Artificial intelligence in cancer imaging: Clinical challenges and applications.

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

Elevated risk of recurrence and retreatment for silent pituitary adenomas.

PURPOSE: Pituitary adenomas are the most common tumor of the pituitary gland and comprise nearly 15% of all intracranial masses. These tumors are stratified into functional or silent categories based on their pattern of hormone expression and secretion. Preliminary evidence supports differential clinical outcomes between some functional pituitary adenoma (FPA) subtypes and silent pituitary adenoma (SPA) subtypes. METHODS: We collected and analyzed the medical records of all patients undergoing resection of SPAs or FPAs from a single high-volume neurosurgeon between 2007 and 2018 at Brigham and Women's Hospital. Descriptive statistics and the Mantel-Cox log-rank test were used to identify differences in outcomes between these cohorts, and multivariate logistic regression was used to identify predictors of radiographic recurrence for SPAs. RESULTS: Our cohort included 88 SPAs and 200 FPAs. The majority of patients in both cohorts were female (48.9% of SPAs and 63.5% of FPAs). SPAs were larger in median diameter than FPAs (2.1 cm vs. 1.2 cm, p < 0.001). The most frequent subtypes of SPA were gonadotrophs (55.7%) and corticotrophs (30.7%). Gross total resection (GTR) was achieved in 70.1% of SPA resections and 86.0% of FPA resections (p < 0.001). SPAs had a higher likelihood of recurring (hazard ratio [HR] 3.2, 95% confidence interval [95%CI] 1.6-7.2) and a higher likelihood of requiring retreatment for recurrence (HR 2.5; 95%CI 1.0-6.1). Subset analyses revealed that recurrence and retreatment were more both likely for subtotally resected SPAs than subtotally resected FPAs, but this pattern was not observed in SPAs and FPAs after GTR. Among SPAs, recurrence was associated with STR (odds ratio [OR] 9.3; 95%CI 1.4-64.0) and younger age (OR 0.92 per year; 95%CI 0.88-0.98) in multivariable analysis. Of SPAs that recurred, 12 of 19 (63.2%) were retreated with repeat surgery (n = 11) or radiosurgery (n = 1), while the remainder were observed (n = 7).There were similar rates of recurrence across different SPA subtypes. CONCLUSION: Patients undergoing resection of SPAs should be closely monitored for disease recurrence through more frequent clinical follow-up and diagnostic imaging than other adenomas, particularly among patients with STR and younger patients. Several patients can be observed after radiographic recurrence, and the decision to retreat should be individualized. Longitudinal clinical follow-up of SPAs, including an assessment of symptoms, endocrine function, and imaging remains critical.

Recurrent Glioblastoma-Molecular Underpinnings and Evolving Treatment Paradigms.

Glioblastoma is the most common and lethal central nervous system malignancy with a median survival after progression of only 6-9 months. Major biochemical mechanisms implicated in glioblastoma recurrence include aberrant molecular pathways, a recurrence-inducing tumor microenvironment, and epigenetic modifications. Contemporary standard-of-care (surgery, radiation, chemotherapy, and tumor treating fields) helps to control the primary tumor but rarely prevents relapse. Cytoreductive treatment such as surgery has shown benefits in recurrent glioblastoma; however, its use remains controversial. Several innovative treatments are emerging for recurrent glioblastoma, including checkpoint inhibitors, chimeric antigen receptor T cell therapy, oncolytic virotherapy, nanoparticle delivery, laser interstitial thermal therapy, and photodynamic therapy. This review seeks to provide readers with an overview of (1) recent discoveries in the molecular basis of recurrence; (2) the role of surgery in treating recurrence; and (3) novel treatment paradigms emerging for recurrent glioblastoma.

Metastatic pituitary tumors: an institutional case series.

PURPOSE: Pituitary carcinomas are a rare entity that respond poorly to multimodal therapy. Patients follow a variable disease course that remains ill-defined. METHODS: We present an institutional case series of patients treated for pituitary carcinomas over a 30-year period from 1992 to 2022. A systematic review was conducted to identify prior case series of patients with pituitary carcinomas. RESULTS: Fourteen patients with a mean age at pituitary carcinoma diagnosis of 52.5 years (standard deviation [SD] 19.4) met inclusion criteria. All 14 patients had tumor subtypes confirmed by immunohistochemistry and hormone testing, with the most common being ACTH-producing pituitary adenomas (n = 12). Patients had a median progression-free survival (PFS) of 1.4 years (range 0.7-10.0) and a median overall survival (OS) of 8.4 years (range 2.3-24.0) from pituitary adenoma diagnosis. Median PFS and OS were 0.6 years (range 0.0-2.2) and 1.5 years (range 0.1-9.6) respectively upon development of metastases. Most patients (n = 12) had locally invasive disease to the cavernous sinus, dorsum sellae dura, or sphenoid sinus prior to metastasis. Common sites of metastasis included the central nervous system, liver, lung, and bone. In a pooled analysis including additional cases from the literature, treatment of metastases with chemotherapy or a combination of radiation therapy and chemotherapy significantly prolonged PFS (p = 0.02), while failing to significantly improve OS (p = 0.14). CONCLUSION: Pituitary carcinomas are highly recurrent, heterogenous tumors with variable responses to treatment. Multidisciplinary management with an experienced neuro-endocrine and neuro-oncology team is needed given the unrelenting nature of this disease.

Critically reading machine learning literature in neurosurgery: a reader's guide and checklist for appraising prediction models.

OBJECTIVE: Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work. METHODS: The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment. RESULTS: The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature. CONCLUSIONS: ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.

Volume replacement is associated with a diminished osmolar effect of mannitol in patients with acute brain injury.

INTRODUCTION: Animal experiments recently demonstrated that replacing urinary loses with crystalloid diminishes the therapeutic effect of mannitol by reducing the increase in osmolality. We aimed to investigate whether this effect is similarly seen in in brain-injured patients by studying the association between total body fluid balance (TBB) and the osmolar response to mannitol. METHODS: We performed a retrospective cohort study of adult patients with acute brain injury between 2015 and 2021 who received ≥ 2 doses of mannitol within 8 hours and no intercurrent concentrated saline solution. We analyzed the association between the change in TBB (∆TBB) and change in osmolality (∆Osm) before and after mannitol in a linear model, both as univariate and after adjustment for common confounding factors. RESULTS: Of 6,145 patients who received mannitol, 155 patients met inclusion criteria (mean age 60 ± 17 years, 48% male, 83% white). The mean total mannitol dose was 2 ± 0.5 g/kg and the mean change in plasma osmolality was 7.9 ± 7.1 mOsm/kg. Each 1 L increase in ∆TBB was associated with a change of -1.1 mOsm/L in ∆Osm (95% CI [-2.2, -0.02], p = 0.045). The magnitude of association was similar to that of total mannitol dose and remained consistent in an adjusted model and after excluding outliers. CONCLUSIONS: In patients with acute brain injury, a positive TBB is associated with a diminished mannitol-induced increase in plasma osmolality. Future prospective studies are needed to confirm these findings and their influence on the therapeutic effect of mannitol.

Tumor-Associated Macrophages/Microglia in Glioblastoma Oncolytic Virotherapy: A Double-Edged Sword.

Oncolytic virotherapy is a rapidly progressing field that uses oncolytic viruses (OVs) to selectively infect malignant cells and cause an antitumor response through direct oncolysis and stimulation of the immune system. Despite demonstrated pre-clinical efficacy of OVs in many cancer types and some favorable clinical results in glioblastoma (GBM) trials, durable increases in overall survival have remained elusive. Recent evidence has emerged that tumor-associated macrophage/microglia (TAM) involvement is likely an important factor contributing to OV treatment failure. It is prudent to note that the relationship between TAMs and OV therapy failures is complex. Canonically activated TAMs (i.e., M1) drive an antitumor response while also inhibiting OV replication and spread. Meanwhile, M2 activated TAMs facilitate an immunosuppressive microenvironment thereby indirectly promoting tumor growth. In this focused review, we discuss the complicated interplay between TAMs and OV therapies in GBM. We review past studies that aimed to maximize effectiveness through immune system modulation-both immunostimulatory and immunosuppressant-and suggest future directions to maximize OV efficacy.

Deep Learning for Adjacent Segment Disease at Preoperative MRI for Cervical Radiculopathy.

Background Patients who undergo surgery for cervical radiculopathy are at risk for developing adjacent segment disease (ASD). Identifying patients who will develop ASD remains challenging for clinicians. Purpose To develop and validate a deep learning algorithm capable of predicting ASD by using only preoperative cervical MRI in patients undergoing single-level anterior cervical diskectomy and fusion (ACDF). Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, retrospective chart review was performed for 1244 patients undergoing single-level ACDF in two tertiary care centers. After application of inclusion and exclusion criteria, 344 patients were included, of whom 60% (n = 208) were used for training and 40% for validation (n = 43) and testing (n = 93). A deep learning-based prediction model with 48 convolutional layers was designed and trained by using preoperative T2-sagittal cervical MRI. To validate model performance, a neuroradiologist and neurosurgeon independently provided ASD predictions for the test set. Validation metrics included accuracy, areas under the curve, and F1 scores. The difference in proportion of wrongful predictions between the model and clinician was statistically tested by using the McNemar test. Results A total of 344 patients (median age, 48 years; interquartile range, 41-58 years; 182 women) were evaluated. The model predicted ASD on the 93 test images with an accuracy of 88 of 93 (95%; 95% CI: 90, 99), sensitivity of 12 of 15 (80%; 95% CI: 60, 100), and specificity of 76 of 78 (97%; 95% CI: 94, 100). The neuroradiologist and neurosurgeon provided predictions with lower accuracy (54 of 93; 58%; 95% CI: 48, 68), sensitivity (nine of 15; 60%; 95% CI: 35, 85), and specificity (45 of 78; 58%; 95% CI: 56, 77) compared with the algorithm. The McNemar test on the contingency table demonstrated that the proportion of wrongful predictions was significantly lower by the model (test statistic, 2.000; P < .001). Conclusion A deep learning algorithm that used only preoperative cervical T2-weighted MRI outperformed clinical experts at predicting adjacent segment disease in patients undergoing surgery for cervical radiculopathy. © RSNA, 2021 An earlier incorrect version appeared online. This article was corrected on September 22, 2021.

Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.

Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58-0.98), accuracy (0.69-0.98), and C-index (0.66-0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.

Fibrinolytics and Intraventricular Hemorrhage: A Systematic Review and Meta-analysis.

Intraventricular hemorrhage (IVH) is an independent poor prognostic factor in subarachnoid and intra-parenchymal hemorrhage. The use of intraventricular fibrinolytics (IVF) has long been debated, and its exact effects on outcomes are unknown. A systematic review and meta-analysis were performed in accordance with the PRISMA guidelines to assess the impact of IVF after non-traumatic IVH on mortality, functional outcome, intracranial bleeding, ventriculitis, time until clearance of third and fourth ventricles, obstruction of external ventricular drains (EVD), and shunt dependency. Nineteen studies were included in the meta-analysis, totaling 1020 patients. IVF was associated with lower mortality (relative risk [RR] 0.58; 95% confidence interval [CI] 0.47-0.72), fewer EVD obstructions (RR 0.41; 95% CI 0.22-0.74), and a shorter time until clearance of the ventricles (median difference [MD] - 4.05 days; 95% CI - 5.52 to - 2.57). There was no difference in good functional outcome, RR 1.41 (95% CI 0.98-2.03), or shunt dependency, RR 0.93 (95% CI 0.70-1.22). Correction for publication bias predicted an increased risk of intracranial bleeding, RR 1.67 (95% CI 1.01-2.74) and a lower risk of ventriculitis, RR 0.68 (95% CI 0.45-1.03) in IVH patients treated with IVF. IVF was associated with improved survival, faster clearance of blood from the ventricles and fewer drain obstructions, but further research is warranted to elucidate the effects on ventriculitis, long-term functional outcomes, and re-hemorrhage.

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas.

PURPOSE: Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions. METHODS: Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive. RESULTS: Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198). CONCLUSION: Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.

An introduction and overview of machine learning in neurosurgical care.

BACKGROUND: Machine 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. METHOD: A 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. RESULTS: Of 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. CONCLUSIONS: ML 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.

Combined petrosal approach for petroclival meningioma.

The authors present the case of a 56-year-old patient who presented with a large petroclival meningioma and clinical symptoms of brainstem compression and hydrocephalus. The authors describe a two-stage combined petrosal approach for complete resection of the tumor. The nuances of the approach and microsurgical technique are discussed, as well as the radiographic and clinical outcomes. The video can be found here: https://youtu.be/JxoTXS-uiHM .

Ultrasound-guided percutaneous injection of methylene blue to identify nerve pathology and guide surgery.

OBJECT The objective of this study was to provide a technique that could be used in the preoperative period to facilitate the surgical exploration of peripheral nerve pathology. METHODS The authors describe a technique in which 1) ultrasonography is used in the immediate preoperative period to identify target peripheral nerves, 2) an ultrasound-guided needle electrode is used to stimulate peripheral nerves to confirm their position, and then 3) a methylene blue (MB) injection is performed to mark the peripheral nerve pathology to facilitate surgical exploration. RESULTS A cohort of 13 patients with varying indications for peripheral nerve surgery is presented in which ultrasound guidance, stimulation, and MB were used to localize and create a road map for surgeries. CONCLUSIONS Preoperative ultrasound-guided MB administration is a promising technique that peripheral nerve surgeons could use to plan and execute surgery.

Evaluation of the safety, accuracy, and helpfulness of the GPT-4.0 Large Language Model in neurosurgery.

BACKGROUND: Although prior work demonstrated the surprising accuracy of Large Language Models (LLMs) on neurosurgery board-style questions, their use in day-to-day clinical situations warrants further investigation. This study assessed GPT-4.0's responses to common clinical questions across various subspecialties of neurosurgery. METHODS: A panel of attending neurosurgeons formulated 35 general neurosurgical questions spanning neuro-oncology, spine, vascular, functional, pediatrics, and trauma. All questions were input into GPT-4.0 with a prespecified, standard prompt. Responses were evaluated by two attending neurosurgeons, each on a standardized scale for accuracy, safety, and helpfulness. Citations were indexed and evaluated against identifiable database references. RESULTS: GPT-4.0 responses were consistent with current medical guidelines and accounted for recent advances in the field 92.8 % and 78.6 % of the time respectively. Neurosurgeons reported GPT-4.0 responses providing unrealistic information or potentially risky information 14.3 % and 7.1 % of the time respectively. Assessed on 5-point scales, responses suggested that GPT-4.0 was clinically useful (4.0 ± 0.6), relevant (4.7 ± 0.3), and coherent (4.9 ± 0.2). The depth of clinical responses varied (3.7 ± 0.6), and "red flag" symptoms were missed 7.1 % of the time. Moreover, GPT-4.0 cited 86 references (2.46 citations per answer), of which only 50 % were deemed valid, and 77.1 % of responses contained at least one inappropriate citation. CONCLUSION: Current general LLM technology can offer generally accurate, safe, and helpful neurosurgical information, but may not fully evaluate medical literature or recent field advances. Citation generation and usage remains unreliable. As this technology becomes more ubiquitous, clinicians will need to exercise caution when dealing with it in practice.

Emergent Coronary Thrombectomy for Acute Myocardial Infarction Immediately Following Craniotomy with Tumor Resection.

The management of perioperative acute myocardial infarction (AMI) following oncologic neurosurgery requires balancing competing risks of myocardial ischemia and postoperative bleeding. There are limited human data to establish the safest timing of antiplatelet or anticoagulation therapy following neurosurgical procedures. For patients with malignancy experiencing AMI in the acute postoperative period, staged percutaneous coronary intervention (PCI) with upfront coronary aspiration thrombectomy followed by delayed completion PCI may offer an opportunity for myocardial salvage while minimizing postoperative bleeding risks. CYP2C19 genotyping and platelet aggregation studies can help confirm adequate platelet inhibition once antiplatelet therapy is resumed.

Automated ventricular segmentation and shunt failure detection using convolutional neural networks.

While ventricular shunts are the main treatment for adult hydrocephalus, shunt malfunction remains a common problem that can be challenging to diagnose. Computer vision-derived algorithms present a potential solution. We designed a feasibility study to see if such an algorithm could automatically predict ventriculomegaly indicative of shunt failure in a real-life adult hydrocephalus population. We retrospectively identified a consecutive series of adult shunted hydrocephalus patients over an eight-year period. Associated computed tomography scans were extracted and each scan was reviewed by two investigators. A machine learning algorithm was trained to identify the lateral and third ventricles, and then applied to test scans. Results were compared to human performance using Sørensen-Dice coefficients, calculated total ventricular volumes, and ventriculomegaly as documented in the electronic medical record. 5610 axial images from 191 patients were included for final analysis, with 52 segments (13.6% of total data) reserved for testing. Algorithmic performance on the test group averaged a Dice score of 0.809 ± 0.094. Calculated total ventricular volumes did not differ significantly between computer-derived volumes and volumes marked by either the first reviewer or second reviewer (p > 0.05). Algorithm detection of ventriculomegaly was correct in all test cases and this correlated with correct prediction of need for shunt revision in 92.3% of test cases. Though development challenges remain, it is feasible to create automated algorithms that detect ventriculomegaly in adult hydrocephalus shunt malfunction with high reliability and accuracy.

From Operating Room to Courtroom: Analyzing Malpractice Trajectories in Cranial Neurosurgery.

BACKGROUND AND OBJECTIVES: Nearly all neurosurgeons in the United States will be named defendants in a malpractice claim before retirement. We perform an assessment of national malpractice trends in cranial neurosurgery to inform neurosurgeons on current outcomes, trends over time, benchmarks for malpractice coverage needs, and ways to mitigate lawsuits. METHODS: The Westlaw Edge and LexisNexis databases were searched to identify medical malpractice cases relating to open cranial surgery between 1987 and 2023. Extracted data included date of verdict, jurisdiction, outcome, details of sustained injuries, and any associated award/settlement figures. RESULTS: Of 1550 cases analyzed, 252 were identified as malpractice claims arising from open cranial surgery. The median settlement amount was $950 000 and the average plaintiff ruling was $2 750 000. The highest plaintiff ruling resulted in an award of $28.1 million. Linear regression revealed no significant relationship between year and defendant win (P-value = .43). After adjusting for inflation, award value increased with time (P-value = .01). The most common cranial subspecialties were tumor (67 cases, 26.6%), vascular (54 cases, 21.4%), infection (23 cases, 9.1%), and trauma (23 cases, 9.1%). Perioperative complications was the most common litigation category (96 cases, 38.1%), followed by delayed treatment (40 cases, 15.9%), failure to diagnose (38 cases, 15.1%), and incorrect choice of procedure (29 cases, 11.5%). The states with most claims were New York (40 cases, 15.9%), California (24 cases, 9.5%), Florida (21 cases, 8.3%), and Pennsylvania (20 cases, 7.9%). CONCLUSION: Although a stable number of cases were won by neurosurgeons, an increase in award sizes was observed in the 37-year period assessed. Perioperative complications and delayed treatment/diagnosis were key drivers of malpractice claims.

Pilot trial of perampanel on peritumoral hyperexcitability and clinical outcomes in newly diagnosed high-grade glioma.

Background: Glutamatergic neuron-glioma synaptogenesis and peritumoral hyperexcitability promote glioma growth in a positive feedback loop. The objective of this study was to evaluate the feasibility and estimated effect sizes of the AMPA-R antagonist, perampanel, on intraoperative electrophysiologic hyperexcitability and clinical outcomes. Methods: An open-label trial was performed comparing perampanel to standard of care (SOC) in patients undergoing resection of newly-diagnosed radiologic high-grade glioma. Perampanel was administered as a pre-operative loading dose followed by maintenance therapy until progressive disease or up to 12-months. SOC treatment involved levetiracetam for 7-days or as clinically indicated. The primary outcome of hyperexcitability was defined by intra-operative electrocorticography high frequency oscillation (HFO) rates. Seizure-freedom and overall survival (OS) were estimated by the Kaplan-Meier method. Tissue concentrations of perampanel, levetiracetam, and metabolites were measured by mass spectrometry. Results: HFO rates were similar between perampanel-treated and SOC cohorts. The trial was terminated early after interim analysis for futility, and outcomes assessed in 11 patients (7 perampanel-treated, 4 SOC). Over a median 281 days of post-enrollment follow-up, 27% of patients had seizures, including 14% treated with perampanel and 50% treated with SOC. OS in perampanel-treated patients was similar to a glioblastoma reference cohort (p=0.81). Glutamate concentrations in surface biopsies were positively correlated with HFO rates in adjacent electrode contacts and were not significantly associated with treatment assignment or drug concentrations. Conclusions: A peri-operative loading regimen of perampanel was safe and well-tolerated, with similar peritumoral hyperexcitability as in levetiracetam-treated patients. Maintenance anti-glutamatergic therapy was not observed to impact survival outcomes.