Admitting Hospital Influences on Withdrawal of Life-Sustaining Treatment Decision for Patients With Severe Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Withdrawal of life-sustaining treatment (WLST) in severe traumatic brain injury (TBI) is complex, with a paucity of standardized guidelines. We aimed to assess the variability in WLST practices between trauma centers in North America. METHODS: This retrospective study used data from trauma centers through the American College of Surgeons Trauma Quality Improvement Program between 2017 and 2020. We included adult patients (>16 years) with severe TBI and a documented decision for WLST. We constructed a series of hierarchical logistic regression models to adjust for patient, injury, and hospital attributes influencing WLST; residual between-center variability was characterized using the median odds ratio. The impact of disparate WLST practices was further assessed by ranking centers by their conditional random intercept and assessing mortality, length of stay, and WLST between quartiles. RESULTS: We identified a total of 85 511 subjects with severe TBI treated across 510 trauma centers, of whom 20 300 (24%) had WLST. Patient-level factors associated with increased likelihood of WLST were advanced age, White race, self-pay, or Medicare insurance status (compared with private insurance). Black race was associated with reduced tendency for WLST. Treatment in nonprofit centers and higher-severity intracranial and extracranial injuries, midline shift, and pupil asymmetry also increased the likelihood for WLST. After adjustment for patient and hospital attributes, the median odds ratio was 1.45 (1.41-1.49 95% CI), suggesting residual variation in WLST between centers. When centers were grouped into quartiles by their propensity for WLST, there was increased adjusted mortality and shorter length of stay in fourth compared with first quartile centers. CONCLUSION: We highlighted the presence of contextual phenomena associated with disparate WLST practice patterns between trauma centers after adjustment for case-mix and hospital attributes. These findings highlight a need for standardized WLST guidelines to improve equity of care provision for patients with severe TBI.

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.

Inverted stent deployment technique for stent assisted coiling of wide-necked posterior communicating artery aneurysm: Technical report.

BACKGROUND: Wide-necked aneurysms of the posterior communicating artery associated with a fetal posterior cerebral artery that arises from the neck or dome of the aneurysm is a clinical scenario that poses a challenge to endovascular methods. CASE DESCRIPTION: A patient presented with spontaneous subarachnoid hemorrhage and was found to have a 4 × 3 × 3 mm posterior communicating artery aneurysm. Of note the aneurysm projected superiorly and a fetal posterior communicating artery aneurysm arose from the neck of the aneurysm. After multidisciplinary discussion a treatment strategy of endovascular stent-assisted coiling was chosen. Given the unique morphology of the aneurysm a novel stent-inversion maneuver was used, in which the stent was deployed from the posterior communicating artery into the distal supra-clinoid internal carotid artery. The patient tolerated the procedure well and complete aneurysm occlusion was achieved. CONCLUSIONS: This case demonstrates a novel stent inversion technique that can be used for stent-assisted coiling of certain challenging aneurysms.

Gender-based differences in physician payments within the fee-for-service system in Ontario: a retrospective, cross-sectional study.

BACKGROUND: Differences in physician income by gender have been described in numerous jurisdictions, but few studies have looked at a Canadian cohort with adjustment for confounders. In this study, we aimed to understand differences in fee-for-service payments to men and women physicians in Ontario. METHODS: We conducted a cross-sectional analysis of all Ontario physicians who submitted claims to the Ontario Health Insurance Plan (OHIP) in 2017. For each physician, we gathered demographic information from the College of Physicians and Surgeons of Ontario registry. We compared differences in physician claims between men and women in the entire cohort and within each specialty using multivariable linear regressions, controlling for length of practice, specialty and practice location. RESULTS: We identified a cohort of 30 167 physicians who submitted claims to OHIP in 2017, including 17 992 men and 12 175 women. When controlling for confounding variables in a linear mixed-effects regression model, annual physician claims were $93 930 (95% confidence interval $88 434 to $99 431) higher for men than for women. Women claimed 74% as much as men when adjusting for covariates. This discrepancy was present in nearly all specialty categories. Men claimed more than women throughout their careers, with the greatest gap 10-15 years into practice. INTERPRETATION: We found a gender gap in fee-for-service claims in Ontario, with women claiming less than men overall and in nearly every specialty. Further work is required to understand the root causes of the gender pay gap.

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.

Emergent and Urgent Transfers to Neurosurgical Centers in Ontario.

Critically ill neurosurgical patients require expedient access to neurosurgical centers (NC) to improve outcome. In regionalized health systems patients are often initially evaluated at a non-neurosurgical center (NNC) and are subsequently transferred to a NC using air or ground vehicles. We sought to identify barriers to accessing a NC for critically ill patients by analyzing interfacility transfer times and referral patterns in the province of Ontario. A retrospective observational analysis was undertaken. The cohort included patients in Ontario with emergent and urgent neurologic pathologies who underwent transfer from a NNC to NC between January 1, 2011 and December 31, 2013. Timing, clinical, and geographic data were collected for each transfer. We identified 1103 emergent/urgent transfers. The median transfer time to a NC was 3.4 h (IQR -2.2, 3.8) and varied by the geographic region of origin. A total of 17% of the patients bypassed a closer NC during transfer to their destination NC. Transfers that bypassed a closer NC travelled further (101 miles vs. 296 miles, p < 0.001), took longer (3.1 h vs. 3.9 h, p < 0.001), and in some regions were associated with a higher risk of in-transit clinical decline (3.0% vs. 8.3%, p < 0.05) when compared with transfers that ended at the closest NC. Regionalization of neurosurgical services in Ontario has led to heavy reliance upon patient transfers to maintain continuity of care. Access to a NC varied across the province, which may represent regional differences in neurosurgical bed availability, resource limitations at smaller NCs, or environmental factors. Our descriptions of referral patterns and transport times can guide health system planning in Ontario and similar jurisdictions in the United States and Canada.

Dynamic contrast-enhanced MRI and CT provide comparable measurement of blood-brain barrier permeability in a rodent stroke model.

In the current management of acute ischemic stroke (AIS), clinical criteria are used to estimate the risk of hemorrhagic transformation (HT), which is a devastating early complication. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and computed tomography (DCE-CT) may serve as physiologically-based decision making tools to more reliably assess the risk of HT. Before these tools can be properly validated, the comparability of the blood-brain barrier (BBB) permeability measurements they generate should be assessed. Sixteen rats were subjected to a transient middle cerebral artery occlusion before successively undergoing DCE-CT and DCE-MRI at 24-hours. BBB permeability (K(trans)) values were generated from both modalities. A correlation of R=0.677 was found (p<0.01) and the resulting relationship was [DCE-CT=(0.610*DCE-MRI)+4.140]. A variance components analysis found the intra-rat coefficient of variation to be 0.384 and 0.258 for K(trans) values from DCE-MRI and DCE-CT respectively. Permeability measures from DCE-CT were 22% higher than those from DCE-MRI. The results of this study demonstrate for the first time comparability between DCE-CT and DCE-MRI in the assessment of AIS. These results may provide a foundation for future clinical trials making combined use of these modalities.

Development of a Skeletal Muscle Mimic Phantom Compatible with QCT and MR Imaging.

OBJECTIVE: The purpose of this study was to develop a skeletal muscle mimic phantom compatible with quantitative computed tomography (QCT) and magnetic resonance imaging, yielding physiologically appropriate values. METHODS: Agar-based phantoms contained varying concentrations of CuCl2 and EDTA to adjust T2 relaxation time and phantom density concurrently. T2 relaxation times were quantified using a 4-mm single-slice fast spin echo sequence repeated for six serial echo times at 937-µm resolution. T2 relaxation maps were generated using the Levenberg-Marquardt equation. A peripheral QCT scanner measured linear attenuation coefficients of phantoms, which were converted to density (mg/cm3) values. Five 2.3 ± 0.5 mm thick slices were acquired at 15 mm/s scan speed and 500-µm resolution. Logarithmic or linear regression models were fitted to EDTA or CuCl2 versus density and T2 relaxation data. RESULTS: Density (D) was linearly dependent on CuCl2 (D = 0.27 [CuCl2] + 63.92, R2 = 0.84, P = 0.01) and invariant to EDTA. T2 relaxation time was related negatively to CuCl2 (T2 = -10.13 ln [CuCl2] + 66.70, R2 = 0.91, P < .01) and positively to EDTA (T2 = 5.72 ln [EDTA] + 54.47, R2 = 0.86, P < .01). Reproducibility within and between phantoms of the same compositions was acceptable (<5% error). Long-term stability was achieved for density but poorer for T2 relaxation time. CONCLUSIONS: This phantom optimization method provides a means for altering a soft tissue phantom suited for calibrating magnetic resonance imaging and QCT signals within values representative of muscle. Phantoms can be used during scans for calibrating magnetic resonance signals between and within individuals over time and can cross-calibrate different scanners.

A Trimodality Comparison of Volumetric Bone Imaging Technologies. Part III: SD, SEE, LSC Association With Fragility Fractures.

Part II of this 3-part series demonstrated 1-yr precision, standard error of the estimate, and 1-yr least significant change for volumetric bone outcomes determined using peripheral (p) quantitative computed tomography (QCT) and peripheral magnetic resonance imaging (pMRI) modalities in vivo. However, no clinically relevant outcomes have been linked to these measures of change. This study examined 97 women with mean age of 75 ± 9 yr and body mass index of 26.84 ± 4.77 kg/m(2), demonstrating a lack of association between fragility fractures and standard deviation, least significant change and standard error of the estimate-based unit differences in volumetric bone outcomes derived from both pMRI and pQCT. Only cortical volumetric bone mineral density and cortical thickness derived from high-resolution pQCT images were associated with an increased odds for fractures. The same measures obtained by pQCT erred toward significance. Despite the smaller 1-yr and short-term precision error for measures at the tibia vs the radius, the associations with fractures observed at the radius were larger than at the tibia for high-resolution pQCT. Unit differences in cortical thickness and cortical volumetric bone mineral density able to yield a 50% increase in odds for fractures were quantified here and suggested as a reference for future power computations.