Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction.

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical-pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

Outpatient care for facial palsy-a survey on patient satisfaction in uni- and interdisciplinary approaches.

Objective: The various causes of facial palsy, diagnostic methods and treatment approaches frequently involve different medical specialities. Nevertheless, there exist only few specialized consultation and therapy services for patients with facial palsy (FP) in Germany. The aim of the present study was to evaluate factors affecting quality of life (QoL) and treatment satisfaction of patients presenting to an interdisciplinary facial nerve outpatient clinic. Methods: The study analyzed patients presenting to the interdisciplinary facial palsy outpatient clinic in Tuebingen between February 2019 and December 2022. General satisfaction and QoL was estimated by numerous self-rating questionnaires: ZUF-8, SF-36, FDI, FaCE, PHQ-9. An ANOVA was performed to analyze determinants affecting the ZUF-8. Correlation analyses between cause and regeneration of FP as well as questionnaire scores were performed. Results were compared with a group of patients who were managed in an unidisciplinary setting. Results: In total, 66 patients with FP were enrolled. FP patients showed increased levels of depression (PHQ-9: 14.52 ± 3.8) correlating with recovery of the palsy (p = 0.008), FaCE (p < 0.001) and FDI ratings (p < 0.001). There was a high level of satisfaction with the services provided during the uni-and interdisciplinary consultation (ZUF-8: 24.59 ± 6.2), especially among the 12/66 patients who received reconstructive, surgical treatment. However, some patients requested more psychological and ophthalmological support. Conclusion: High levels of treatment satisfaction can be achieved in both an uni-and interdisciplinary setting. However, multimodal therapy approaches should be applied, considering physical and psychological aspects. In the absence of recovery, surgical interventions must be considered as treatment options. Further studies should continue to investigate potential differences between uni-and interdisciplinary treatment.

Defining benchmark outcomes for mesial temporal lobe epilepsy surgery: A global multicenter analysis of 1119 cases.

OBJECTIVE: Benchmarking has been proposed to reflect surgical quality and represents the highest standard reference values for desirable results. We sought to determine benchmark outcomes in patients after surgery for drug-resistant mesial temporal lobe epilepsy (MTLE). METHODS: This retrospective multicenter study included patients who underwent MTLE surgery at 19 expert centers on five continents. Benchmarks were defined for 15 endpoints covering surgery and epilepsy outcome at discharge, 1 year after surgery, and the last available follow-up. Patients were risk-stratified by applying outcome-relevant comorbidities, and benchmarks were calculated for low-risk ("benchmark") cases. Respective measures were derived from the median value at each center, and the 75th percentile was considered the benchmark cutoff. RESULTS: A total of 1119 patients with a mean age (range) of 36.7 (1-74) years and a male-to-female ratio of 1:1.1 were included. Most patients (59.2%) underwent anterior temporal lobe resection with amygdalohippocampectomy. The overall rate of complications or neurological deficits was 14.4%, with no in-hospital death. After risk stratification, 377 (33.7%) benchmark cases of 1119 patients were identified, representing 13.6%-72.9% of cases per center and leaving 742 patients in the high-risk cohort. Benchmark cutoffs for any complication, clinically apparent stroke, and reoperation rate at discharge were ≤24.6%, ≤.5%, and ≤3.9%, respectively. A favorable seizure outcome (defined as International League Against Epilepsy class I and II) was reached in 83.6% at 1 year and 79.0% at the last follow-up in benchmark cases, leading to benchmark cutoffs of ≥75.2% (1-year follow-up) and ≥69.5% (mean follow-up of 39.0 months). SIGNIFICANCE: This study presents internationally applicable benchmark outcomes for the efficacy and safety of MTLE surgery. It may allow for comparison between centers, patient registries, and novel surgical and interventional techniques.

Predictive value of facial motor-evoked potential and electromyography for facial motor function in vestibular schwannoma surgery.

PURPOSE: Intraoperative neuromonitoring (IONM) aims to preserve facial nerve (FN) function during vestibular schwannoma (VS) surgery. However, current techniques such as facial nerve motor evoked potentials (FNMEP) or electromyography (fEMG) alone are limited in predicting postoperative facial palsy (FP). The objective of this study was to analyze a compound fEMG/FNMEP approach. METHODS: Intraoperative FNMEP amplitude and the occurrence of fEMG-based A-trains were prospectively determined for the orbicularis oris (ORI) and oculi (OCU) muscle in 322 VS patients. Sensitivity and specificity of techniques to predict postoperative FN function were calculated. Confounding factors as tumor size, volume of intracranial air, or IONM duration were analyzed. RESULTS: A relevant immediate postoperative FP was captured in 105/322 patients with a significant higher risk in large VS. While fEMG demonstrated a high sensitivity (77% and 86% immediately and 15 month postoperative, respectively) for identifying relevant FP, specificity was low. In contrast, FNMEP have a significantly higher specificity of 80.8% for predicting postoperative FP, whereas the sensitivity is low. A retrospective combination of techniques demonstrated still an incorrect prediction of FP in ~ 1/3 of patients. CONCLUSIONS: FNMEP and fEMG differ in sensitivity and specificity to predict postoperative FP. Although a combination of IONM techniques during VS surgery may improve prediction of FN function, current techniques are still inaccurate. Further development is necessary to improve IONM approaches for FP prediction.

Robot-assisted stereoencephalography vs subdural electrodes in the evaluation of temporal lobe epilepsy.

OBJECTIVE: Invasive video-electroencephalography (iVEEG) is the gold standard for evaluation of refractory temporal lobe epilepsy before second stage resective surgery (SSRS). Traditionally, the presumed seizure onset zone (SOZ) has been covered with subdural electrodes (SDE), a very invasive procedure prone to complications. Temporal stereoelectroencephalography (SEEG) with conventional frame-based stereotaxy is time-consuming and impeded by the geometry of the frame. The introduction of robotic assistance promised a simplification of temporal SEEG implantation. However, the efficacy of temporal SEEG in iVEEG remains unclear. The aim of this study was therefore to describe the efficiency and efficacy of SEEG in iVEEG of temporal lobe epilepsy. METHODS: This retrospective study enrolled 60 consecutive patients with medically intractable epilepsy who underwent iVEEG of a potential temporal SOZ by SDE (n = 40) or SEEG (n = 20). Surgical time efficiency was analyzed by the skin-to-skin time (STS) and the total procedure time (TPT) and compared between groups (SDE vs SEEG). Surgical risk was depicted by the 90-day complication rate. Temporal SOZ were treated by SSRS. Favorable outcome (Engel°1) was assessed after 1 year of follow-up. RESULTS: Robot-assisted SEEG significantly reduced the duration of surgery (STS and TPT) compared to SDE implantations. There was no significant difference in complication rates. Notably, all surgical revisions in this study were attributed to SDE. Unilateral temporal SOZ was detected in 34/60 cases. Of the 34 patients, 30 underwent second stage SSRS. Both SDE and SEEG had a good predictive value for the outcome of temporal SSRS with no significant group difference. SIGNIFICANCE: Robot-assisted SEEG improves the accessibility of the temporal lobe for iVEEG by increasing surgical time efficiency and by simplifying trajectory selection without losing its predictive value for SSRS.