Three-Dimensional-Printed Titanium Versus Polyetheretherketone Cages for Lumbar Interbody Fusion: A Systematic Review of Comparative In Vitro, Animal, and Human Studies.

Interbody fusion is a workhorse technique in lumbar spine surgery that facilities indirect decompression, sagittal plane realignment, and successful bony fusion. The 2 most commonly employed cage materials are titanium (Ti) alloy and polyetheretherketone (PEEK). While Ti alloy implants have superior osteoinductive properties they more poorly match the biomechanical properties of cancellous bones. Newly developed 3-dimensional (3D)-printed porous titanium (3D-pTi) address this disadvantage and are proposed as a new standard for lumbar interbody fusion (LIF) devices. In the present study, the literature directly comparing 3D-pTi and PEEK interbody devices is systematically reviewed with a focus on fusion outcomes and subsidence rates reported in the in vitro, animal, and human literature. A systematic review directly comparing outcomes of PEEK and 3D-pTi interbody spinal cages was performed. PubMed, Embase, and Cochrane Library databases were searched according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. Mean Newcastle-Ottawa Scale score for cohort studies was 6.4. A total of 7 eligible studies were included, comprising a combination of clinical series, ovine animal data, and in vitro biomechanical studies. There was a total population of 299 human and 59 ovine subjects, with 134 human (44.8%) and 38 (64.4%) ovine models implanted with 3D-pTi cages. Of the 7 studies, 6 reported overall outcomes in favor of 3D-pTi compared to PEEK, including subsidence and osseointegration, while 1 study reported neutral outcomes for device related revision and reoperation rate. Though limited data are available, the current literature supports 3D-pTi interbodies as offering superior fusion outcomes relative to PEEK interbodies for LIF without increasing subsidence or reoperation risk. Histologic evidence suggests 3D-Ti to have superior osteoinductive properties that may underlie these superior outcomes, but additional clinical investigation is merited.

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

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.

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

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.