Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives. 

Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges

Liquid biopsy: creating opportunities in brain space

Percutaneous pedicle screws (PPSs) are commonly used in posterior spinal fusion to treat spine conditions such as trauma, tumors, and degenerative diseases. Precise PPS placement is essential in preventing neurological complications and improving patient outcomes. Recent studies have suggested that intraoperative computed tomography (CT) navigation can reduce the dependence on extensive surgical expertise for achieving accurate PPS placement. However, more comprehensive documentation is needed regarding the procedural accuracy of lateral spine surgery (LSS). In this retrospective study, we investigated patients who underwent posterior instrumentation with PPSs in the thoracic to lumbar spine, utilizing an intraoperative CT navigation system, between April 2019 and September 2023. The system’s methodology involved real-time CT-based guidance during PPS placement, ensuring precision. Our study included 170 patients (151 undergoing LLIF procedures and 19 trauma patients), resulting in 836 PPS placements. The overall PPS deviation rate, assessed using the Ravi scale, was 2.5%, with a notably higher incidence of deviations observed in the thoracic spine (7.4%) compared to the lumbar spine (1.9%). Interestingly, we found no statistically significant difference in screw deviation rates between upside and downside PPS placements. Regarding perioperative complications, three patients experienced issues related to intraoperative CT navigation. The observed higher rate of inaccuracies in the thoracic spine suggests that various factors may contribute to these differences in accuracy, including screw size and anatomical variations. Further research is required to refine PPS insertion techniques, particularly in the context of LSS. In conclusion, this retrospective study sheds light on the challenges associated with achieving precise PPS placement in the lateral decubitus position, with a significantly higher deviation rate observed in the thoracic spine compared to the lumbar spine. This study emphasizes the need for ongoing research to improve PPS insertion techniques, leading to enhanced patient outcomes in spine surgery.

Assessing Procedural Accuracy in Lateral Spine Surgery: A Retrospective Analysis of Percutaneous Pedicle Screw Placement with Intraoperative CT Navigation

A historical overview of the evolution of anterolateral approaches to the lumber spine and associated patient outcomes is presented. In addition, the modern incorporation of new technologies is discussed, including interbody cages, intraoperative image guidance , robotics, augmented reality, and machine learning, which have significantly improved the spine surgery safety and efficacy profile. Current challenges and future directions are also covered, emphasizing the need for further research and development, particularly in robotic assistance and machine learning algorithms. 

Advances in Anterolateral Approaches to the Lumbar Spine

Artificial Intelligence Applications to Detect Pediatric Brain Tumor Biomarkers

Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.

/pdf/assessing-metabolic-markers-in-glioblastoma-using-machine-284rcqiy.pdf

Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review

OBJECTIVE
While single-position surgery (SPS) eliminates the need for patient repositioning, the placement of screws in the unconventional lateral position poses unique challenges related to asymmetry relative to the surgical table. Use of robotic guidance or intraoperative navigation can help to overcome this. The aim of this study was to compare the relative accuracies offered by these various navigation modalities for pedicle screws placed in lateral SPS.


METHODS
According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the PubMed/Medline, Embase, and Cochrane Library databases were queried for studies reporting pedicle screw placement accuracy using fluoroscopic, CT-navigated, O-arm, or robotic guidance in lateral SPS, and a systematic review and meta-analysis was performed. Included studies all compared evaluated screw placement accuracy in lateral SPS using a single navigation method. Quality assessment was performed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system; risk of bias was assessed using the Newcastle-Ottawa Scale and the Joanna Briggs Institute checklist. The primary outcome, rate of pedicle screw breach, was analyzed using random-effects meta-analysis.


RESULTS
Eleven studies were included comprising 548 patients who underwent the placement of instrumentation with 2488 screws. For the fluoroscopic, CT-navigated, O-arm, and robotic guidance cohorts, there were 3, 2, 3, and 3 studies, respectively. Breach rates by modality were as follows: fluoroscopic guidance (6.6%), CT navigation (4.7%), O-arm (3.9%), and robotic guidance (3.9%). Random-effects meta-analysis showed a significant difference between studies, with an overall breach rate of 4.9% (95% CI 3.1%-7.5%; p < 0.001); however, testing for subgroup differences failed to show a significant difference between guidance modalities (QM = 0.69, df = 3; p = 0.88). Heterogeneity between studies was significant (I2 = 79.0%, τ2 = 0.41, χ2 = 47.65, df = 10; p < 0.001).


CONCLUSIONS
Robotic guidance of screws is noninferior to alternative guidance modalities in lateral SPS; however, additional prospective studies directly comparing different guidance types are merited.

Robot-assisted percutaneous pedicle screw placement accuracy compared with alternative guidance in lateral single-position surgery: a systematic review and meta-analysis.

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Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review

Robot-assisted percutaneous pedicle screw placement accuracy compared with alternative guidance in lateral single-position surgery: a systematic review and meta-analysis.