The effect of smoking on survival in lung carcinoma patients with brain metastasis: a systematic review and meta-analysis.

The effects of smoking on survival in BM patients have yet to be reviewed and meta-analysed. However, previous studies have shown that smokers had a greater risk of dying from lung cancer compared to non-smokers. This meta-analysis, therefore, aimed to analyse the effects of cigarette smoking on overall survival (OS) and progression-free survival (PFS) in lung cancer BM patients. PubMed, Embase, Web of Science, Cochrane and Google Scholar were searched for comparative studies regarding the effects of smoking on incidence and survival in brain metastases patients up to December 2020. Three independent reviewers extracted overall survival (OS) and progression-free survival data (PFS). Random-effects models were used to pool multivariate-adjusted hazard ratios (HR). Out of 1890 studies, fifteen studies with a total of 2915 patients met our inclusion criteria. Amongst lung carcinoma BM patients, those who were smokers (ever or yes) had a worse overall survival (HR: 1.34, 95% CI 1.13, 1.60, I2: 72.1%, p-heterogeneity < 0.001) than those who were non-smokers (never or no). A subgroup analysis showed the association to remain significant in the ever/never subgroup (HR: 1.34, 95% CI 1.11, 1.63) but not in the yes/no smoking subgroup (HR: 1.30, 95% CI 0.44, 3.88). This difference between the two subgroups was not statistically significant (p = 0.91). Amongst lung carcinoma BM patients, smoking was associated with a worse OS and PFS. Future studies examining BMs should report survival data stratified by uniform smoking status definitions.

Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.

BACKGROUND: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional approaches that rely on hand-crafted features and deep learning (DL) techniques that utilize automatic feature extraction. The aim of this study was to evaluate the performance of published traditional ML versus DL algorithms in classification, grading, outcome prediction, and segmentation of meningiomas. METHODS: A systematic review and meta-analysis were conducted. Major databases were searched through September 2021 for publications evaluating traditional ML versus DL models on meningioma management. Performance measures including pooled sensitivity, specificity, F1-score, area under the receiver-operating characteristic curve, positive and negative likelihood ratios (LR+, LR-) along with their respective 95% confidence intervals (95% CIs) were derived using random-effects models. RESULTS: Five hundred thirty-four records were screened, and 43 articles were included, regarding classification (3 articles), grading (29), outcome prediction (7), and segmentation (6) of meningiomas. Of the 29 studies that reported on grading, 10 could be meta-analyzed with 2 DL models (sensitivity 0.89, 95% CI: 0.74-0.96; specificity 0.91, 95% CI: 0.45-0.99; LR+ 10.1, 95% CI: 1.33-137; LR- 0.12, 95% CI: 0.04-0.59) and 8 traditional ML (sensitivity 0.74, 95% CI: 0.62-0.83; specificity 0.93, 95% CI: 0.79-0.98; LR+ 10.5, 95% CI: 2.91-39.5; and LR- 0.28, 95% CI: 0.17-0.49). The insufficient performance metrics reported precluded further statistical analysis of other performance metrics. CONCLUSIONS: ML on meningiomas is mostly carried out with traditional methods. For meningioma grading, traditional ML methods generally had a higher LR+, while DL models a lower LR-.

Telemedicine and the right to health: A neurosurgical perspective.

Neurosurgical task force is limited and unevenly distributed. Telemedicine has become increasingly popular, and could help neurosurgical centers meet patient right to care. This scoping review aims to evaluate the impact and feasibility of telemedicine on the right to neurosurgical care, using the AAAQ toolbox. The AAAQ toolbox consists of Availability, Accessibility, Acceptability and Quality. Neurosurgical availability is limited by the number of neurosurgeons, but by using task shifting and -sharing via telemedicine, the number of patients receiving neurosurgical care could increase without increasing the number of neurosurgeons. Telemedicine can improve geographic accessibility to neurosurgical care, but may also introduce technological literacy barriers. Acceptability of telemedicine is a double-edged sword; while a useful service, telemedicine also creates ethical concerns regarding privacy and confidentiality. Regulations and adaptations for vulnerable patient groups are key considerations for deploying telemedicine. Finally, there is emerging evidence that the quality of remote neurosurgical diagnostics and care can keep high standards. Overall, telemedicine has the potential of taking neurosurgery one step closer to meeting patient right to health, globally.

Statins and Gliomas: A Systematic Review of the Preclinical Studies and Meta-Analysis of the Clinical Literature.

BACKGROUND: Gliomas represent most common primary brain tumors. Glioblastoma (GBM) is the most common subtype and carries a poor prognosis. There is growing interest in the anti-glioma properties of statins. The aim of this study was to conduct a systematic review of the preclinical literature and to meta-analyze existing clinical studies to determine what benefit, if any, statins may confer in the context of glioma. METHODS: The PubMed, Embase, Cochrane, and Web of Science libraries were queried in May 2021. Preclinical studies were included if they investigated the anti-cancer effects of statins in glioma in vitro and in vivo. Clinical studies were included if they reported incidence rates of glioma by statin use, or mortality outcomes among GBM patients by statin use. Pooled point estimates were calculated using a random-effects model. RESULTS: In total, 64 publications, 51 preclinical and 13 clinical, were included. Preclinical studies indicated that statins inhibited glioma cell proliferation, migration, and invasion. These effects were time- and concentration-dependent. Synergistic anti-glioma effects were observed when statins were combined with other anti-cancer therapies. Clinical observational studies showed an inverse, albeit non-statistically significant, association between statin use and incidence rate of glioma (HR = 0.84, 95% CI 0.62-1.13, I2 = 72%, p-heterogeneity = 0.003, 6 studies). Statin use was not associated with better overall survival following GBM surgery (HR = 1.05, 95% CI 0.85-1.30, I2 = 30%, p-heterogeneity = 0.23, 4 studies). CONCLUSION: Statins were potent anti-cancer drugs that suppressed glioma growth through various mechanisms in vitro; these effects have translated into the clinical realm, clinically but not statistically, in terms of glioma incidence but not GBM survival.

The combined use of steroids and immune checkpoint inhibitors in brain metastasis patients: a systematic review and meta-analysis.

BACKGROUND: Immune checkpoint inhibitors (ICI) have been a breakthrough for selected cancer patients, including those with brain metastases (BMs). Likewise, steroids have been an integral component of symptomatic management of BM patients. However, clinical evidence on the interaction between ICI and steroids in BM patients is conflicting and has not adequately been summarized thus far. Hence, the aim of this study was to perform a systematic literature review and meta-analysis on the association between steroid use and overall survival (OS) in BM patients receiving ICI. METHODS: A systematic literature search was performed. Pooled effect estimates were calculated using random-effects models across included studies. RESULTS: After screening 1145 abstracts, 15 observational studies were included. Fourteen studies reported sufficient data for meta-analysis, comprising 1102 BM patients of which 32.1% received steroids. In the steroid group, median OS ranged from 2.9 to 10.2 months. In the nonsteroid group, median OS ranged from 4.9 to 25.1 months. Pooled results demonstrated significantly worse OS (HR = 1.84, 95% CI 1.22-2.77) and systemic progression-free survival (PFS; HR = 2.00, 95% CI 1.37-2.91) in the steroid group. Stratified analysis showed a consistent effect across the melanoma subgroup; not in the lung cancer subgroup. No significant association was shown between steroid use and intracranial PFS (HR = 1.31, 95% CI 0.42-4.07). CONCLUSIONS: Administration of steroids was associated with significantly worse OS and PFS in BM patients receiving ICI. Further research on dose, timing, and duration of steroids is needed to elucidate the cause of this association and optimize outcomes in BM patients receiving ICI.

Neurosurgical resection for locally recurrent brain metastasis.

BACKGROUND: In patients with locally recurrent brain metastases (LRBMs), the role of (repeat) craniotomy is controversial. This study aimed to analyze long-term oncological outcomes in this heterogeneous population. METHODS: Craniotomies for LRBM were identified from a tertiary neuro-oncological institution. First, we assessed overall survival (OS) and intracranial control (ICC) stratified by molecular profile, prognostic indices, and multimodality treatment. Second, we compared LRBMs to propensity score-matched patients who underwent craniotomy for newly diagnosed brain metastases (NDBM). RESULTS: Across 180 patients, median survival after LRBM resection was 13.8 months and varied by molecular profile, with >24 months survival in ALK/EGFR+ lung adenocarcinoma and HER2+ breast cancer. Furthermore, 102 patients (56.7%) experienced intracranial recurrence; median time to recurrence was 5.6 months. Compared to NDBMs (n = 898), LRBM patients were younger, more likely to harbor a targetable mutation and less likely to receive adjuvant radiation (P < 0.05). After 1:3 propensity matching stratified by molecular profile, LRBM patients generally experienced shorter OS (hazard ratio 1.67 and 1.36 for patients with or without a mutation, P < 0.05) but similar ICC (hazard ratio 1.11 in both groups, P > 0.20) compared to NDBM patients with similar baseline. Results across specific molecular subgroups suggested comparable effect directions of varying sizes. CONCLUSIONS: In our data, patients with LRBMs undergoing craniotomy comprised a subgroup of brain metastasis patients with relatively favorable clinical characteristics and good survival outcomes. Recurrent status predicted shorter OS but did not impact ICC. Craniotomy could be considered in selected, prognostically favorable patients.