Sarah Estar Johnson

To evaluate the use of percutaneous curved vertebroplasty (PCVP) and unipedicular vertebroplasty (UVP) for the treatment of osteoporotic vertebral compression fractures (OVCFs) through a systematic review and meta-analysis of the available literature. A systematic review of the scientific literature was conducted. Three randomized controlled trials and one retrospective study were included. Our outcomes of interest included injected cement volume, postoperative visual analog scale (VAS) scores, operative time, and cement leakage rate. Risk of bias tables were generated to determine the quality of each article. Four studies with 302 patients were included in the study. There were 142 (47.02%) patients in the PCVP group and 160 (52.98%) patients in the UVP group. PCVP was associated with significantly higher volumes of injected cement than UVP (MD: 1.20, CI: [0.27; 2.12], p = 0.01). Despite showing a strong trend in favor of PCVP, the meta-analysis did not report its superiority over UVP in terms of VAS (MD: -0.69, CI: [-1.66; 0.28], p = 0.16) and cement leakage rates (OR: 1.65, CI: [0.24; 1.75], p = 0.26), likely due to a few number of studies. Additionally, both the procedures had comparable mean operative times (MD: 0.20, CI: [-1.62; 2.03], p = 0.83), possibly due to overlapping procedure protocols. PCVP reported a significantly higher volume of the injected cement compared to UVP for the management of OVCFs. While trends favoring PCVP in postoperative outcomes were observed, the limited number of studies warrants further high-quality investigations, particularly randomized controlled trials with long-term outcomes, to establish the definitive superiority of either technique.

Background: Pineal parenchymal tumor of intermediate differentiation (PPTID) is a rare tumor. This study aims to evaluate patient outcomes and propose a treatment algorithm based on existing literature and our case series. Methods: This prospective observational study includes seven patients diagnosed with PPTID through histopathology. We analyzed their clinical presentation, magnetic resonance imaging findings, surgical approaches, histopathological and immunohistochemical analysis, adjuvant treatments, and outcomes. We conducted univariate and multivariate statistical analyses. Results: The mean patient age was 40 years, with a male predominance. All patients presented with hydrocephalus, four of which required cerebrospinal fluid diversion procedures. The average tumor size was 3.13cm, with 85.7% showing brain invasion. Surgical outcomes included one gross total resection, two near total resections, and four subtotal resections. The supracerebellar infratentorial (Krause) approach was used in 71.4% of cases. About 85.7% were diagnosed with grade 3 PPTID. Five patients received adjuvant radiotherapy. The analysis showed each additional mitosis unit decreased survival by 0.17 units, equating to roughly 2 months (P < 0.016). Follow-up ranged from 6 to 120 months, with a 5-year survival rate of 57.1%. Factors influencing survival included the extent of tumor resection, brain invasion, tumor grade, and adjuvant treatment, although it was not statistically significant due to the small sample size. Conclusion: We propose a treatment algorithm for PPTID and highlight the importance of further research to understand its biological characteristics. Safe maximal resection appears beneficial for higher-grade PPTID, but the role of adjuvant treatment after complete resection of lower-grade tumors remains uncertain.

Radiofrequency ablation (RFA) of the basivertebral and sinuvertebral nerve is a relatively new intervention for patients with chronic discogenic low back pain. It aims to ablate the irritated nerve endings to improve pain control and disability. This meta-analysis includes prospective single and double-arm studies that determine if RFA is effective in improving chronic low back pain and disability for patients with degenerative spinal discs who have not yet required surgical decompression. The outcomes of interest were comprised of commonly used patient-reported outcomes, which included the Oswestry Disability Index (ODI) and Visual Analog Scale (VAS) scores. Forest plots were generated to report associations as well as funnel plots to report the risk of publication bias. The meta-analysis included 429 patients from five eligible studies, comprising two randomized controlled trials and three prospective single-arm trials. A total of 280 patients underwent RFA whereas 149 served as controls. The baseline scores in the single-arm trials served as the outcomes of their control group. The RFA group (n = 280) had significantly lower ODI scores (mean difference = - 28.08; 95% CI: [- 43.53, - 12.63]) than the control group (n = 240). Similarly, the RFA group (n = 279) had significantly lower VAS scores (mean difference = - 3.16; 95% CI: [- 5.02, - 1.31]) than the control group (n = 238). Our study demonstrates RFA as a promising intervention for chronic discogenic low back pain, noting significant improvements in pain control and disability. Although not intended to substitute surgical decompression, it can serve as a better alternative to other non-operative therapies in patients who do not require surgical management. Clinical trial number: not applicable.

Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice.