Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy. Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma. Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis. Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.

Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that affects children and adults. Individuals with NF1 are at high risk for central nervous system neoplasms including gliomas. The purpose of this review is to discuss the spectrum of intracranial gliomas arising in individuals with NF1 with a focus on recent preclinical and clinical data. In this review, possible mechanisms of gliomagenesis are discussed, including the contribution of different signaling pathways and tumor microenvironment. Furthermore, we discuss the recent notable advances in the developing therapeutic landscape for NF1-associated gliomas including clinical trials and collaborative efforts.

OBJECTIVEDespite numerous imaging studies highlighting the importance of the thalamus in a patient’s surgical prognosis, human electrophysiological studies involving the limbic thalamic nuclei are limited. The objective of this study was to evaluate the safety and accuracy of robot-assisted stereotactic electrode placement in the limbic thalamic nuclei of patients with suspected temporal lobe epilepsy (TLE).METHODSAfter providing informed consent, 24 adults with drug-resistant, suspected TLE undergoing evaluation with stereoelectroencephalography (SEEG) were enrolled in the prospective study. The trajectory of one electrode planned for clinical sampling of the operculoinsular cortex was modified to extend it to the thalamus, thereby preventing the need for additional electrode placement for research. The anterior nucleus of the thalamus (ANT) (n = 13) and the medial group of thalamic nuclei (MED) (n = 11), including the mediodorsal and centromedian nuclei, were targeted. The postimplantation CT scan was coregistered to the preoperative MR image, and Morel’s thalamic atlas was used to confirm the accuracy of implantation.RESULTSTen (77%) of 13 patients in the ANT group and 10 (91%) of 11 patients in the MED group had electrodes accurately placed in the thalamic nuclei. None of the patients had a thalamic hemorrhage. However, trace asymptomatic hemorrhages at the cortical-level entry site were noted in 20.8% of patients, who did not require additional surgical intervention. SEEG data from all the patients were interpretable and analyzable. The trajectories for the ANT implant differed slightly from those of the MED group at the entry point—i.e., the precentral gyrus in the former and the postcentral gyrus in the latter.CONCLUSIONSUsing judiciously planned robot-assisted SEEG, the authors demonstrate the safety of electrophysiological sampling from various thalamic nuclei for research recordings, presenting a technique that avoids implanting additional depth electrodes or compromising clinical care. With these results, we propose that if patients are fully informed of the risks involved, there are potential benefits of gaining mechanistic insights to seizure genesis, which may help to develop neuromodulation therapies.

Patient satisfaction reflects the patients’ perception of the outcome of care and is being considered for use in future reimbursement schemes. No consensus exists regarding the best instrument to measure patient satisfaction in the field of spine surgery. This systematic review aimed to determine how patient satisfaction for spine surgery has been measured previously and whether a disease-specific, comprehensive instrument to measure patient satisfaction has been established; we also aimed to define the dimensions of care that determine patient satisfaction in spine surgery. A systematic search of three online databases, unpublished sources, and citations was undertaken to identify 156 empirical studies that reported on patient satisfaction in the field of spine surgery. Manuscripts were reviewed in terms of the patient satisfaction instrument used, and the instruments were categorized as per content and method axes. Taxonomy of patient satisfaction with spine surgery identified the major characteristics of providers and medical care that influenced patient satisfaction and acted as a structure to categorically define the dimensions of patient satisfaction in spine surgery. The reviewed studies predominantly used global (108/156) rather than multidimensional (46/156), instruments. Most studies (96.2%) reported satisfaction with outcome rather than with care, and only 18.5% of the studies (29/156) utilized a disease-specific instrument. The following seven dimensions of patient status, outcome, and care experience that affected patient satisfaction were identified: pain, function, patient expectations/preference, specific patient health characteristics, caregiver interpersonal manner, efficacy/clinical outcomes, and postoperative care/therapy. Currently, no disease-specific instrument that includes all dimensions of patient satisfaction in spine surgery has been developed. Such a patient satisfaction instrument should be designed, tested for reliability and validity, and widely implemented.

OBJECTIVEDespite an aggressive multimodal therapeutic regimen, glioblastoma (GBM) continues to portend a grave prognosis, which is driven in part by tumor heterogeneity at both the molecular and cellular levels. Accordingly, herein the authors sought to identify metabolic differences between GBM tumor core cells and edge cells and, in so doing, elucidate novel actionable therapeutic targets centered on tumor metabolism.METHODSComprehensive metabolic analyses were performed on 20 high-grade glioma (HGG) tissues and 30 glioma-initiating cell (GIC) sphere culture models. The results of the metabolic analyses were combined with the Ivy GBM data set. Differences in tumor metabolism between GBM tumor tissue derived from within the contrast-enhancing region (i.e., tumor core) and that from the peritumoral brain lesions (i.e., tumor edge) were sought and explored. Such changes were ultimately confirmed at the protein level via immunohistochemistry.RESULTSMetabolic heterogeneity in both HGG tumor tissues and GBM sphere culture models was identified, and analyses suggested that tyrosine metabolism may serve as a possible therapeutic target in GBM, particularly in the tumor core. Furthermore, activation of the enzyme tyrosine aminotransferase (TAT) within the tyrosine metabolic pathway influenced the noted therapeutic resistance of the GBM core.CONCLUSIONSSelective inhibition of the tyrosine metabolism pathway may prove highly beneficial as an adjuvant to multimodal GBM therapies.