Joeky T. Senders

Agents for fluorescence-guided glioma surgery: a systematic review of preclinical and clinical results

BackgroundFluorescence-guided surgery (FGS) is a technique used to enhance visualization of tumor margins in order to increase the extent of tumor resection in glioma surgery. In this paper, we systematically review all clinically tested fluorescent agents for application in FGS for glioma and all preclinically tested agents with the potential for FGS for glioma.MethodsWe searched the PubMed and Embase databases for all potentially relevant studies through March 2016. We assessed fluorescent agents by the following outcomes: rate of gross total resection (GTR), overall and progression-free survival, sensitivity and specificity in discriminating tumor and healthy brain tissue, tumor-to-normal ratio of fluorescent signal, and incidence of adverse events.ResultsThe search strategy resulted in 2155 articles that were screened by titles and abstracts. After full-text screening, 105 articles fulfilled the inclusion criteria evaluating the following fluorescent agents: 5-aminolevulinic acid (5-ALA) (44 studies, including three randomized control trials), fluorescein (11), indocyanine green (five), hypericin (two), 5-aminofluorescein-human serum albumin (one), endogenous fluorophores (nine) and fluorescent agents in a pre-clinical testing phase (30). Three meta-analyses were also identified.Conclusions5-ALA is the only fluorescent agent that has been tested in a randomized controlled trial and results in an improvement of GTR and progression-free survival in high-grade gliomas. Observational cohort studies and case series suggest similar outcomes for FGS using fluorescein. Molecular targeting agents (e.g., fluorophore/nanoparticle labeled with anti-EGFR antibodies) are still in the pre-clinical phase, but offer promising results and may be valuable future alternatives.

Increased power of resting-state gamma oscillations in autism spectrum disorder detected by routine electroencephalography

Experimental studies suggest that increased resting-state power of gamma oscillations is associated with autism spectrum disorder (ASD). To extend the clinical applicability of this finding, we retrospectively investigated routine electroencephalography (EEG) recordings of 19 patients with ASD and 19 age- and gender-matched controls. Relative resting-state condition gamma spectral power was variable, but on average significantly increased in children with ASD. This effect remained when excluding electrodes associated with myogenic gamma activity. These findings further indicate that increased resting-state gamma activity characterizes a subset of ASD and may also be detected by routine EEG as a clinically accessible and well-tolerated investigation.

Residual Convolutional Neural Network for Determination of IDH Status in Low- and High-grade Gliomas from MR Imaging

Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data. Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Womens Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming. Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively. Conclusions: We developed a deep learning technique to noninvasively predict IDH genotype in grade II–IV glioma using conventional MR imaging using a multi-institutional data set. Clin Cancer Res; 24(5); 1073–81. ©2017 AACR.

Natural and Artificial Intelligence in Neurosurgery: A Systematic Review

BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. OBJECTIVE To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as “natural intelligence.” METHODS A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. RESULTS Twenty‐three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4‐21%) and 0.14 (interquartile range 0.07‐0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P‐value was provided or calculated, ML models outperformed clinical experts (P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models (P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. CONCLUSION We conclude that ML models have the potential to augment the decision‐making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human‐vs‐machine to a human‐and‐machine paradigm could be essential to overcome these hurdles.

Venous thromboembolism and intracranial hemorrhage after craniotomy for primary malignant brain tumors: a National Surgical Quality Improvement Program analysis

Venous thromboembolism (VTE), including deep venous thrombosis (DVT) and pulmonary embolism (PE), frequently complicates the postoperative course of primary malignant brain tumor patients. Thromboprophylactic anticoagulation is commonly used to prevent VTE at the risk of intracranial hemorrhage (ICH). We extracted all patients who underwent craniotomy for a primary malignant brain tumor from the National Surgical Quality Improvement Program (NSQIP) registry (2005–2015) to perform a time-to-event analysis and identify relevant predictors of DVT, PE, and ICH within 30 days after surgery. Among the 7376 identified patients, the complication rates were 2.6, 1.5, and 1.3% for DVT, PE, and ICH, respectively. VTE was the second-most common major complication and third-most common reason for readmission. ICH was the most common reason for reoperation. The increased risk of VTE extends beyond the period of hospitalization, especially for PE, whereas ICH occurred predominantly within the first days after surgery. Older age and higher BMI were overall predictors of VTE. Dependent functional status and longer operative times were predictive for VTE during hospitalization, but not for post-discharge events. Admission two or more days before surgery was predictive for DVT, but not for PE. Preoperative steroid usage and male gender were predictive for post-discharge DVT and PE, respectively. ICH was associated with various comorbidities and longer operative times. This multicenter study demonstrates distinct critical time periods for the development of thrombotic and hemorrhagic events after craniotomy. Furthermore, the VTE risk profile depends on the type of VTE (DVT vs. PE) and clinical setting (hospitalized vs. post-discharge patients).

Innovation in neurosurgery: less than IDEAL? A systematic review

BackgroundSurgical innovation is different from the introduction of novel pharmaceuticals. To help address this, in 2009 the IDEAL Collaboration (Idea, Development, Exploration, Assessment, Long-term follow-up) introduced the five-stage framework for surgical innovation. To evaluate the framework feasibility for novel neurosurgical procedure introduction, two innovative surgical procedures were examined: the endoscopic endonasal approach for skull base meningiomas (EEMS) and the WovenEndobridge (WEB device) for endovascular treatment of intracranial aneurysms.MethodsThe published literature on EEMS and WEB devices was systematically reviewed. Identified studies were classified according to the IDEAL framework stage. Next, studies were evaluated for possible categorization according to the IDEAL framework.ResultsFive hundred seventy-six papers describing EEMS were identified of which 26 papers were included. No prospective studies were identified, and no studies reported on ethical approval or patient informed consent for the innovative procedure. Therefore, no clinical studies could be categorized according to the IDEAL Framework. For WEB devices, 6229 articles were screened of which 21 were included. In contrast to EEMS, two studies were categorized as 2a and two as 2b.ConclusionThe results of this systematic review demonstrate that both EEMS and WEB devices were not introduced according to the (later developed in the case of EEMS) IDEAL framework. Elements of the framework such as informed consent, ethical approval, and rigorous outcomes reporting are important and could serve to improve the quality of neurosurgical research. Alternative study designs and the use of big data could be useful modifications of the IDEAL framework for innovation in neurosurgery.

The Woven Endobridge Device for Treatment of Intracranial Aneurysms: A Systematic Review

INTRODUCTION The Woven Endobridge (WEB) device is an innovative endovascular device for treatment of intracranial aneurysms, especially bifurcation and wide-neck aneurysms. Although not approved by the U.S. Food and Drug Administration, it has been available in Europe since 2011. The aim of this review is to evaluate the outcomes of WEB device use for intracranial aneurysm treatment. METHODS A systematic review was conducted with MEDLINE search engines PubMed and Embase from 2011. The search strategy provided 6229 articles, and 19 articles were included. RESULTS A total of 19 papers were identified describing the use of WEB devices in 687 patients with 718 aneurysms. The 2 largest prospective multicenter studies (WEBCAST and the French Observatory Trial) reported successful treatment, defined as complete closure or a neck remnant, in 85% and 79% of aneurysms, respectively. The use of a WEB device in combination with coiling or stenting was described with varying results in multiple small series. Outcomes of WEB device use in ruptured aneurysms in 2 studies showed 94% and 80% adequate treatment. Thromboembolic events were described in 71 patients (10.3% of all patients) and infarctions in 8 patients (1.2% of all patients). CONCLUSIONS Despite initial promising results, the WEB device should be used with caution given its potentially large learning curve and because it has primarily been investigated only in wide-neck and bifurcation aneurysms. In addition, currently available prospective studies have short follow-up, and the device has not been directly compared with other treatment modalities.

An introduction and overview of machine learning in neurosurgical care

BackgroundMachine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML’s potential to assist and improve neurosurgical care.MethodA systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment.ResultsOf the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson’s disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.ConclusionsML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.

A nationwide analysis of 30‐day adverse events, unplanned readmission, and length of hospital stay after peripheral nerve surgery in extremities and the brachial plexus

Little is known on adverse events and their timing after peripheral nerve surgery in extremities. The aim of this study is to identify predictors and typical timing of complications, unplanned readmission, and length of hospital stay for patients undergoing peripheral nerve surgery in the extremities.

Behavior and attitudes among European neurosurgeons – An international survey

BACKGROUND A recent survey showed that potentially hazardous levels of certain attitudes have been associated with worse patient outcomes in orthopedic surgery, based on a questionnaire that was adopted from aviation. This questionnaire aims to evaluate the prevalence of potentially hazardous levels of machismo, impulsiveness, anxiety, antiauthority, resignation, and invulnerability in attitudes and was adopted for use among neurosurgeons. METHODS All individual members of the European Association of Neurosurgical Societies (EANS) were invited to fill-out an online questionnaire. Questions were on a five-point Likert-scale ranging from strongly disagree to strongly agree with five questions per attitude and answers were collected together with neurosurgeon and practice characteristics. Participants could score five points for each question after which an overall score was calculated for each attitude. Like the orthopedic survey, a potentially hazardous level of any behavior was defined as a score >20. RESULTS Resignation (n = 21; 7.7%) and anxiety (n = 10; 3.7%) had the highest prevalence of potentially hazardous levels among neurosurgeons. Few neurosurgeons showed potentially hazardous levels of antiauthority (n = 4; 1.5%), self-confidence (n = 2; 0.7%), or impulsive attitudes (n = 1; 0.4%). None of the participants showed potentially hazardous levels of machismo. Overall, 12.2% of neurosurgeons had a potentially hazardous score for at least one of the evaluated attitudes. CONCLUSION Findings of this study indicate a low prevalence of potentially hazardous levels of certain attitudes among neurosurgeons based on a questionnaire tailored to neurosurgery. However, the implications of this study are limited by various factors and warrant further evaluation and warrant further evaluation.