Methodological and ethical challenges in the use of focused ultrasound for blood-brain barrier disruption in neuro-oncology.

BACKGROUND: Focused ultrasound (FUS) shows promise for enhancing drug delivery to the brain by temporarily opening the blood-brain barrier (BBB), and it is increasingly used in the clinical setting to treat brain tumours. It remains however unclear whether FUS is being introduced in an ethically and methodologically sound manner. The IDEAL-D framework for the introduction of surgical innovations and the SYRCLE and ROBINS-I tools for assessing the risk of bias in animal studies and non-randomized trials, respectively, provide a comprehensive evaluation for this. OBJECTIVES AND METHODS: A comprehensive literature review on FUS in neuro-oncology was conducted. Subsequently, the included studies were evaluated using the IDEAL-D framework, SYRCLE, and ROBINS-I tools. RESULTS: In total, 19 published studies and 12 registered trials were identified. FUS demonstrated successful BBB disruption, increased drug delivery, and improved survival rates. However, the SYRCLE analysis revealed a high risk of bias in animal studies, while the ROBINS-I analysis found that most human studies had a high risk of bias due to a lack of blinding and heterogeneous samples. Of the 15 pre-clinical stage 0 studies, only six had formal ethical approval, and only five followed animal care policies. Both stage 1 studies and stage 1/2a studies failed to provide information on patient data confidentiality. Overall, no animal or human study reached the IDEAL-D stage endpoint. CONCLUSION: FUS holds promise for enhancing drug delivery to the brain, but its development and implementation must adhere to rigorous safety standards using the established ethical and methodological frameworks. The complementary use of IDEAL-D, SYRCLE, and ROBINS-I tools indicates a high risk of bias and ethical limitations in both animal and human studies, highlighting the need for further improvements in study design for a safe implementation of FUS in neuro-oncology.

Predicting leptomeningeal disease spread after resection of brain metastases using machine learning.

OBJECTIVE: The incidence of leptomeningeal disease (LMD) has increased as treatments for brain metastases (BMs) have improved and patients with metastatic disease are living longer. Sample sizes of individual studies investigating LMD after surgery for BMs and its risk factors have been limited, ranging from 200 to 400 patients at risk for LMD, which only allows the use of conventional biostatistics. Here, the authors used machine learning techniques to enhance LMD prediction in a cohort of surgically treated BMs. METHODS: A conditional survival forest, a Cox proportional hazards model, an extreme gradient boosting (XGBoost) classifier, an extra trees classifier, and logistic regression were trained. A synthetic minority oversampling technique (SMOTE) was used to train the models and handle the inherent class imbalance. Patients were divided into an 80:20 training and test set. Fivefold cross-validation was used on the training set for hyperparameter optimization. Patients eligible for study inclusion were adults who had consecutively undergone neurosurgical BM treatment, had been admitted to Brigham and Women's Hospital from January 2007 through December 2019, and had a minimum of 1 month of follow-up after neurosurgical treatment. RESULTS: A total of 1054 surgically treated BM patients were included in this analysis. LMD occurred in 168 patients (15.9%) at a median of 7.05 months after BM diagnosis. The discrimination of LMD occurrence was optimal using an XGboost algorithm (area under the curve = 0.83), and the time to LMD was prognosticated evenly by the random forest algorithm and the Cox proportional hazards model (C-index = 0.76). The most important feature for both LMD classification and regression was the BM proximity to the CSF space, followed by a cerebellar BM location. Lymph node metastasis of the primary tumor at BM diagnosis and a cerebellar BM location were the strongest risk factors for both LMD occurrence and time to LMD. CONCLUSIONS: The outcomes of LMD patients in the BM population are predictable using SMOTE and machine learning. Lymph node metastasis of the primary tumor at BM diagnosis and a cerebellar BM location were the strongest LMD risk factors.

Risk Factors of Second Local Recurrence in Surgically Treated Recurrent Brain Metastases: An Exploratory Analysis.

BACKGROUND: A first local recurrence is common after resection or radiotherapy for brain metastasis (BM). However, patients with BMs can develop multiple local recurrences over time. Published data on second local recurrences are scarce. This study aimed to report predictors associated with a second local recurrence in patients with BMs who underwent a craniotomy for a first locally recurrent BM. METHODS: Patients were identified from a database at Brigham and Women's Hospital in Boston. Hazard ratios and 95% confidence intervals for predictors of a second local recurrence were computed using a Cox proportional hazards model. RESULTS: Of 170 identified surgically treated first locally recurrent lesions, 74 (43.5%) progressed to second locally recurrent lesions at a median of 7 months after craniotomy. Subtotal resection of the first local BM recurrence was significantly associated with shorter time to second local recurrence (hazard ratio = 2.09; 95% confidence interval, 1.27-3.45). Infratentorial location was associated with a worse second local recurrence prognosis (hazard ratio = 2.22; 95% confidence interval, 1.24-3.96). CONCLUSIONS: A second local recurrence occurred after 43.5% of craniotomies for first recurrent lesions. Subtotal resection and infratentorial location were the strongest risk factors for worse second local recurrence prognosis following resection of first recurrent BM.

Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World.

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.

Leptomeningeal disease in neurosurgical brain metastases patients: A systematic review and meta-analysis.

BACKGROUND: Leptomeningeal disease (LMD) is a complication distinguished by progression of metastatic disease into the leptomeninges and subsequent spread via cerebrospinal fluid (CSF). Although treatments for LMD exist, it is considered fatal with a median survival of 2-4 months. A broader overview of the risk factors that increase the brain metastasis (BM) patient's risk of LMD is needed. This meta-analysis aimed to systematically review and quantitatively assess risk factors for LMD after surgical resection for BM. METHODS: A systematic literature search was performed on 7 May 2021. Pooled effect sizes were calculated using a random-effects model for variables reported by three or more studies. RESULTS: Among 503 studies, thirteen studies met the inclusion criteria with a total surgical sample size of 2105 patients, of which 386 patients developed LMD. The median incidence of LMD across included studies was 16.1%. Eighteen unique risk factors were reported as significantly associated with LMD occurrence, including but not limited to: larger tumor size, infratentorial BM location, proximity of BM to cerebrospinal fluid spaces, ventricle violation during surgery, subtotal or piecemeal resection, and postoperative stereotactic radiosurgery. Pooled results demonstrated that breast cancer as the primary tumor location (HR = 2.73, 95% CI: 2.12-3.52) and multiple BMs (HR = 1.37, 95% CI: 1.18-1.58) were significantly associated with a higher risk of LMD occurrence. CONCLUSION: Breast cancer origin and multiple BMs increase the risk of LMD occurrence after neurosurgery. Several other risk factors which might play a role in LMD development were also identified.

Beta-blockers and glioma: a systematic review of preclinical studies and clinical results.

Given the median survival of 15 months after diagnosis, novel treatment strategies are needed for glioblastoma. Beta-blockers have been demonstrated to inhibit angiogenesis and tumor cell proliferation in various cancer types. The aim of this study was to systematically review the evidence on the effect of beta-blockers on glioma growth. A systematic literature search was performed in the PubMed, Embase, Google Scholar, Web of Science, and Cochrane Central to identify all relevant studies. Preclinical studies concerning the pharmacodynamic effects of beta-blockers on glioma growth and proliferation were included, as well as clinical studies that studied the effect of beta-blockers on patient outcomes according to PRISMA guidelines. Among the 980 citations, 10 preclinical studies and 1 clinical study were included after title/abstract and full-text screening. The following potential mechanisms were identified: reduction of glioma cell proliferation (n = 9), decrease of glioma cell migration (n = 2), increase of drug sensitivity (n = 1), induction of glioma cell death (n = 1). Beta-blockers affect glioma proliferation by inducing a brief reduction of cAMP and a temporary cell cycle arrest in vitro. Contrasting results were observed concerning glioma cell migration. The identified clinical study did not find an association between beta-blockers and survival in glioma patients. Although preclinical studies provide scarce evidence for the use of beta-blockers in glioma, they identified potential pathways for targeting glioma. Future studies are needed to clarify the effect of beta-blockers on clinical endpoints including survival outcomes in glioma patients to scrutinize the value of beta-blockers in glioma care.

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.

The ethical and legal status of neurosurgical guidelines: the neurosurgeon's golden fleece or Achilles' heel?

Neurosurgical guidelines are fundamental for evidence-based practice and have considerably increased both in number and content over the last decades. Yet, guidelines in neurosurgery are not without limitations, as they are overwhelmingly based on low-level evidence. Such recommendations have in the past been occasionally overturned by well-designed randomized controlled trials (RCTs), demonstrating the volatility of poorly underpinned evidence. Furthermore, even RCTs in surgery come with several limitations; most notably, interventions are often insufficiently standardized and assume a homogeneous patient population, which is not always applicable to neurosurgery. Lastly, guidelines are often outdated by the time they are published and smaller fields such as neurosurgery may lack a sufficient workforce to provide regular updates. These limitations raise the question of whether it is ethical to use low-level evidence for guideline recommendations, and if so, how strictly guidelines should be adhered to from an ethical and legal perspective. This article aims to offer a critical approach to the ethical and legal status of guidelines in neurosurgery. To this aim, the authors discuss: 1) the current state of neurosurgical guidelines and the evidence they are based on; 2) the degree of implementation of these guidelines; 3) the legal status of guidelines in medical disciplinary cases; and 4) the ethical balance between confident and critical use of guidelines. Ultimately, guidelines are neither laws that should always be followed nor purely academic efforts with little practical use. Every patient is unique, and tailored treatment defined by the surgeon will ensure optimal care; guidelines play an important role in creating a solid base that can be adhered to or deviated from, depending on the situation. From a research perspective, it is inevitable to rely on weaker evidence initially in order to generate more robust evidence later, and clinician-researchers have an ethical duty to contribute to generating and improving neurosurgical guidelines.