal laser surgery. Fat, muscle and cartilage were irradiated with a CO2 laser at 2, 4, 6 and 8 W in five different oxygen concentrations with and without smoke exhaustion. The time to ignition was recorded for each different experimental setup. Fat burnt fastest, followed by cartilage and muscle. The elevation of laser energy or oxygen concentration reduced the time to inflammation of any tissue. The elevation of oxygen by 10 % increases the risk of inflammation more than the elevation of laser power by 2 W. Under smoke exhaustion, inflammation and burning occurred delayed or were even inhibited at lower oxygen concentrations. Lasing in more than 50 % oxygen is comparatively dangerous and can cause airway fire in less than 5 s, especially when laser energies of more than 5 W are applied. In equal or lower than 50 % oxygen, an irradiation interval of 5 s can be considered a comparatively safe time limit to prevent inflammation in laryngeal laser surgery. Smoke exhaustion should always be applied.

BackgroundDeep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting.MethodsAn expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated.ResultsBlinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703).ConclusionsWe show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating.

Superparamagnetic iron oxide nanoparticles (SPIONs) feature distinct magnetic properties that make them useful and effective tools for various diagnostic, therapeutic and theranostic applications. In particular, their use in magnetic drug targeting (MDT) promises to be an effective approach for the treatment of various diseases such as cancer. At the cellular level, SPION uptake, along with SPION-mediated toxicity, represents the most important prerequisite for successful application. Thus, the present study determines SPION uptake, toxicity and biocompatibility in human head and neck tumor cell lines of the tongue, pharynx and salivary gland. Using magnetic susceptibility measurements, microscopy, atomic emission spectroscopy, flow cytometry, and plasma coagulation, we analyzed the magnetic properties, cellular uptake and biocompatibility of two different SPION types in the presence and absence of external magnetic fields. Incubation of cells with lauric acid and human serum albumin-coated nanoparticles (SPIONLA-HSA) resulted in substantial particle uptake with low cytotoxicity. In contrast, uptake of lauric acid-coated nanoparticles (SPIONLA) was substantially increased but accompanied by higher toxicity. The presence of an external magnetic field significantly increased cellular uptake of both particles, although cytotoxicity was not significantly increased in any of the cell lines. SPIONs coated with lauric acid and/or human serum albumin show different patterns of uptake and toxicity in response to an external magnetic field. Consequently, the results indicate the potential use of SPIONs as vehicles for MDT in head and neck cancer.