[Interdisciplinary Treatment Of Tumorous And Tumour-Like Lesions Of Peripheral Nerves]. / Interdisziplinäre Behandlung von Raumforderungen in Assoziation zu peripheren Nerven: Tumore und tumorähnliche Läsionen.

Tumorous or tumour-like lesions of peripheral nerves are generally rare, heterogeneous and challenging to diagnose and treat. They may become apparent by a palpable swelling (lump) near nerves, sensory and/or motor deficits, pain to touch or neuropathic pain. In 91% of cases, tumours are benign. The differentiation of entities and their characteristics as well as a function-preserving resection strategy are highly relevant. Misdiagnosis and inadequate treatment can lead to severe deficits and pain syndromes. Benign tumours include schwannomas and neurofibromas, which can occur sporadically but can also be associated with neurogenetic tumour disposition syndromes if they occur more frequently. Rarer benign nerve tumours include perineuriomas, lipomas, aggressive fibrosis (desmoid tumours), paragangliomas and haemangiomas. Ganglion cysts are described as tumour-like lesions. The association of nerve tumours with neurogenetic syndromes and the correct classification of potentially malignant lesions such as MPNST (malignant peripheral nerve sheath tumour) or intermediate stages such as ANNUBPs (atypical neurofibromatous neoplasms with unknown biological potential) pose particular challenges. Interdisciplinarity is highly relevant for clinical treatment and a correct diagnosis. The aim of our work is to provide an overview of the relevant entities, diagnostic evaluation and contemporary treatment strategies based on the current data situation and taking into account the recently published interdisciplinary AWMF S2k guideline "Diagnosis and Treatment of Peripheral Nerve Tumours".

Enhancing the prediction for shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage using a machine learning approach.

Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid hemorrhage (aSAH) may decrease the duration of in-hospital stay and reduce the risk of catheter-associated meningitis. Machine learning (ML) may improve predictions of SDHC in comparison to traditional non-ML methods. ML models were trained for CHESS and SDASH and two combined individual feature sets with clinical, radiographic, and laboratory variables. Seven different algorithms were used including three types of generalized linear models (GLM) as well as a tree boosting (CatBoost) algorithm, a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net. The discrimination of the area under the curve (AUC) was classified (0.7 ≤ AUC < 0.8, acceptable; 0.8 ≤ AUC < 0.9, excellent; AUC ≥ 0.9, outstanding). Of the 292 patients included with aSAH, 28.8% (n = 84) developed SDHC. Non-ML-based prediction of SDHC produced an acceptable performance with AUC values of 0.77 (CHESS) and 0.78 (SDASH). Using combined feature sets with more complex variables included than those incorporated in the scores, the ML models NB and MLP reached excellent performances, with an AUC of 0.80, respectively. After adding the amount of CSF drained within the first 14 days as a late feature to ML-based prediction, excellent performances were reached in the MLP (AUC 0.81), NB (AUC 0.80), and tree boosting model (AUC 0.81). ML models may enable clinicians to reliably predict the risk of SDHC after aSAH based exclusively on admission data. Future ML models may help optimize the management of SDHC in aSAH by avoiding delays in clinical decision-making.

Thoracic Outlet Syndrome Part I: Systematic Review of the Literature and Consensus on Anatomy, Diagnosis, and Classification of Thoracic Outlet Syndrome by the European Association of Neurosurgical Societies' Section of Peripheral Nerve Surgery.

BACKGROUND: Although numerous articles have been published not only on the classification of thoracic outlet syndrome (TOS) but also on diagnostic standards, timing, and type of surgical intervention, there still remains some controversy because of the lack of level 1 evidence. So far, attempts to generate uniform reporting standards have not yielded conclusive results. OBJECTIVE: To systematically review the body of evidence and reach a consensus among neurosurgeons experienced in TOS regarding anatomy, diagnosis, and classification. METHODS: A systematic literature search on PubMed/MEDLINE was performed on February 13, 2021, yielding 2853 results. Abstracts were screened and classified. Recommendations were developed in a meeting held online on February 10, 2021, and refined according to the Delphi consensus method. RESULTS: Six randomized controlled trials (on surgical, conservative, and injection therapies), 4 "guideline" articles (on imaging and reporting standards), 5 observational studies (on diagnostics, hierarchic designs of physiotherapy vs surgery, and quality of life outcomes), and 6 meta-analyses were identified. The European Association of Neurosurgical Societies' section of peripheral nerve surgery established 18 statements regarding anatomy, diagnosis, and classification of TOS with agreement levels of 98.4 % (±3.0). CONCLUSION: Because of the lack of level 1 evidence, consensus statements on anatomy, diagnosis, and classification of TOS from experts of the section of peripheral nerve surgery of the European Association of Neurosurgical Societies were developed with the Delphi method. Further work on reporting standards, prospective data collections, therapy, and long-term outcome is necessary.

Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores.

Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input variables other than traditional clinico-radiographic features and to evaluate whether a higher performance for outcome prediction in aSAH can be achieved.

Super-selective cervical nerve root stimulation in contralateral C7 transfer: An intraoperative study.

OBJECTIVE: We designed this study using super-selective intraoperative cervical nerve root stimulation aiming to support decision making about complete or partial contralateral C7 (cC7) nerve root transfer in patients with multiple cervical root avulsion injury. METHODS: Super-selective intraoperative stimulations of anterior, lateral, medial and posterior aspect of C5-C8 nerve roots were performed. Compound muscle action potentials (CMAP) were recorded in the lateral part of the deltoid (DM), long head of biceps brachii (BCM), brachioradial (BRM), long head of triceps brachii (TCM), and extensor digitorum communis (EDC) muscle. Muscle strength was documented immediately after cC7 transfer procedures and on scheduled follow-up visits according to the Medical Research Council (MRC) scale. RESULTS: In the DM, stimulation of the posterior aspect of C5 resulted in the largest CMAP amplitudes (2.0 mV ±â€¯1.9; 80% ±â€¯28.3). The BCM CMAPs induced by the different aspects of C6 all revealed homogenous stimulation results. Stimulation of the lateral aspect of C7 induced the largest amplitude of TCM CMAPs (1.3 mV ±â€¯1.0; 67.1% ±â€¯43.3). CMAP amplitudes of individual muscles and individual contributions of cervical nerve roots to the TCM varied between subjects. Overall donor side morbidity was low, no permanent motor deficit occurred. CONCLUSION: A super-selective intraoperative cervical nerve root stimulation may help minimize donor side morbidity in transfer procedures. Individual differences of cervical nerve root innervation pattern need to be addressed in future electrophysiological studies. SIGNIFICANCE: Our study outlines individual differences of cervical nerve root innervation pattern.

Mechanisms, Treatment, and Patient Outcome of Iatrogenic Injury to the Brachial Plexus-A Retrospective Single-Center Study.

BACKGROUND: Injury to the brachial plexus is a devastating condition, with severe impairment of upper extremity function resulting in distinct disability. There are no systematic reports on epidemiology, causative mechanisms, treatment strategies. or outcomes of iatrogenic brachial plexus injury (iBPI). METHODS: We screened all cases of iatrogenic nerve injuries recorded between 2007 and 2017 at a single specialized institution. Mechanism of iBPI, type of previous causative intervention, location and type of the lesion as well as the type of revision surgery and functional patient outcome were analyzed. RESULTS: We identified 14 cases of iBPI, which all presented with significant impairment of upper extremity motor function (at least 1 muscle Medical Research Council grade 0). Neuropathic pain was present in most patients (11/14). Orthopedic shoulder procedures such as rotator cuff fixation, arthroplasty, and repositioning of a clavicle fracture accounted for iBPI in 7 of 14 patients. Other reasons for iBPI were resection or biopsy of a peripheral nerve sheath tumor in 3 patients or lymph node situated at the cervicomediastinal area in 2 patients. Mechanisms also included transaxillary rib resection in one and sternotomy in another patient. The treatment of iBPI was conducted according to each individual's needs and included neurolysis in 4, nerve grafting in 9, and nerve transfers in 1 patient. We found improved symptoms after treatment in most patients (11/14). CONCLUSIONS: Most common causes for iBPI were shoulder surgery and resection or biopsy of peripheral nerve sheath tumor and lymph nodes. Early referral to specialized peripheral nerve centers may help to improve functional patient outcome.