BackgroundFocused 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... Show moreBackgroundFocused 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 methodsA 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.ResultsIn 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.ConclusionFUS 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. Show less
BACKGROUND: Supplementary motor area syndrome (SMAS) may occur after frontal tumor surgery, with variable presentation and outcomes. We reviewed the literature on postoperative SMAS after brain... Show moreBACKGROUND: Supplementary motor area syndrome (SMAS) may occur after frontal tumor surgery, with variable presentation and outcomes. We reviewed the literature on postoperative SMAS after brain tumor resection.METHODS: PubMed, Web of Science, Scopus, and Cochrane were searched following the PRISMA guidelines to include studies reporting SMAS after brain tumor resection.RESULTS: We included 31 studies encompassing 236 patients. Most tumors were gliomas (94.5%), frequently of low grade (61.4%). Most lesions were located on the left hemisphere (64.4%), involving the supplementary motor area (61.4%) and the cingulate gyrus (20.8%). Tractography and functional magnetic resonance imaging evaluation were completed in 45 (19.1%) and 26 (11%) patients. Gross total resection was achieved in 46.3% patients and complete SMA resection in 69.4%. A total of 215 procedures (91.1%) used intraoperative neuromonitoring mostly consisting of direct cortical/subcortical stimulation (56.4%), motor (33.9%), and somatosensory (25.4%) evoked potentials. Postoperative SMAS symptoms occurred within 24 hours after surgery, characterized by motor deficits (97%), including paresis (68.6%) and hemiplegia (16.1%), and speech disorders (53%), including hesitancy (24.2%) and mutism (22%). Average SMAS duration was 45 days (range, 1-365 days), with total resolution occurring in 188 patients (79.7%) and partial improvement in 46 (19.5%). Forty-eight patients (20.3%) had persisting symptoms, mostly speech hesitancy (60.4%) and fine motor disorders (45.8%).CONCLUSIONS: Postoperative SMAS may occur within the first 24 hours after mesial frontal tumor surgery. Preoperative mapping and intraoperative neuromonitoring may assist resection and predict outcomes. Neuroplasticity and interhemispheric connectivity play a major role in resolution. Show less
Despite improved surgical and adjuvant treatment options, malignant brain tumors remain non-curable to date. The thin line between treatment effectiveness and patient harms underpins the importance... Show moreDespite improved surgical and adjuvant treatment options, malignant brain tumors remain non-curable to date. The thin line between treatment effectiveness and patient harms underpins the importance of tailoring clinical management to the individual brain tumor patient. Over the past decades, the volume and complexity of clinically-derived patient data (i.e., imaging, genomics, free-text etc.) is increasing exponentially. Machine learning provides a vast range of algorithms that can learn from this data and guide clinical decision-making by providing accurate patient-level predictions. The current thesis describes several studies along the continuum of the machine learning spectrum as it applies to neurosurgical oncology. Part I investigates postoperative complications and risk factors in patients operated for a primary malignant brain tumor. Part II describes de development of a model for the prediction of individual-patient survival in glioblastoma patients. Part III encompasses the development of a natural language processing framework for automated medical text analysis. Machine learning algorithms should be considered as an extension to statistical approaches and exist along a continuum determined by how much is specified by humans and how much is learnt by the machine. Although machine learning algorithms can produce highly accurate predictions based on high-dimensional data, clinicians and researchers should interpret the clinical implications of these predictions on case-by-case basis. Show less
