Inner Ear Breaches from Vestibular Schwannoma Surgery: Revisiting the Incidence of Otologic Injury from Retrosigmoid and Middle Cranial Fossa Approaches.

OBJECTIVE: To assess the rate of iatrogenic injury to the inner ear in vestibular schwannoma resections. STUDY DESIGN: Retrospective case review. SETTING: Multiple academic tertiary care hospitals. PATIENTS: Patients who underwent retrosigmoid or middle cranial fossa approaches for vestibular schwannoma resection between 1993 and 2015. INTERVENTION: Diagnostic with therapeutic implications. MAIN OUTCOME MEASURE: Drilling breach of the inner ear as confirmed by operative note or postoperative computed tomography (CT). RESULTS: 21.5% of patients undergoing either retrosigmoid or middle fossa approaches to the internal auditory canal were identified with a breach of the vestibulocochlear system. Because of the lack of postoperative CT imaging in this cohort, this is likely an underestimation of the true incidence of inner ear breaches. Of all postoperative CT scans reviewed, 51.8% had an inner ear breach. As there may be bias in patients undergoing postoperative CT, a middle figure based on sensitivity analyses estimates the incidence of inner ear breaches from lateral skull base surgery to be 34.7%. CONCLUSIONS: A high percentage of vestibular schwannoma surgeries via retrosigmoid and middle cranial fossa approaches result in drilling breaches of the inner ear. This study reinforces the value of preoperative image analysis for determining risk of inner ear breaches during vestibular schwannoma surgery and the importance of acquiring CT studies postoperatively to evaluate the integrity of the inner ear.

Iatrogenic inner ear dehiscence associated with lateral skull base surgery: a systematic analysis of drilling injuries and their causal factors.

PURPOSE: Drilling injuries of the inner ear are an underreported complication of lateral skull base (LSB) surgery. Inner ear breaches can cause hearing loss, vestibular dysfunction, and third window phenomenon. This study aims to elucidate primary factors causing iatrogenic inner ear dehiscences (IED) in 9 patients who presented to a tertiary care center with postoperative symptoms of IED following LSB surgery for vestibular schwannoma, endolymphatic sac tumor, Meniere's disease, paraganglioma jugulare, and vagal schwannoma. METHODS: Utilizing 3D Slicer image processing software, geometric and volumetric analysis was applied to both preoperative and postoperative imaging to identify causal factors iatrogenic inner ear breaches. Segmentation analyses, craniotomy analyses, and drilling trajectory analyses were performed. Cases of retrosigmoid approaches for vestibular schwannoma resection were compared to matched controls. RESULTS: Excessive lateral drilling and breach of a single inner ear structure occurred in 3 cases undergoing transjugular (n=2) and transmastoid (n=1) approaches. Inadequate drilling trajectory breaching ≥1 inner ear structure occurred in 6 cases undergoing retrosigmoid (n=4), transmastoid (n=1), and middle cranial fossa approaches (n=1). In retrosigmoid approaches the 2-cm visualization window and craniotomy limits did not provide drilling angles to the entire tumor without causing IED in comparison to matched controls. CONCLUSIONS: Inappropriate drill depth, errant lateral drilling, inadequate drill trajectory, or a combination of these led to iatrogenic IED. Image-based segmentation, individualized 3D anatomical model generation, and geometric and volumetric analyses can optimize operative plans and possibly reduce inner ear breaches from lateral skull base surgery.

Reducing the foreign body response on human cochlear implants and their materials in vivo with photografted zwitterionic hydrogel coatings.

The foreign body response to implanted materials often complicates the functionality of sensitive biomedical devices. For cochlear implants, this response can reduce device performance, battery life and preservation of residual acoustic hearing. As a permanent and passive solution to the foreign body response, this work investigates ultra-low-fouling poly(carboxybetaine methacrylate) (pCBMA) thin film hydrogels that are simultaneously photo-grafted and photo-polymerized onto polydimethylsiloxane (PDMS). The cellular anti-fouling properties of these coatings are robustly maintained even after six-months subcutaneous incubation and over a broad range of cross-linker compositions. On pCBMA-coated PDMS sheets implanted subcutaneously, capsule thickness and inflammation are reduced significantly in comparison to uncoated PDMS or coatings of polymerized poly(ethylene glycol dimethacrylate) (pPEGDMA). Further, capsule thickness is reduced over a wide range of pCBMA cross-linker compositions. On cochlear implant electrode arrays implanted subcutaneously for one year, the coating bridges over the exposed platinum electrodes and dramatically reduces the capsule thickness over the entire implant. Coated cochlear implant electrode arrays could therefore lead to persistent improved performance and reduced risk of residual hearing loss. More generally, the in vivo anti-fibrotic properties of pCBMA coatings also demonstrate potential to mitigate the fibrotic response on a variety of sensing/stimulating implants. STATEMENT OF SIGNIFICANCE: This article presents, for the first time, evidence of the in vivo anti-fibrotic effect of zwitterionic hydrogel thin films photografted to polydimethylsiloxane (PDMS) and human cochlear implant arrays. The hydrogel coating shows no evidence of degradation or loss of function after long-term implantation. The coating process enables full coverage of the electrode array. The coating reduces fibrotic capsule thickness 50-70% over a broad range of cross-link densities for implantations from six weeks to one year.

Hajek and Hirsch: Otolaryngology Pioneers of Endonasal Transsphenoidal Pituitary Surgery.

OBJECTIVE: To evaluate the historical descriptive origins of the extracranial transnasal transsphenoidal route to the sphenoid sinus and sella turcica focusing on the works of two otolaryngologists: Markus Hajek (1861-1941) and Oskar Hirsch (1877-1965). DATA SOURCES: A collection of primary references of author publications, and contemporary references and textbooks. REVIEW METHODS: Primary references were reviewed with specific focus on surgical routes to the sphenoid sinus and sella turcica. Chronology was cross-referenced with contemporary publications by contemporaries. Translations from German were conducted by author AM when necessary. RESULTS: Markus Hajek elegantly described the surgical approach to the posterior ethmoids and sphenoid sinus in 1904 using a transnasal route. Building on this foundation, Oskar Hirsch described the fully extracranial endonasal transethmoid transsphenoidal approach in 1909. He was first to describe surgical entrance to the sella using this exclusively unilateral endonasal route, which he demonstrated on a cadaver. He reports performing this procedure on a live patient in April, 1910, under local anesthesia in stages over 5 weeks. For better exposure, Hirsch consolidated his method with Killian's submucosal window resection of the posterior nasal septum allowing for bilateral access to the sphenoid sinus and sella, and completed a single stage procedure on a patient in June 1910. CONCLUSION: Oskar Hirsch was the first to describe and perform a stepwise surgical approach to the sella using an exclusively extracranial, endonasal, transethmoid, and transsphenoidal approach. He built upon his mentor Markus Hajek's approaches to the posterior ethmoid cells and sphenoid sinus. LEVEL OF EVIDENCE: NA: Background information, synthesis from multiple sources emphasizing factual information Laryngoscope, 133:807-813, 2023.

A novel dual-network architecture for mixed-supervised medical image segmentation.

In medical image segmentation tasks, deep learning-based models usually require densely and precisely annotated datasets to train, which are time-consuming and expensive to prepare. One possible solution is to train with the mixed-supervised dataset, where only a part of data is densely annotated with segmentation map and the rest is annotated with some weak form, such as bounding box. In this paper, we propose a novel network architecture called Mixed-Supervised Dual-Network (MSDN), which consists of two separate networks for the segmentation and detection tasks respectively, and a series of connection modules between the layers of the two networks. These connection modules are used to extract and transfer useful information from the detection task to help the segmentation task. We exploit a variant of a recently designed technique called 'Squeeze and Excitation' in the connection module to boost the information transfer between the two tasks. Compared with existing model with shared backbone and multiple branches, our model has flexible and trainable feature sharing fashion and thus is more effective and stable. We conduct experiments on 4 medical image segmentation datasets, and experiment results show that the proposed MSDN model outperforms multiple baselines.

Mixed-Supervised Dual-Network for Medical Image Segmentation.

Deep learning based medical image segmentation models usually require large datasets with high-quality dense segmentations to train, which are very time-consuming and expensive to prepare. One way to tackle this difficulty is using the mixed-supervised learning framework, where only a part of data is densely annotated with segmentation label and the rest is weakly labeled with bounding boxes. The model is trained jointly in a multi-task learning setting. In this paper, we propose Mixed-Supervised Dual-Network (MSDN), a novel architecture which consists of two separate networks for the detection and segmentation tasks respectively, and a series of connection modules between the layers of the two networks. These connection modules are used to transfer useful information from the auxiliary detection task to help the segmentation task. We propose to use a recent technique called 'Squeeze and Excitation' in the connection module to boost the transfer. We conduct experiments on two medical image segmentation datasets. The proposed MSDN model outperforms multiple baselines.