Malignant cell clones resistant to chemotherapy and radiotherapy frequently lead to treatment failure in patients with glioblastoma multiforme. Preliminary studies suggest that adding chloroquine to conventional therapy may improve treatment outcomes. To examine the effect of adding chloroquine to conventional therapy for glioblastoma multiforme. Randomized, double-blind, placebo-controlled trial. National Institute of Neurology and Neurosurgery of Mexico. 30 patients with surgically confirmed glioblastoma confined to 1 cerebral hemisphere, with a Karnofsky performance score greater than 70, no comorbid disease, and age younger than 60 years. Oral chloroquine at 150 mg/d for 12 months beginning on postoperative day 5 or placebo. All patients received conventional chemotherapy and radiotherapy. Primary outcome was survival after surgery; surviving patients were followed up to October 2005. Periodic evaluation using the Karnofsky scale and imaging studies, as well as hematologic tests and ophthalmologic examinations, was done in all patients. Median survival after surgery was 24 months for chloroquine-treated patients and 11 months for controls. At the end of the observation period, 6 patients treated with chloroquine had survived 59, 45, 30, 27, 27, and 20 months, respectively; 3 patients from the control group had survived 32, 25, and 22 months, respectively. Although not statistically significantly different, the rate of death with time was approximately half as large in patients receiving chloroquine as in patients receiving placebo (hazard ratio, 0.52 [95% CI, 0.21 to 1.26]; P = 0.139). The observed advantage of chloroquine may be due to chance; differences in pretreatment characteristics and conventional treatment regimens could not be adjusted for because of the small sample size. Chloroquine may improve mid-term survival when given in addition to conventional therapy for glioblastoma multiforme. These results suggest that larger, more definitive studies of chloroquine as adjuvant therapy for glioblastoma are warranted.

Background: There is still some reluctance to refer pediatric patients for epilepsy surgery, despite evidence of success in retrospective series.Objective: To describe surgical experience and long-term outcome in pediatric temporal lobe epilepsy (TLE) at a single institution.Methods: Retrospective review of pediatric (<18-years-old) TLE patients who underwent surgery between November 1996 and December 2006 at Cleveland Clinic Epilepsy Center. Cox proportional hazard modeling was used to assess outcome predictors.Results: One hundred thirty pediatric patients met study criteria. Mean time between seizure onset and surgery was 6.3 years. Invasive evaluation was used in 32 patients (24.5%). Hippocampal sclerosis was present in 70 patients (53.8%), either alone or associated in dual pathology. The complication rate was 7%. The seizure-freedom rates at 1, 2, 5, and 12 years were 76%, 72%, 54%, and 41%, respectively (Kaplan-Meier). With the use of the Engel outcome classification, 98 (75.3%) patients were class I, 11 (8.5%) class II, 9 (7%) class III, and 12 (9.2%) were class IV at last follow-up. Only 4 (3.1%) patients underwent reoperations. Antiepileptic drugs (AEDs) were discontinued in 36 patients (28.3%) in a mean period of 18 months (SD ± 17 months; range, 6-102 months). Although left-sided resection, lower number of preoperative AED trials (≤4), and tumor pathology correlated with favorable seizure outcomes, extensive surgical resection remained the only significant outcome predictor after multivariate analysis (P = .007; HR = 0.13 [95% confidence interval 0.007–0.64]).Conclusion: Careful selection of surgical candidates by multidisciplinary evaluations is required. Long-term seizure control is achieved successfully with acceptable low complication rates.

Preoperative neurosurgical planning is an important step in avoiding surgical complications, reducing morbidity, and improving patient safety. The incursion of machine learning (ML) in this domain has recently gained attention, given the notable advantages in processing large datasets and potentially generating efficient and accurate algorithms in patient care. We explored the evolving applications of ML algorithms in the preoperative planning of brain and spine surgery. In accordance with the Arksey and O'Malley framework, a scoping review was conducted using 3 databases (PubMed, Embase, and Web of Science). Articles that described the use of ML for preoperative planning in brain and spine surgery were included. Relevant data were collected regarding the neurosurgical field of application, patient baseline features, disease description, type of ML technology, study's aim, preoperative ML algorithm description, and advantages and limitations of ML algorithms. Our search strategy yielded 7407 articles, of which 8 studies (5 retrospective, 2 prospective, and 1 experimental) satisfied the inclusion criteria. Clinical information from 518 patients (62.7% female; mean age: 44.8 years) was used for generating ML algorithms, including convolutional neural networks (14.3%), logistic regression (14.3%), and random forest (14.3%), among others. Neurosurgical fields of applications included functional neurosurgery (37.5%), tumor surgery (37.5%), and spine surgery (25%). The main advantages of ML included automated processing of clinical and imaging information, selection of an individualized patient surgical approach, and data-driven support for treatment decision-making. All studies reported technical limitations, such as long processing time, algorithmic bias, limited generalizability, and the need for database updating and maintenance. ML algorithms for preoperative neurosurgical planning are being developed for efficient, automated, and safe treatment decision-making. However, future studies are necessary to validate their objective performance across diverse clinical scenarios. Enhancing the robustness, transparency, and understanding of ML applications will be crucial for their successful integration into neurosurgical practice.