Fractionated radiotherapy for surgically resected intracranial meningiomas: A multicentre retrospective cohort study.

BACKGROUND: Aside from surgical resection, the only standard of care treatment modality for meningiomas is radiotherapy (RT). Despite this, few studies have focused on identifying clinical covariates associated with failure of fractionated RT following surgical resection (fRT), and the timing of fRT following surgery still remains controversial (adjuvant versus salvage fRT). We assessed the outcomes of the largest, multi-institutional cohort of surgically resected meningiomas treated with subsequent adjuvant and salvage fRT to identify factors associated with local freedom from recurrence (LFFR) over 3-10 years post-fRT and to determine the optimal timing of fRT. METHODS: Patients with intracranial meningiomas who underwent surgery and fRT between 1997 and 2018 were included. Primary endpoints were radiographic recurrence/progression and time to progression from the completion of fRT. RESULTS: 404 meningiomas were included for analysis. Of these, 167 (41.3%) recurred post-fRT. Clinical covariates independently associated with worse PFS post-fRT included receipt of previous RT to the meningioma, having a WHO grade 3 meningioma or recurrent meningioma, the meningioma having a higher MIB1-index or brain invasion on pathology, and older patient age at diagnosis. Subgroup analysis identified higher MIB1-index as a histological factor associated with poorer LFFR in WHO grade 2 meningiomas. 179 patients underwent adjuvant RT shortly after surgery whereas 225 patients had delayed, salvage fRT after recurrence/progression. Following propensity score matching, patients that underwent adjuvant fRT had improved LFFR post-fRT compared to those that received salvage fRT. CONCLUSION: There is a paucity of clinical factors that can predict a meningioma's response to fRT following surgery. Adjuvant fRT may be associated with improved PFS post-fRT compared to salvage fRT. Molecular biomarkers of RT-responsiveness are needed to better inform fRT treatment decisions.

Outcomes and predictors of response to fractionated radiotherapy as primary treatment for intracranial meningiomas.

Background: Surgery is the primary treatment for most meningiomas. However, primary fractionated radiotherapy (fRT) remains an option for patients with larger meningiomas in challenging anatomic locations or patients at prohibitively high surgical risk. Outcome prediction for these patients is uncertain and cannot be guided by histopathology without available tumor tissue from surgery. Therefore, we aimed to assess the clinical factors that contribute to treatment failure in a large cohort of meningiomas consecutively treated with fRT as primary therapy, with the goal of identifying predictors of response. Methods: Patients treated with primary fRT for intracranial meningiomas from 1998 to 2017 were reviewed. Those who received primary surgical resection, radiosurgery, previous fRT, or had <6 months of clinical follow-up were excluded. We applied logistic regression and Cox regression modeling to ascertain key predictors of treatment failure, progression-free survival (PFS), and adverse events (AE) following fRT. Results: Our cohort included 137 meningiomas, 21 of which progressed after fRT (median PFS 3.45 years). Progressive meningiomas had a larger median gross tumor volume (GTV) compared to those that remained stable (19.1 cm3 vs 9.6 cm3, p = 2.86 × 10-2). GTV > 11.27 cm3 was independently predictive of progression and larger GTV was associated with higher risk of significant (grades 3/4) AE following fRT. Cavernous sinus and optic nerve sheath meningiomas had overall excellent outcomes post-fRT. Conclusions: We present a large cohort of meningiomas treated with primary fRT and find GTV and anatomic location to be key predictors of outcome, adding to the complex treatment considerations for this heterogeneous disease.

Multicenter retrospective cohort study of the association between surgery for odontoid fractures in the elderly and in-hospital outcomes.

Odontoid fractures are increasingly prevalent in older adults and associated with high morbidity and mortality. Optimal management remains controversial. Our study aims to investigate the association between surgical management of odontoid fractures and in-hospital mortality in a multi-center geriatric cohort. We identified patients 65 years or older with C2 odontoid fractures from the Trauma Quality Improvement Program database. The primary study outcome was in-hospital mortality. Secondary outcomes were in-hospital complications and hospital length of stay. Generalized estimating equation models were used to compare outcomes between operative and non-operative cohorts. Among the 13,218 eligible patients, 1100 (8.3%) were treated surgically. The risk of in-hospital mortality did not differ between surgical and non-surgical groups, after patient and hospital-level adjustment (OR: 0.94, 95%CI: 0.55-1.60). The risks of major complications and immobility-related complications were higher in the operative cohort (adjusted OR: 2.12, 95%CI: 1.53-2.94; and OR: 2.24, 95%CI: 1.38-3.63, respectively). Patients undergoing surgery had extended in-hospital length of stay compared to the non-operative group (9 days, IQR: 6-12 days vs. 4 days, IQR: 3-7 days). These findings were supported by secondary analyses that considered between-center differences in rates of surgery. Among geriatric patients with odontoid fractures surgical management was associated with similar in-hospital mortality, but higher in-hospital complication rates compared to non-operative management. Surgical management of geriatric patients with odontoid fractures requires careful patient selection and consideration of pre-existing comorbidities.

DNA methylation profiling of a lipomatous meningioma: illustrative case.

BACKGROUND: Lipomatous meningiomas are an extremely rare, benign meningioma subtype subcategorized under metaplastic meningioma in the most recent 2021 update to the World Health Organization classification. They make up less than 0.3% of all meningiomas and, to date, less than 70 cases have been reported in the literature, none of which have undergone molecular profiling. This study aims to promote the utility of molecular profiling to better diagnose these rare tumors. OBSERVATIONS: The authors present the first case of a lipomatous meningioma with DNA methylation profiling that both confirmed its benign biology and uncovered unique cytogenetic changes. Molecular characterization of a lipomatous meningioma confirmed its diagnosis as a distinct, benign meningioma subtype and revealed several copy number variations on chromosome 8 and in NF2 and SMARCB1. Here we discuss some of the radiological and histopathological features of lipomatous meningiomas, how they can be used to distinguish from other meningiomas and other similarly presenting tumors, and a brief literature review discussing the pathophysiology and presentation of this rare tumor. LESSONS: This study provides evidence supporting the use of molecular profiling to diagnose lipomatous meningiomas and guide their clinical management more accurately.

A deep learning model for detection of cervical spinal cord compression in MRI scans.

Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volume of medical imaging data and provide initial interpretation of images gathered in a primary-care setting. We aimed to develop and validate a deep learning model for detection of cervical spinal cord compression in MRI scans. Patients undergoing surgery for DCM as a part of the AO Spine CSM-NA or CSM-I prospective cohort studies were included in our study. Patients were divided into a training/validation or holdout dataset. Images were labelled by two specialist physicians. We trained a deep convolutional neural network using images from the training/validation dataset and assessed model performance on the holdout dataset. The training/validation cohort included 201 patients with 6588 images and the holdout dataset included 88 patients with 2991 images. On the holdout dataset the deep learning model achieved an overall AUC of 0.94, sensitivity of 0.88, specificity of 0.89, and f1-score of 0.82. This model could improve the efficiency and objectivity of the interpretation of cervical spine MRI scans.

Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions.

STUDY DESIGN: Narrative review. OBJECTIVES: We aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders. METHODS: A literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included. RESULTS: Within spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology. CONCLUSION: Although substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.

Predicting Outcomes After Surgical Decompression for Mild Degenerative Cervical Myelopathy: Moving Beyond the mJOA to Identify Surgical Candidates.

BACKGROUND: Patients with mild degenerative cervical myelopathy (DCM) represent a heterogeneous population, and indications for surgical decompression remain controversial. OBJECTIVE: To dissociate patient phenotypes within the broader population of mild DCM associated with degree of impairment in baseline quality of life (QOL) and surgical outcomes. METHODS: This was a post hoc analysis of patients with mild DCM (modified Japanese Orthopedic Association [mJOA] 15-17) enrolled in the AOSpine CSM-NA/CSM-I studies. A k-means clustering algorithm was applied to baseline QOL (Short Form-36 [SF-36]) scores to separate patients into 2 clusters. Baseline variables and surgical outcomes (change in SF-36 scores at 1 yr) were compared between clusters. A k-nearest neighbors (kNN) algorithm was used to evaluate the ability to classify patients into the 2 clusters by significant baseline clinical variables. RESULTS: One hundred eighty-five patients were eligible. Two groups were generated by k-means clustering. Cluster 1 had a greater proportion of females (44% vs 28%, P = .029) and symptoms of neck pain (32% vs 11%, P = .001), gait difficulty (57% vs 40%, P = .025), or weakness (75% vs 59%, P = .041). Although baseline mJOA correlated with neither baseline QOL nor outcomes, cluster 1 was associated with significantly greater improvement in disability (P = .003) and QOL (P < .001) scores following surgery. A kNN algorithm could predict cluster classification with 71% accuracy by neck pain, motor symptoms, and gender alone. CONCLUSION: We have dissociated a distinct patient phenotype of mild DCM, characterized by neck pain, motor symptoms, and female gender associated with greater impairment in QOL and greater response to surgery.

Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy.

Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.

Patient phenotypes associated with outcome following surgery for mild degenerative cervical myelopathy: a principal component regression analysis.

BACKGROUND CONTEXT: Predictors of outcome after surgery for degenerative cervical myelopathy (DCM) have been determined previously through hypothesis-driven multivariate statistical models that rely on a priori knowledge of potential confounders, exclude potentially important variables because of restrictions in model building, cannot include highly collinear variables in the same model, and ignore intrinsic correlations among variables. PURPOSE: The present study aimed to apply a data-driven approach to identify patient phenotypes that may predict outcomes after surgery for mild DCM. STUDY DESIGN: This is a principal component analysis of data from two related prospective, multicenter cohort studies. PATIENT SAMPLE: The study included patients with mild DCM, defined by a modified Japanese Orthopaedic Association score of 15-17, undergoing surgical decompression as part of the AOSpine CSM-NA or CSM-I trials. OUTCOME MEASURES: Patient outcomes were evaluated preoperatively at baseline and at 6 months, 1 year, and 2 years after surgery. Quality of life (QOL) was evaluated by the Neck Disability Index (NDI) and Short Form-36 version 2 (SF-36v2). These are both patient self-reported measures that evaluate health-related QOL, with NDI being specific to neck conditions and SF-36v2 being a generic instrument. MATERIALS AND METHODS: The analysis included 154 patients. A heterogeneous correlation matrix was created using a combination of Pearson, polyserial, and polychoric regressions among 67 variables, which then underwent eigen decomposition. Scores of significant principal components (PCs) (with eigenvalues>1) were included in multivariate logistic regression analyses for three dichotomous outcomes of interest: achievement of the minimum clinically important difference [MCID] in (1) NDI (≤-7.5), (2) SF-36v2 Physical Component Summary (PCS) score (≥5), and (3) SF-36v2 Mental Component Summary (MCS) score (≥5). RESULTS: Twenty-four significant PCs accounting for 75% of the variance in the data were identified. Two PCs were associated with achievement of the MCID in NDI. The first (PC 1) was dominated by variables related to surgical approach and number of operated levels; the second (PC 21) consisted of variables related to patient demographics, severity and etiology of DCM, comorbid status, and surgical approach. Both PC 1 and PC 21 also correlated with SF-36v2 PCS score, in addition to PC 4, which described patients' physical profile, including gender, height, and weight, as well as comorbid renal disease; PC 6, which received large loadings from variables related to cardiac disease, impaired mobility, and length of surgery and recovery; and PC 9, which harbored large contributions from features of upper limb dysfunction, cardiorespiratory disease, surgical approach, and region. In addition to PC 21, a component profiling patients' socioeconomic status and support systems and degree of physical disability (PC 24) was associated with achievement of the MCID in SF-36 MCS score. CONCLUSIONS: Through a data-driven approach, we identified several phenotypes associated with disability and physical and mental health-related QOL. Such data reduction methods may separate patient-, disease-, and treatment-related variables more accurately into clinically meaningful phenotypes that may inform patient care and recruitment into clinical trials.

Risk factors for surgical site infection after intracranial electroencephalography monitoring for epilepsy in the pediatric population.

OBJECTIVE Intracranial electroencephalography (iEEG) monitoring is an important method of identifying the seizure focus in patients with medically refractory epilepsy. While previous studies have demonstrated low rates of surgical complications, reported rates of surgical site infection (SSI) are highly variable. To date, no studies have specifically evaluated the patient or operative risk factors contributing to SSI. The goals of this study were to examine the rate of SSI after iEEG monitoring for epilepsy workup in pediatric patients and to determine the variables that might contribute to the development of SSI. METHODS A retrospective analysis of hospital charts at the Hospital for Sick Children was performed for all patients who had undergone iEEG monitoring between 2000 and 2016. Univariate and multivariate analyses were performed to look for statistically significant variables in relation to SSI. RESULTS Among 199 patients eligible for analysis, 8 (4.0%) developed SSIs within a period ranging from 21 to 51 days postoperatively. Univariate analysis yielded 4 factors related to SSI: number of people present in the operating room on electrode insertion (p = 0.02), length of insertion surgery (p = 0.04), previous operation at the same surgical site (p = 0.04), and number of depth electrodes inserted (p = 0.01). Multivariate analysis revealed that both the number of people present during the implant operation (OR 0.08, 95% CI 0.01-0.70) and the number of depth electrodes inserted (OR 3.52, 95% CI 1.44-8.59) independently contributed to SSI. CONCLUSIONS This is the largest case series and the first comprehensive review of both patient and operative risk factors in the development of SSI from iEEG monitoring in a pediatric population. The authors' institution had a lower rate of infection than those in most other studies, which could be explained by their protocol of administering intravenous antibiotics perioperatively and post-implant removal antibiotics for 14 days. The authors found a correlation between SSI and the number of people present during the implant operation, as well as the number of depth electrodes; both may contribute to breaks in sterility.

Explanatory Versus Pragmatic Trials: An Essential Concept in Study Design and Interpretation.

Randomized clinical trials often represent the highest level of clinical evidence available to evaluate the efficacy of an intervention in clinical medicine. Although the process of randomization serves to maximize internal validity, the external validity, or generalizability, of such studies depends on several factors determined at the design phase of the trial including eligibility criteria, study setting, and outcomes of interest. In general, explanatory trials are optimized to demonstrate the efficacy of an intervention in a highly selected patient group; however, findings from these studies may not be generalizable to the larger clinical problem. In contrast, pragmatic trials attempt to understand the real-world benefit of an intervention by incorporating design elements that allow for greater generalizability and clinical applicability of study results. In this article we describe the explanatory-pragmatic continuum for clinical trials in greater detail. Further, a well-accepted tool for grading trials on this continuum is described, and applied, to 2 recently published trials pertaining to the surgical management of lumbar degenerative spondylolisthesis.

Evolution of blood-brain-barrier permeability after acute ischemic stroke.

The dynamics of BBB permeability after AIS in humans are not well understood. In the present study we measured the evolution of BBB permeability after AIS in humans using MRI. Patients presenting to our institution with a diagnosis of AIS underwent a single dynamic contrast-enhanced MRI (DCE-MRI) sequence to measure BBB permeability during their initial workup. Forty-two patients were included in the final analysis. The patient sample underwent DCE-MRI at a mean time of 23.8hrs after the onset of AIS symptoms (range: 1.3-90.7hrs). At all time-points the BBB permeability within the infarct region of the brain as defined on DWI/ADC was higher compared to the homologous region of the contralateral hemisphere (p<0.005). BBB permeability, expressed as a ratio of infarct permeability to contralateral permeability, was greatest at 6-48hrs after the onset of AIS. Although the data was not acquired longitudinally, these findings suggest that the permeability of the BBB is continually elevated following AIS, which contradicts previous assertions that BBB permeability after AIS follows a biphasic course. Knowledge of BBB dynamics following AIS may provide insight into future treatments for AIS, especially BBB stabilizing agents.

Longitudinal assessment of imatinib's effect on the blood-brain barrier after ischemia/reperfusion injury with permeability MRI.

Acute ischemic stroke (AIS) often results in degeneration of the blood-brain barrier (BBB), which can lead to vasogenic edema and an increased risk of intracerebral hemorrhage. Imatinib is an agent that may be able to protect the BBB and reduce the risk of the harmful consequences of BBB degeneration. We sought to measure the effect of Imatinib on the BBB after experimental stroke longitudinally in vivo with permeability dynamic contrast-enhanced MRI. Ischemia/reperfusion injury was induced with a transient middle cerebral artery occlusion surgery. Rats were given Imatinib at 2 and 20 h after stroke onset. Post-assessment included neurologic functioning, MR imaging, Evans Blue extravasation, Western blot, and immunohistology assay. Imatinib protected the BBB by 24 h but failed to decrease BBB permeability at an earlier time-point. Imatinib also reduced infarct volume, edema, and improved neurologic functioning by 24 h. Rats treated with Imatinib also had a higher expression of the BBB structural protein Zona ocludens-1 and a reduction in nuclear factor-kappa beta (NF-κß) activation. Imatinib is a promising agent to protect the BBB after AIS, but its effect on the BBB may not become prominent until 24 h after the onset of ischemia. This finding may help elucidate Imatinib's role in the clinical management of AIS and influence future study designs.

Neuroproteome changes after ischemia/reperfusion injury and tissue plasminogen activator administration in rats: a quantitative iTRAQ proteomics study.

The thrombolytic, recombinant tissue plasminogen activator (rt-PA) is the only approved therapy for acute ischemic stroke (AIS). When administered after AIS, rt-PA has many adverse pleiotropic actions, which are currently poorly understood. The identification of proteins showing differential expression after rt-PA administration may provide insight into these pleiotropic actions. In this study we used a 2D-LC MS/MS iTRAQ proteomic analysis, western blotting, and pathway analysis to analyze changes in protein expression 24-hours after rt-PA administration in the cortical brain tissue of 36 rats that underwent a sham or transient middle cerebral artery occlusion surgery. After rt-PA administration we reported alterations in the expressions of 18 proteins, many of which were involved in excitatory neurotransmitter function or cytoskeletal structure. The expression changes of GAD2 and EAAT1 were validated with western blot. The interactions between the identified proteins were analyzed with the IPA pathway analysis tool and three proteins: DPYSL2, RTN4, and the NF-kB complex, were found to have characteristics of being key proteins in the network. The differential protein expressions we observed may reflect pleiotropic actions of rt-PA after experimental stroke, and shine light on the mechanisms of rt-PA's adverse effects. This may have important implications in clinical settings where thrombolytic therapy is used to treat AIS.