Exploring Pediatric Vertebral, Sacral, and Pelvic Osteosarcomas through the NCDB: Demographics, Treatment Utilization, and Survival Outcomes.

BACKGROUND AND OBJECTIVES: Retrieve data from the National Cancer Database (NCDB) to examine information on the epidemiological prevalence, treatment strategies, and survival outcomes of pediatric vertebral, sacral and pelvic osteosarcomas. METHODS: We reviewed NCDB data from 2008 to 2018, concentrating on vertebral, sacral, and pelvic osteosarcomas in children 0 to 21 years. Our analysis involved logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. RESULTS: The study population included 207 patients. For vertebral osteosarcomas, 62.5% of patients were female, and 78.1% were white. Regional lymph node involvement predicted 80 times higher mortality hazard (p = 0.021). Distant metastasis predicted 25 times higher mortality hazard (p = 0.027). For sacral and pelvic osteosarcomas, 58.3% of patients were male, and 72% were white. Patients with residual tumor were 4 times more likely to have prolonged LOS (p = 0.031). No residual tumor (HR = 0.53, p = 0.03) and radiotherapy receipt (HR = 0.46, p = 0.034) were associated with lower mortality hazards. Distant metastasis predicted 3 times higher mortality hazard (p < 0.001). Hispanic ethnicity was linked to lower resection odds (OR = 0.342, p = 0.043), possibly due to language barriers affecting patient understanding and care decisions. CONCLUSIONS: In conclusion, our examination of NCDB offers a thorough exploration of demographics, treatment patterns, and results, highlighting the importance of personalized approaches to enhance patient outcomes.

Mathematical modeling of meningioma volume change after radiation treatment.

OBJECTIVE: Meningiomas are the most common primary central nervous tumor and are often treated with radiation therapy. This study examines the long-term volumetric changes of intracranial meningiomas in response to radiation therapy. The objective is to analyze and model the volumetric changes following treatment. METHODS: Data from a retrospective single-institution database (2005-2015) were used, with inclusion criteria being patients with a diagnosis of meningiomas, along with additional inclusion criteria consisting of treatment with radiation, having at least three magnetic resonance imaging (MRI) scans with one or more before and after radiation treatment, and the patients following up for at least eighteen months. Exclusion criteria consisted of patients less than 18 years old, patients receiving surgery and/or adjuvant chemotherapy following radiation, and patients without any available details regarding radiation treatment parameters. Tumor volumes were measured via T1-weighted post-contrast MRI and calculated using the ABC/2 ellipsoidal approximation, a method allowing for the measurement of non-linear growth volume reduction. RESULTS: Of 48 meningioma patients considered, 10 % experienced post-radiation growth, while 75 % witnessed a ≥50 % decrease in volume over a follow-up period of 0.3-14.9 years. Median decay rate was 0.81, and within 1.17 years, 90 % achieved the predicted volume reduction. Predicted vs. actual volumes showed a mean difference of 0.009 ± 0.347 cc. Initial tumor volumes strongly correlated (Pearson's R=0.98, R-squared=0.96) with final asymptotic volumes, which had a median of 1.50 cc, with interquartile range (IQR) = [0.39, 3.67]. CONCLUSION: 90 % of patients achieved tumor-volume reduction at 1.17 years post-treatment, reaching a non-zero asymptote strongly correlated with initial tumor volume, and 75 % experienced at least a 50 % volume decrease. Individual volume changes for responsive meningiomas can be modeled and predicted using exponential decay curves.

Optimal Timing in Cervical Spinal Cord Injury: A Comprehensive Meta-Analysis of Ultra-Early Surgical Intervention Within Five Hours.

The optimal timing of surgery for cervical spinal cord injuries (SCI) and its impact on neurological recovery continue to be subjects of debate. This systematic review and meta-analysis aims to consolidate and assess the existing evidence regarding the efficacy of ultra-early decompression surgery in improving clinical outcomes after cervical SCI. A search was conducted in PubMed, Embase, Cochrane, and CINAHL databases from inception until September 18, 2023, focusing on human studies. The groups were categorized into ultra-early decompression (decompression surgery ≤ 5 hours post-injury) and a control group (decompression surgery between 5-24 hours post-injury). A random effects meta-analysis was performed on all studies using R Studio. Outcomes were reported as effect size (OR, treatment effect, and 95% CI. Of the 140 patients, 63 (45%) underwent decompression ≤ 5 hours, while 77 (55%) underwent decompression > 5 hours post-injury. Analysis using the OR model showed no statistically significant difference in the odds of neurological improvement between the ultra-early group and the early group (OR = 1.33, 95% CI: 0.22-8.18, p = 0.761). This study did not observe significant neurological improvement among cervical SCI patients who underwent decompression within five hours. Due to the scarcity of literature on the ultra-early decompression of cervical SCI, this study underscores the necessity for additional investigation into the potential benefits of earlier interventions for cervical SCI to enhance patient outcomes.

Chondrosarcoma of the Mobile Spine in the Elderly: A National Cancer Database Study.

BACKGROUND: The current research on geriatric patients with spinal chondrosarcoma is limited. This study aimed to investigate the demographics, patterns of care, and survival of geriatric patients with chondrosarcoma of the mobile spine. METHODS: The National Cancer Database was queried from 2008 to 2018 for geriatric patients (60-89 years) with chondrosarcoma of the mobile spine. The primary outcome of this study was overall survival. The secondary outcome was treatment utilization patterns. Survival analyses were conducted using log-rank tests and Cox proportional hazards regressions. Logistic regression models were utilized to assess correlations between baseline variables and treatment utilization. RESULTS: The database retrieved 122 patients. While 43.7% of the patients presented with tumors exceeding 5 cm in size, the incidence of regional lymph node involvement or distant metastases was relatively low, affecting only 5% of the patients. Furthermore, 22.3% of the patients had tumors graded as 3-4. The 5-year overall survival rate was 52.9% (95% confidence interval: 42-66.6). The mortality risk was significantly associated with age, tumor grade and stage, and treatment plan. Most patients (79.5%) underwent surgery, while 35.9% and 4.2% were treated with radiotherapy and chemotherapy, respectively. Age, race, comorbidities, geographical region, tumor stage, and healthcare facility type significantly correlated with treatment utilization. CONCLUSIONS: Surgical resection significantly lowered the mortality risk in geriatric patients with spinal chondrosarcomas. Demographic and geographical factors significantly dictated treatment plans. Further studies are required to assess the role of radiotherapy and chemotherapy in treating these patients in the modern era.

Bibliometric and trend analysis of the top 100 most-cited articles on lateral interbody fusion (LIF).

PURPOSE: Lateral interbody fusion (LIF) is an increasingly popular minimally-invasive spine procedure. This study identifies notable trends in LIF literature and provides a detailed review of the bibliometric aspects of the top 100 most-cited articles. METHODS: Articles were queried from the Web of Science database. Inclusion criteria consisted of peer-reviewed articles, full-text availability, and LIF focus. Network analysis including co-authorship mapping and bibliographic coupling were complemented by trend analysis to determine prominent contributors and themes. Analyses were conducted using VOSviewer and Bibliometrix (RStudio). RESULTS: There has been a rapid increase in LIF publication and citation count since 1998. Leading journals were Spine (n = 24), Journal of Neurosurgery Spine (n = 22), and European Spine Journal (n = 12). NuVasive funded the most publications (n = 17), followed by DePuy Synthes Spine (n = 4). The United States was the most represented country (n = 81); however, trend analysis suggests a steadily growing international contribution. The most prolific author was J.S. Uribe (n = 16), followed by a tie in second place by E. Dakwar and L. Pimenta (n = 8). The most frequent keywords, "complication" (n = 34), "surgery" (n = 30), and "outcomes" (n = 24), demonstrated a patient-centric theme. CONCLUSIONS: This bibliometric analysis provides in-depth insights into the evolution and trends of LIF over the last two decades. The trends and themes identified demonstrate the innovative, collaborative, and patient-focused characteristics of this subfield. Future researchers can use this as a foundation for understanding the past and present state of LIF research while designing investigations.

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility. METHODS: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application. RESULTS: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively. CONCLUSIONS: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

An Interpretable Machine Learning Approach to Predict Survival Outcomes in Spinal and Sacropelvic Chordomas.

STUDY DESIGN: Retrospective, population-based cohort study. OBJECTIVE: This study aimed to develop machine learning (ML) models to predict five-year and 10-year mortality in spinal and sacropelvic chordoma patients and integrate them into a web application for enhanced prognostication. SUMMARY OF BACKGROUND DATA: Past research has uncovered factors influencing survival in spinal chordoma patients. While identifying individual predictors is important, personalized survival predictions are equally vital. Though prior efforts have resulted in nomograms aiming to serve this purpose, they cannot capture complex interactions within data and rely on statistical assumptions that may not fit real-world data. METHODS: Adult spinal and sacropelvic chordoma patients were identified from the National Cancer Database. Sociodemographic, clinicopathologic, diagnostic, and treatment-related variables were utilized as predictive features. Five supervised ML algorithms (TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest) were implemented to predict mortality at five and 10 years postdiagnosis. Model performance was primarily evaluated using the area under the receiver operating characteristic (AUROC). SHapley Additive exPlanations (SHAP) values and partial dependence plots provided feature importance and interpretability. The top models were integrated into a web application. RESULTS: From the NCDB, 1206 adult patients diagnosed with histologically confirmed spinal and sacropelvic chordomas were retrieved for the five-year mortality outcome [423 (35.1%) with five-year mortality] and 801 patients for the 10-year mortality outcome [588 (73.4%) with 10-year mortality]. Top-performing models for both of the outcomes were the models created with the CatBoost algorithm. The CatBoost model for five-year mortality predictions displayed a mean AUROC of 0.801, and the CatBoost model predicting 10-year mortality yielded a mean AUROC of 0.814. CONCLUSIONS: This study developed ML models that can accurately predict five-year to 10-year survival probabilities in spinal chordoma patients. Integrating these interpretable, personalized prognostic models into a web application provides quantitative survival estimates for a given patient. The local interpretability enables transparency into how predictions are influenced. Further external validation is warranted to support generalizability and clinical utility.

Ultra-Early (<5 Hours) Decompression for Thoracolumbar Spinal Cord Injury: A Case Series.

Early surgical decompression within 24 hours for traumatic spinal cord injury (SCI) is associated with improved neurological recovery. However, the ideal timing of decompression is still up for debate. The objective of this study was to utilize our retrospective single-institution series of ultra-early (<5 hours) decompression to determine if ultra-early decompression led to improved neurological outcomes and was a feasible target over previously defined early decompression targets. Retrospective data on patients with SCI who underwent ultra-early (<5 hours) decompression at a level one metropolitan trauma center were extracted and collected from 2015-2018. American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade improvement was the primary outcome, with ASIA Motor score improvement and complication rate as secondary outcomes. Four individuals met the criteria for inclusion in this case series. All four suffered thoracolumbar SCI. All patients improved neurologically by AIS grade, and there were no complications directly related to ultra-early surgery. Given the small sample size, there was no statistically significant difference in outcomes compared to a control group who underwent early (5-24 hour) decompression in the same period. Ultra-early decompression is a feasible and safe target for thoracolumbar SCI and may lead to improved neurological outcomes without increased risk of complications. This case series can help create the foundation for future, larger studies that may definitively show the benefit of ultra-early decompression.

Intraspinal cervicothoracic junction chondrosarcoma: illustrative case.

BACKGROUND: Chondrosarcoma is an uncommon spinal tumor that can present as an extraskeletal mass. Rarely, these tumors present as dumbbell tumors through the neural foramina, mimicking schwannomas or neurofibromas. OBSERVATIONS: A 46-year-old female presented with 2 years of worsening right-arm radiculopathy. Magnetic resonance imaging of the thoracic spine revealed a peripherally enhancing extramedullary mass through the right T1 foramen and compressing the spinal cord. Computed tomography showed the mass to be partially calcified. She underwent C7-T2 laminectomy and C6-T3 posterior instrumented fusion with gross-total resection of an extradural mass. Pathology revealed a grade I chondrosarcoma. Her symptoms improved postoperatively, with some residual right-arm radicular pain. LESSONS: Intraspinal extradural dumbbell conventional chondrosarcoma is rare, with only 9 cases, including ours, reported. Patient ages range from 16 to 72 years old, and male sex is more common in these cases. The most common location is the thoracic spine, and our case is the only reported one in the cervicothoracic junction. These tumors often mimic schwannomas on imaging, but chondrosarcoma should remain in the differential diagnosis, because management of these tumors differs. Chondrosarcoma may benefit from more aggressive resection, including en bloc resection, and may require adjuvant radiotherapy.