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OBJECTIVECranial nerve (CN) preservation remains a challenge for skull base neurosurgeons, and neurophysiological intraoperative monitoring presents many methods for CN identification and mapping. The blink reflex, which is the electrophysiological representation of the corneal reflex, can be used to test both trigeminal and facial nerve function. The objective of this study was to present a method for obtaining a reliable blink reflex response and maintaining it during the course of a procedure. METHODSA method for robust blink reflex recording is presented. Electrode placement, recording parameters, stimulation parameters, anesthetic considerations, and reliability troubleshooting are described. RESULTSThis method has been iteratively developed at the authors’ institution across multiple sites for more than 5 years. The blink reflex was monitored in multiple cranial approaches and for various pathologies. The most common cases monitored were vestibular schwannoma resections and microvascular decompressions. The most common cranial approaches were the translabyrinthine, retrosigmoid/suboccipital, and middle cranial fossa approaches. CONCLUSIONSTo gain a more comprehensive understanding of the clinical utility of the blink reflex in surgical decision-making and outcome prediction, prospective studies involving larger patient cohorts are warranted. This report outlines a reproducible methodology and invites validation and constructive input from the broader neurosurgical and neuromonitoring communities. 

OBJECTIVECancer patients often experience high levels of distress, which are particularly pronounced in the perioperative period. However, there is a dearth of research on distress rates in patients with metastatic spine disease (MSD). This study aims to assess pre- and postoperative distress levels in patients with MSD undergoing surgical intervention, as well as the association between distress and sociodemographic factors. METHODSThe authors retrospectively queried electronic medical records from a single institution for demographic and clinical data on patients with MSD who underwent surgical intervention between 2015 and 2023. Data included pre- (within 30 days of surgery) and postoperative (within 30 and 90 days of surgery) National Comprehensive Cancer Network’s distress thermometer (NCCN-DT) scores. The proportion of patients with clinically significant distress (DT score ≥ 4) at each time point was examined, as well as changes between baseline distress and distress 30 days postoperatively. The association between clinically significant distress and sex, age, race/ethnicity, and marital status was assessed. A p value < 0.05 was considered significant. RESULTSThe study identified 265 patients with complete NCCN-DT questionnaires. Nearly half (47.5%) of the patients were female, with 66.0% identifying as Caucasian/White. The mean (± standard deviation) age at surgery was 61.4 ± 12.1 years. Preoperatively, the mean distress score was 3.6 ± 3.1 (range 0–10), with 89 (46.4%) of 192 patients reporting moderate to severe distress (DT ≥ 4). The mean distress score at 30 days postoperatively was 3.2 ± 3.0 (range 0–10), with 43.8% of patients reporting moderate to severe distress. At 90 days postoperatively, the mean distress score was 2.3 ± 2.5 (range 0–9) with 26.6% of patients reporting moderate to severe levels. Non-White patients had significantly higher preoperative distress than their White counterparts (p = 0.03). CONCLUSIONSDistress is a common experience among patients with MSD undergoing surgical intervention. Preoperatively, nearly half of these patients report moderate to severe distress, with distress levels remaining elevated through the 1st month after surgery. These findings highlight the critical need for timely psychosocial interventions to address distress at key stages of the surgical process. Race-based differences in distress rates emphasize the importance of developing targeted support strategies for more vulnerable groups. 

Abstract BackgroundPrognostic indices for patients with brain metastases (BM) are needed to individualize treatment and stratify clinical trials. Two frequently used tools to estimate survival in patients with BM are the recursive partitioning analysis (RPA) and the diagnosis-specific graded prognostic assessment (DS-GPA). Given recent advances in therapies and improved survival for patients with BM, this study aims to validate and analyze these 2 models in a modern cohort. MethodsPatients diagnosed with BM were identified via our institution’s Tumor Board meetings. Data were retrospectively collected from the date of diagnosis with BM. The concordance of the RPA and GPA was calculated using Harrell’s C index. A Cox proportional hazards model with backwards elimination was used to generate a parsimonious model predictive of survival. ResultsOur study consisted of 206 patients diagnosed with BM between 2010 and 2019. The RPA had a prediction performance characterized by Harrell’s C index of 0.588. The DS-GPA demonstrated a Harrell’s C index of 0.630. A Cox proportional hazards model assessing the effect of age, presence of lung, or liver metastases, and Eastern Cooperative Oncology Group (ECOG) performance status score of 3/4 on survival yielded a Harrell’s C index of 0.616. Revising the analysis with an uncategorized ECOG demonstrated a C index of 0.648. ConclusionsWe found that the performance of the RPA remains unchanged from previous validation studies a decade earlier. The DS-GPA outperformed the RPA in predicting overall survival in our modern cohort. Analyzing variables shared by the RPA and DS-GPA produced a model that performed analogously to the DS-GPA. 

Abstract Background and Importance Tegmen defects associated with cerebrospinal fluid (CSF) leaks are a rare pathology that can result in severe complications if left untreated. There is no universal optimal surgical algorithm for repair, although the most common techniques are the middle fossa craniotomy (traditionally 25 cm2 in area), the transmastoid approach, or both. Here, we describe successful use of a keyhole mini-craniotomy, only 6 cm2 in area, without mastoidectomy or days of lumbar drainage. Clinical Presentation Three patients presented with right-sided CSF otorrhea and hearing loss, with varying sizes of tegmen defects and associated encephaloceles. Keyhole craniotomies measuring 3 × 2 cm were used to perform a multilayer repair comprising an intradural collagen dural substitute, extradural fascial graft, extradural collagen dural substitute, fibrin sealant, and sometimes bony reconstruction using partial thickness craniotomy grafting. All patients were discharged on postoperative day 1 or 2, with no recurrence of symptoms at 6 months. Conclusion The keyhole craniotomy approach does not sacrifice the extent of operative access for this pathology. This minimally invasive approach can likely be used more often without need for concomitant mastoidectomy, ultimately enabling shorter hospital stays and more rapid recovery. 

Endoscopic endonasal approaches (EEA) have become more prevalent for minimally invasive skull base and sinus surgeries. However, rigid scopes and tools significantly decrease the surgeon’s ability to operate in tight anatomical spaces and avoid critical structures such as the internal carotid artery and cranial nerves. This paper proposes a novel tendon-actuated concentric tube endonasal robot (TACTER) design in which two tendon-actuated robots are concentric to each other, resulting in an outer and inner robot that can bend independently. The outer robot is a unidirectionally asymmetric notch (UAN) nickel-titanium robot, and the inner robot is a 3D-printed bidirectional robot, with a nickel–titanium bending member. In addition, the inner robot can translate axially within the outer robot, allowing the tool to traverse through structures while bending, thereby executing follow-the-leader motion. A Cosserat-rod-based mechanical model is proposed that uses tendon tension of both tendon-actuated robots and the relative translation between the robots as inputs and predicts the TACTER tip position for varying input parameters. The model is validated with experiments, and a human cadaver experiment is presented to demonstrate maneuverability from the nostril to the sphenoid sinus. This work presents the first tendon-actuated concentric tube (TACT) dexterous robotic tool capable of performing follow-the-leader motion within natural nasal orifices to cover workspaces typically required for a successful EEA. 

Optical coherence tomography (OCT) is an interferometric technique for micron-level imaging in biological and non-biological contexts. As a non-invasive, non-ionizing, and video-rate imaging modality, OCT is widely used in biomedical and clinical applications, especially ophthalmology, where it functions in many roles, including tissue mapping, disease diagnosis, and intrasurgical visualization. In recent years, the rapid growth of medical robotics has led to new applications for OCT, primarily for 3D free-space scanning, volumetric perception, and novel optical designs for specialized medical applications. This review paper surveys these recent developments at the intersection of OCT and robotics and organizes them by degree of integration and application, with a focus on biomedical and clinical topics. We conclude with perspectives on how these recent innovations may lead to further advances in imaging and medical technology. 

Manual surgical resection of soft tissue sarcoma tissue can involve many challenges, including the critical need for precise determination of tumor boundary with normal tissue and limitations of current surgical instrumentation, in addition to standard risks of infection or tissue healing difficulty. Substantial research has been conducted in the biomedical sensing landscape for development of non-human contact sensing devices. One such point-of-care platform, previously devised by our group, utilizes autofluorescence-based spectroscopic signatures to highlight important physiological differences in tumorous and healthy tissue. The following study builds on this work, implementing classification algorithms, including Artificial Neural Network, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors, to diagnose freshly resected murine tissue as sarcoma or healthy. Classification accuracies of over 93% are achieved with Logistic Regression, and Area Under the Curve scores over 94% are achieved with Support Vector Machines, delineating a clear way to automate photonic diagnosis of ambiguous tissue in assistance of surgeons. These interpretable algorithms can also be linked to important physiological diagnostic indicators, unlike the black-box ANN architecture. This is the first known study to use machine learning to interpret data from a non-contact autofluorescence sensing device on sarcoma tissue, and has direct applications in rapid intraoperative sensing.