Background: One of the most frequent complications of spinal surgery is accidental dural tears (ADTs). Minimal access surgical techniques (MAST) have been described as a promising approach to minimizing such complications. ADTs have been studied extensively in connection with open spinal surgery, but there is less literature on minimally invasive spinal surgery (MISS).Materials and Methods: We reviewed 187 patients who had undergone degenerative lumbar spinal surgery using minimally invasive spinal fusions techniques. We analyzed the influence of age, Body Mass Index (BMI), smoking, diabetes, and previous surgery on the rate of ADTs in MISS.Results: Twenty-two patients (11.764%) suffered from an ADT. We recommended bed rest for two and a half to 5 days, depending on the type of repair required and the amount of cerebrospinal fluid (CSF) leakage. We could not find any statistically significant correlation between ADTs and age (p = 0.34,), BMI (p = 0.92), smoking (p = 0.46), and diabetes (p = 0.71). ADTs were significantly more frequent in cases of previous surgery (p < 0.001). None of the patients developed a transcutaneous CSF leak or post-operative infection.Conclusions: The frequency of ADTs in MISS appears comparable to that encountered when using open surgical techniques. Additionally, MAST produces less dead space along the corridor to the spine. Such reduced dead space may not be enough for pseudomeningocele to occur, cerebrospinal fluid to accumulate, and fistula to form. MAST, therefore, provides a certain amount of protection.

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79–0.86) for the training cohort over 10 years and 0.74 (Cl 0.64–0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73–0.82) for training and 0.67 (Cl 0.57–0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

Objectives: To review the surgical results and to identify possible parameters influencing the clinical outcomes in an unselected patient collective undergoing minimally invasive lumbar interbody fusion in a spinal care unit.Methods: A total of 229 adult patients who underwent minimally invasive lumbar spinal fusion between 2008 and 2016 were included in this retrospective analysis. Lumbar fusion was performed using transforaminal interbody fusion (TLIF) devices and posterolateral fusion. To eliminate confounding parameters, in all patients interbody fusion was indicated by lumbar degenerative pathologies, and surgery was performed using the same fusion device. Treatment efficacy was evaluated using scores describing pain (visual analogue scale [VAS]) and health impairment (EQ‐5D, Oswestry Disability Index [ODI]). The influence of patient age, obesity, active smoking status, and co‐morbidities on clinical outcome and perioperative complications was analyzed.Results: The patient population reviewed had improved VAS (P_{(leg pain)} ≤ 0.0001, P_{(back pain)} ≤ 0.0001), ODI (P ≤ 0.0001), EQ‐VAS (P ≤ 0.0001), and EQ‐5D subscales “mobility”, “self‐care”, “pain”, and “anxiety” (P_(mobility) ≤ 0.0001, P_{(self‐care)} = 0.41, P_(pain) ≤ 0.0001, P_(anxiety) = 0.011) postoperatively. Neither advanced patient age, nor increased body mass index (BMI), hypertension, or active smoking status had a significantly limiting influence on the success of minimally invasive spinal surgeries (MIS). Duration of surgery strongly correlated with the number of spinal levels treated and with intraoperative blood loss (r = 0.774, P ≤ 0.0001, n = 208). Weak positive correlations were found between patient age and duration of surgery (r = 0.184, P = 0.005, n = 229), intraoperative blood loss (r = 0.165, P = 0.012, n = 229), and duration of hospitalization (r = 0.270, P ≤ 0.0001, n = 228), respectively. When compared to non‐smokers, smokers were younger (P ≤ 0.0001), and had a significantly lower BMI (P = 0.001), shorter durations of surgery (P ≤ 0.0001), decreased intraoperative blood loss (P = 0.022), and shorter hospital stays (P = 0.006), respectively. Complications occurred in 17 patients (7%) and were not affected by patient age, BMI, hypertension, or active smoking status.Conclusion: Minimally invasive spinal surgery is a safe and effective treatment option and may be superior to open surgery in subpopulations with significant co‐morbidities and risk factors, such as elderly and obese patients as well as patients with an active smoking status.

Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model’s predictive quality when applied to future patients. ML models for the prediction of the Glasgow outcome scale, modified Rankin Scale (mRS), and new transient or permanent neurological deficits (output variables) were created from all UIA patients that underwent microsurgery at the Kepler University Hospital Linz (Austria) between 2002 and 2020 (n = 466), based on 18 patient- and 10 aneurysm-specific preoperative parameters (input variables). Train-test splitting was performed with a temporal split for outcome prediction in microsurgical therapy of UIA. Moreover, an external validation was conducted on an independent external data set (n = 256) of the Department of Neurosurgery, University Medical Centre Hamburg-Eppendorf. In total, 722 aneurysms were included in this study. A postoperative mRS > 2 was best predicted by a quadratic discriminant analysis (QDA) estimator in the internal test set, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.87 ± 0.03 and a sensitivity and specificity of 0.83 ± 0.08 and 0.71 ± 0.07, respectively. A Multilayer Perceptron predicted the post- to preoperative mRS difference > 1 with a ROC-AUC of 0.70 ± 0.02 and a sensitivity and specificity of 0.74 ± 0.07 and 0.50 ± 0.04, respectively. The QDA was the best model for predicting a permanent new neurological deficit with a ROC-AUC of 0.71 ± 0.04 and a sensitivity and specificity of 0.65 ± 0.24 and 0.60 ± 0.12, respectively. Furthermore, these models performed significantly better than the classic logistic regression models (p < 0.0001). The present results showed good performance in predicting functional and clinical outcomes after microsurgical therapy of UIAs in the internal data set, especially for the main outcome parameters, mRS and permanent neurological deficit. The external validation showed poor discrimination with ROC-AUC values of 0.61, 0.53 and 0.58 respectively for predicting a postoperative mRS > 2, a pre- and postoperative difference in mRS > 1 point and a GOS < 5. Therefore, generalizability of the models could not be demonstrated in the external validation. A SHapley Additive exPlanations (SHAP) analysis revealed that this is due to the most important features being distributed quite differently in the internal and external data sets. The implementation of newly available data and the merging of larger databases to form more broad-based predictive models is imperative in the future.

The amygdala has long been implicated in the pathophysiology of human temporal lobe epilepsy (TLE). The different nuclei of this complex structure are interconnected and share reciprocal connections with the hippocampus and other brain structures, partly via the entorhinal cortex. Expression of GABA_A receptor subunits α1, α2, α3, α5, β2, β2/3, and γ2 was evaluated by immunohistochemistry in amygdala specimens and the entorhinal cortex of 12 TLE patients and 12 autopsy controls. A substantial decrease in the expression of α1, α2, α3, and β2/3 subunits was found in TLE cases, accompanied by an increase of γ2 subunit expression in many nuclei. In the entorhinal cortex, the expression of all GABA_A receptor subunits was decreased except for the α1 subunit, which was increased on cellular somata. The overall reduction in α subunit expression may lead to decreased sensitivity to GABA and its ligands and compromise phasic inhibition, whereas upregulation of the γ2 subunit might influence clustering and kinetics of receptors and impair tonic inhibition. The description of these alterations in the human amygdala is important for the understanding of network changes in TLE as well as the development of subunit-specific therapeutic agents for the treatment of this disease.