Trial Readiness of Cavernous Malformations With Symptomatic Hemorrhage, Part I: Event Rates and Clinical Outcome.

BACKGROUND: Cerebral cavernous malformation with symptomatic hemorrhage (SH) are targets for novel therapies. A multisite trial-readiness project (https://www.clinicaltrials.gov; Unique identifier: NCT03652181) aimed to identify clinical, imaging, and functional changes in these patients. METHODS: We enrolled adult cerebral cavernous malformation patients from 5 high-volume centers with SH within the prior year and no planned surgery. In addition to clinical and imaging review, we assessed baseline, 1- and 2-year National Institutes of Health Stroke Scale, modified Rankin Scale, European Quality of Life 5D-3 L, and patient-reported outcome-measurement information system, Version 2.0. SH and asymptomatic change rates were adjudicated. Changes in functional scores were assessed as a marker for hemorrhage. RESULTS: One hundred twenty-three, 102, and 69 patients completed baseline, 1- and 2-year clinical assessments, respectively. There were 21 SH during 178.3 patient years of follow-up (11.8% per patient year). At baseline, 62.6% and 95.1% of patients had a modified Rankin Scale score of 1 and National Institutes of Health Stroke Scale score of 0 to 4, respectively, which improved to 75.4% (P=0.03) and 100% (P=0.06) at 2 years. At baseline, 74.8% had at least one abnormal patient-reported outcome-measurement information system, Version 2.0 domain compared with 61.2% at 2 years (P=0.004). The most common abnormal European Quality of Life 5D-3 L domains were pain (48.7%), anxiety (41.5%), and participation in usual activities (41.4%). Patients with prospective SH were more likely than those without SH to display functional decline in sleep, fatigue, and social function patient-reported outcome-measurement information system, Version 2.0 domains at 2 years. Other score changes did not differ significantly between groups at 2 years. The sensitivity of scores as an SH marker remained poor at the time interval assessed. CONCLUSIONS: We report SH rate, functional, and patient-reported outcomes in trial-eligible cerebral cavernous malformation with SH patients. Functional outcomes and patient-reported outcomes generally improved over 2 years. No score change was highly sensitive or specific for SH and could not be used as a primary end point in a trial.

Trial Readiness of Cavernous Malformations With Symptomatic Hemorrhage, Part II: Biomarkers and Trial Modeling.

BACKGROUND: Quantitative susceptibility mapping (QSM) and dynamic contrast-enhanced quantitative perfusion (DCEQP) magnetic resonance imaging sequences assessing iron deposition and vascular permeability were previously correlated with new hemorrhage in cerebral cavernous malformations. We assessed their prospective changes in a multisite trial-readiness project. METHODS: Patients with cavernous malformation and symptomatic hemorrhage (SH) in the prior year, without prior or planned lesion resection or irradiation were enrolled. Mean QSM and DCEQP of the SH lesion were acquired at baseline and at 1- and 2-year follow-ups. Sensitivity and specificity of biomarker changes were analyzed in relation to predefined criteria for recurrent SH or asymptomatic change. Sample size calculations for hypothesized therapeutic effects were conducted. RESULTS: We logged 143 QSM and 130 DCEQP paired annual assessments. Annual QSM change was greater in cases with SH than in cases without SH (P=0.019). Annual QSM increase by ≥6% occurred in 7 of 7 cases (100%) with recurrent SH and in 7 of 10 cases (70%) with asymptomatic change during the same epoch and 3.82× more frequently than clinical events. DCEQP change had lower sensitivity for SH and asymptomatic change than QSM change and greater variance. A trial with the smallest sample size would detect a 30% difference in QSM annual change during 2 years of follow-up in 34 or 42 subjects (1 and 2 tailed, respectively); power, 0.8, α=0.05. CONCLUSIONS: Assessment of QSM change is feasible and sensitive to recurrent bleeding in cavernous malformations. Evaluation of an intervention on QSM percent change may be used as a time-averaged difference between 2 arms using a repeated measures analysis. DCEQP change is associated with lesser sensitivity and higher variability than QSM. These results are the basis of an application for certification by the US Food and Drug Administration of QSM as a biomarker of drug effect on bleeding in cavernous malformations. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03652181.

Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis.

PURPOSE: In this study we gathered and analyzed the available evidence regarding 17 different imaging modalities and performed network meta-analysis to find the most effective modality for the differentiation between brain tumor recurrence and post-treatment radiation effects. METHODS: We conducted a comprehensive systematic search on PubMed and Embase. The quality of eligible studies was assessed using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument. For each meta-analysis, we recalculated the effect size, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio from the individual study data provided in the original meta-analysis using a random-effects model. Imaging technique comparisons were then assessed using NMA. Ranking was assessed using the multidimensional scaling approach and by visually assessing surface under the cumulative ranking curves. RESULTS: We identified 32 eligible studies. High confidence in the results was found in only one of them, with a substantial heterogeneity and small study effect in 21% and 9% of included meta-analysis respectively. Comparisons between MRS Cho/NAA, Cho/Cr, DWI, and DSC were most studied. Our analysis showed MRS (Cho/NAA) and 18F-DOPA PET displayed the highest sensitivity and negative likelihood ratios. 18-FET PET was ranked highest among the 17 studied techniques with statistical significance. APT MRI was the only non-nuclear imaging modality to rank higher than DSC, with statistical insignificance, however. CONCLUSION: The evidence regarding which imaging modality is best for the differentiation between radiation necrosis and post-treatment radiation effects is still inconclusive. Using NMA, our analysis ranked FET PET to be the best for such a task based on the available evidence. APT MRI showed promising results as a non-nuclear alternative.

Point-of-Care Bedside Brain Magnetic Resonance Imaging Is Safe in Extracorporeal Membrane Oxygenation Patients With Swan Ganz Catheters: A Phantom Experiment and Single Center Experience.

INTRODUCTION: More than 1.2 million pulmonary artery catheters (PACs) are used in cardiac patients per annum within the United States. However, it is contraindicated in traditional 1.5 and 3T magnetic resonance imaging (MRI) scans. We aimed to test preclinical and clinical safety of using this imaging modality given the potential utility of needing it in the clinical setting. METHODS: We conducted two phantom experiments to ensure that the electromagnetic field power deposition associated with bare and jacketed PACs was safe and within the acceptable limit established by the Food and Drug Administration. The primary end points were the safety and feasibility of performing Point-of-Care (POC) MRI without imaging-related adverse events. We performed a preclinical computational electromagnetic simulation and evaluated these findings in nine patients with PACs on veno-arterial extracorporeal membrane oxygenation. RESULTS: The phantom experiments showed that the baseline point specific absorption rate through the head averaged 0.4 W/kg. In both the bare and jacketed catheters, the highest net specific absorption rates were at the neck entry point and tip but were negligible and unlikely to cause any heat-related tissue or catheter damage. In nine patients (median age 66, interquartile range 42-72 y) with veno-arterial extracorporeal membrane oxygenation due to cardiogenic shock and PACs placed for close hemodynamic monitoring, POC MRI was safe and feasible with good diagnostic imaging quality. CONCLUSIONS: Adult ECMO patients with PACs can safely undergo point-of-care low-field (64 mT) brain MRI within a reasonable timeframe in an intensive care unit setting to assess for acute brain injury that might otherwise be missed with conventional head computed tomography.

Staying Virtual: A Survey Study of the Virtual Lecture Experience in Academic Medicine.

BACKGROUND: Increasing clinical demands can adversely impact academic advancement, including the ability to deliver lectures and disseminate scholarly work. The virtual lecture platform became mainstream during the height of the coronavirus-19 pandemic. Lessons learned from this period may offer insight into supporting academic productivity among physicians who must balance multiple demands, including high clinical workloads and family care responsibilities. We evaluated perceptions on delivering virtual lectures to determine whether virtual venues merit continuation beyond the pandemic's initial phase and whether these perceptions differ by gender and rank. METHODS: In a survey study, faculty who spoke in 1 of 3 virtual lecture programs in the Departments of Anesthesiology and Critical Care Medicine, Otolaryngology, and Radiology at a university hospital in 2020 to 2022 were queried about their experience. Speakers' motivations to lecture virtually and the perceived advantages and disadvantages of virtual and in-person lectures were analyzed using descriptive statistics and qualitative analyses. RESULTS: Seventy-two of 95 (76%) faculty members responded (40% women, 38% men, and 22% gender undisclosed). Virtual lectures supported the speakers "a lot" to "extremely" with the following goals: enhancing one's reputation and credibility (76%), networking (70%), receiving feedback (63%), and advancing prospects for promotion (59%). Virtual programs also increased the speakers' sense of accomplishment (70%) and professional optimism (61%) by at least "a lot," including instructors and assistant professors who previously had difficulty obtaining invitations to speak outside their institution. Many respondents had declined prior invitations to speak in-person due to clinical workload (66%) and family care responsibilities (58%). Previous opportunities to lecture in-person were also refused due to finances (39%), teaching (26%), and research (19%) requirements, personal medical conditions or disabilities (9%), and religious obligations (5%). Promotion was a stronger motivating factor to lecture virtually for instructors and assistant professors than for associate and full professors. By contrast, disseminating work and ideas was a stronger motivator for associate and full professors. Associate and full professors also reported greater improvement in work-related well-being than earlier career faculty from the virtual lecture experience. Very few differences were found by gender. CONCLUSIONS: Virtual lecture programs support faculty who might not otherwise have the opportunity to lecture in-person due to multiple constraints. To increase the dissemination of scholarly work and expand opportunities to all faculty, virtual lectures should continue even as in-person venues are reestablished.

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

BACKGROUND: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG. PURPOSE: This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images. STUDY TYPE: Retrospective and prospective. POPULATION: For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males). FIELD STRENGTH/SEQUENCE: 5T and 3T, T2-weighted turbo spin echo imaging. ASSESSMENT: Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve. STATISTICAL TESTS: Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant. RESULTS: In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%. DATA CONCLUSION: In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making. EVIDENCE LEVEL: 2 Technical Efficacy: Stage 2.

The S-Factor, a New Measure of Disease Severity in Spinocerebellar Ataxia: Findings and Implications.

Spinocerebellar ataxias (SCAs) are progressive neurodegenerative disorders, but there is no metric that predicts disease severity over time. We hypothesized that by developing a new metric, the Severity Factor (S-Factor) using immutable disease parameters, it would be possible to capture disease severity independent of clinical rating scales. Extracting data from the CRC-SCA and READISCA natural history studies, we calculated the S-Factor for 438 participants with symptomatic SCA1, SCA2, SCA3, or SCA6, as follows: ((length of CAG repeat expansion - maximum normal repeat length) /maximum normal repeat length) × (current age - age at disease onset) × 10). Within each SCA type, the S-Factor at the first Scale for the Assessment and Rating of Ataxia (SARA) visit (baseline) was correlated against scores on SARA and other motor and cognitive assessments. In 281 participants with longitudinal data, the slope of the S-Factor over time was correlated against slopes of scores on SARA and other motor rating scales. At baseline, the S-Factor showed moderate-to-strong correlations with SARA and other motor rating scales at the group level, but not with cognitive performance. Longitudinally the S-Factor slope showed no consistent association with the slope of performance on motor scales. Approximately 30% of SARA slopes reflected a trend of non-progression in motor symptoms. The S-Factor is an observer-independent metric of disease burden in SCAs. It may be useful at the group level to compare cohorts at baseline in clinical studies. Derivation and examination of the S-factor highlighted challenges in the use of clinical rating scales in this population.

Ablation of apparent diffusion coefficient hyperintensity clusters in mesial temporal lobe epilepsy improves seizure outcomes after laser interstitial thermal therapy.

OBJECTIVE: Laser interstitial thermal therapy (LiTT) is a minimally invasive surgical procedure for intractable mesial temporal epilepsy (mTLE). LiTT is safe and effective, but seizure outcomes are highly variable due to patient variability, suboptimal targeting, and incomplete ablation of the epileptogenic zone. Apparent diffusion coefficient (ADC) is a magnetic resonance imaging (MRI) sequence that can identify potential epileptogenic foci in the mesial temporal lobe to improve ablation and seizure outcomes. The objective of this study was to investigate whether ablation of tissue clusters with high ADC values in the mesial temporal structures is associated with seizure outcome in mTLE after LiTT. METHODS: Twenty-seven patients with mTLE who underwent LiTT at our institution were analyzed. One-year seizure outcome was categorized as complete seizure freedom (International League Against Epilepsy [ILAE] Class I) and residual seizures (ILAE Class II-VI). Volumes of hippocampus and amygdala were segmented from the preoperative T1 MRI sequence. Spatially distinct hyperintensity clusters were identified in the preoperative ADC map. Proportion of cluster volume and number ablated were associated with seizure outcomes. RESULTS: The mean age at surgery was 37.5 years and the mean follow-up duration was 1.9 years. Proportions of hippocampal cluster volume (p = .013) and number (p = .03) ablated were significantly higher in patients with seizure freedom. For amygdala clusters, the proportion of cluster number ablated was significantly associated with seizure outcome (p = .026). In the combined amygdalohippocampal complex, ablation of amygdalohippocampal clusters reliably predicted seizure outcome by their volume ablated (area under the curve [AUC] = 0.7670, p = .02). SIGNIFICANCE: Seizure outcome after LiTT in patients with mTLE was associated significantly with the extent of cluster ablation in the amygdalohippocampal complex. The results suggest that preoperative ADC analysis may help identify high-yield pathological tissue clusters that represent epileptogenic foci. ADC-based cluster analysis can potentially assist ablation targeting and improve seizure outcome after LiTT in mTLE.

The Cortico-Limbo-Thalamo-Cortical Circuits: An Update to the Original Papez Circuit of the Human Limbic System.

The Papez circuit, first proposed by James Papez in 1937, is a circuit believed to control memory and emotions, composed of the cingulate cortex, entorhinal cortex, parahippocampal gyrus, hippocampus, hypothalamus, and thalamus. Pursuant to James Papez, Paul Yakovlev and Paul MacLean incorporated the prefrontal/orbitofrontal cortex, septum, amygdalae, and anterior temporal lobes into the limbic system. Over the past few years, diffusion-weighted tractography techniques revealed additional limbic fiber connectivity, which incorporates multiple circuits to the already known complex limbic network. In the current review, we aimed to comprehensively summarize the anatomy of the limbic system and elaborate on the anatomical connectivity of the limbic circuits based on the published literature as an update to the original Papez circuit.

Brain Tumor Segmentation for Multi-Modal MRI with Missing Information.

Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. However, developing a network that maintains clinical relevance in situations where certain MRI sequence(s) might be unavailable or unusual poses a significant challenge. While one solution is to train multiple models with different MRI sequence combinations, it is impractical to train every model from all possible sequence combinations. In this paper, we propose a DCNN-based brain tumor segmentation framework incorporating a novel sequence dropout technique in which networks are trained to be robust to missing MRI sequences while employing all other available sequences. Experiments were performed on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. When all MRI sequences were available, there were no significant differences in performance of the model with and without dropout for enhanced tumor (ET), tumor (TC), and whole tumor (WT) (p-values 1.000, 1.000, 0.799, respectively), demonstrating that the addition of dropout improves robustness without hindering overall performance. When key sequences were unavailable, the network with sequence dropout performed significantly better. For example, when tested on only T1, T2, and FLAIR sequences together, DSC for ET, TC, and WT increased from 0.143 to 0.486, 0.431 to 0.680, and 0.854 to 0.901, respectively. Sequence dropout represents a relatively simple yet effective approach for brain tumor segmentation with missing MRI sequences.

Prevalence and Correlates of Depressive Symptoms Within 6 Months After First-Time Mild Traumatic Brain Injury.

OBJECTIVE: Depressive symptoms are among the most common neuropsychiatric sequelae of mild traumatic brain injury (mTBI). Very few studies have compared correlates of depressive symptoms within the first 6 months of injury in cohorts experiencing their first TBI. The authors investigated whether the correlates of depressive symptoms (being female, older, lower education, having brain lesions, experiencing worse postconcussive symptoms, and incomplete functional recovery) that have been established in populations with moderate to severe TBI were the same for individuals with first-time mTBI within the first 6 months of recovery. METHODS: Two hundred seventeen individuals with first-time mTBI were divided into subgroups-new-onset depressive symptoms, recurrent depressive symptoms, prior depression history only, and never depressed-and compared on clinical and demographic variables and the presence of postconcussive symptoms and functional recovery at 3 and 6 months. RESULTS: New-onset depressive symptoms developed in 12% of the cohort, whereas 11% of the cohort had recurrent depressive symptoms. Both depressive symptoms groups were more likely to comprise women and persons of color and were at higher risk for clinically significant postconcussive symptoms and incomplete functional recovery for the first 6 months postinjury. CONCLUSIONS: Presence of depressive symptoms after first-time mTBI was associated with persistent postconcussive symptoms and incomplete functional recovery in the first 6 months. Adding to the existing literature, these findings identified correlates of depressive symptom development and poor outcomes after mTBI, thus providing further evidence that mTBI may produce persistent symptoms and functional limitations that warrant clinical attention.

Assessing the SAfety and FEasibility of bedside portable low-field brain Magnetic Resonance Imaging in patients on ECMO (SAFE-MRI ECMO study): study protocol and first case series experience.

BACKGROUND: To assess the safety and feasibility of imaging of the brain with a point-of-care (POC) magnetic resonance imaging (MRI) system in patients on extracorporeal membrane oxygenation (ECMO). Early detection of acute brain injury (ABI) is critical in improving survival for patients with ECMO support. METHODS: Patients from a single tertiary academic ECMO center who underwent head CT (HCT), followed by POC brain MRI examinations within 24 h following HCT while on ECMO. Primary outcomes were safety and feasibility, defined as completion of MRI examination without serious adverse events (SAEs). Secondary outcome was the quality of MR images in assessing ABIs. RESULTS: We report 3 consecutive adult patients (median age 47 years; 67% male) with veno-arterial (n = 1) and veno-venous ECMO (n = 2) (VA- and VV-ECMO) support. All patients were imaged successfully without SAEs. Times to complete POC brain MRI examinations were 34, 40, and 43 min. Two patients had ECMO suction events, resolved with fluid and repositioning. Two patients were found to have an unsuspected acute stroke, well visualized with MRI. CONCLUSIONS: Adult patients with VA- or VV-ECMO support can be safely imaged with low-field POC brain MRI in the intensive care unit, allowing for the assessment of presence and timing of ABI.

Your mileage may vary: impact of data input method for a deep learning bone age app's predictions.

OBJECTIVE: The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods. METHODS: The 16Bit Bone Age app is a browser-based deep learning application for predicting bone age on pediatric hand radiographs; recommended data input methods are direct image file upload or smartphone-capture of image. We collected 50 hand radiographs, split equally among 5 bone age groups. Three observers used the 16Bit Bone Age app to assess these images using 3 different data input methods: (1) direct image upload, (2) smartphone photo of image in radiology reading room, and (3) smartphone photo of image in a clinic. RESULTS: Interobserver agreement was excellent for direct upload (ICC = 1.00) and for photos in reading room (ICC = 0.96) and good for photos in clinic (ICC = 0.82), respectively. Intraobserver agreement for the entire test set across the 3 data input methods was variable with ICCs of 0.95, 0.96, and 0.57 for the 3 observers, respectively. DISCUSSION: Our findings indicate that different data input methods can result in discordant bone age predictions from the 16Bit Bone Age app. Further study is needed to determine the impact of data input methods, such as smartphone image capture, on deep learning app performance and accuracy.

Can AI distinguish a bone radiograph from photos of flowers or cars? Evaluation of bone age deep learning model on inappropriate data inputs.

OBJECTIVE: To evaluate the behavior of a publicly available deep convolutional neural network (DCNN) bone age algorithm when presented with inappropriate data inputs in both radiological and non-radiological domains. METHODS: We evaluated a publicly available DCNN-based bone age application. The DCNN was trained on 12,612 pediatric hand radiographs and won the 2017 RSNA Pediatric Bone Age Challenge (concordance of 0.991 with radiologist ground-truth). We used the application to analyze 50 left-hand radiographs (appropriate data inputs) and seven classes of inappropriate data inputs in radiological (i.e., chest radiographs) and non-radiological (i.e., image of street numbers) domains. For each image, we noted if (1) the application distinguished between appropriate and inappropriate data inputs and (2) inference time per image. Mean inference times were compared using ANOVA. RESULTS: The 16Bit Bone Age application calculated bone age for all pediatric hand radiographs with mean inference time of 1.1 s. The application did not distinguish between pediatric hand radiographs and inappropriate image types, including radiological and non-radiological domains. The application inappropriately calculated bone age for all inappropriate image types, with mean inference time of 1.1 s for all categories (p = 1). CONCLUSION: A publicly available DCNN-based bone age application failed to distinguish between appropriate and inappropriate data inputs and calculated bone age for inappropriate images. The awareness of inappropriate outputs based on inappropriate DCNN input is important if tasks such as bone age determination are automated, emphasizing the need for appropriate oversight at the data input and verification stage to avoid unrecognized erroneous results.

Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm.

OBJECTIVE: Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans. METHODS: All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity. RESULTS: A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively. CONCLUSIONS: In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.

Leveraging artificial intelligence in ischemic stroke imaging.

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.

Can Preoperative Mapping with Functional MRI Reduce Morbidity in Brain Tumor Resection? A Systematic Review and Meta-Analysis of 68 Observational Studies.

Background Preoperative functional MRI (fMRI) is one of several techniques developed to localize critical brain structures and brain tumors. However, the usefulness of fMRI for preoperative surgical planning and its potential effect on neurologic outcomes remain unclear. Purpose To assess the overall postoperative morbidity among patients with brain tumors by using preoperative fMRI versus surgery without this tool or with use of standard (nonfunctional) neuronavigation. Materials and Methods A systematic review and meta-analysis of studies across major databases from 1946 to June 20, 2020, were conducted. Inclusion criteria were original studies that (a) included patients with brain tumors, (b) performed preoperative neuroimaging workup with fMRI, (c) investigated the usefulness of a preoperative or intraoperative functional neuroimaging technique and used that technique to resect cerebral tumors, and (d) reported postoperative clinical measures. Pooled estimates for adverse event rate (ER) effect size (log ER, log odds ratio, or Hedges g) with 95% CIs were computed by using a random-effects model. Results Sixty-eight studies met eligibility criteria (3280 participants; 58.9% men [1555 of 2641]; mean age, 46 years ± 8 [standard deviation]). Functional deterioration after surgical procedure was less likely to occur when fMRI mapping was performed before the operation (odds ratio, 0.25; 95% CI: 0.12, 0.53; P < .001]), and postsurgical Karnofsky performance status scores were higher in patients who underwent fMRI mapping (Hedges g, 0.66; 95% CI: 0.21, 1.11; P = .004]). Craniotomies for tumor resection performed with preoperative fMRI were associated with a pooled adverse ER of 11% (95% CI: 8.4, 13.1), compared with a 21.0% ER (95% CI: 12.2, 33.5) in patients who did not undergo fMRI mapping. Conclusion From the currently available data, the benefit of preoperative functional MRI planning for the resection of brain tumors appears to reduce postsurgical morbidity, especially when used with other advanced imaging techniques, such as diffusion-tensor imaging, intraoperative MRI, or cortical stimulation. © RSNA, 2021 Online supplemental material is available for this article.

DECT in Detection of Vertebral Fracture-associated Bone Marrow Edema: A Systematic Review and Meta-Analysis with Emphasis on Technical and Imaging Interpretation Parameters.

Background Dual-energy CT (DECT) shows promising performance in detecting bone marrow edema (BME) associated with vertebral body fractures. However, the optimal technical and image interpretation parameters are not well described. Purpose To conduct a systematic review and meta-analysis to determine the diagnostic performance of DECT in detecting BME associated with vertebral fractures (VFs), using different technical and image interpretation parameters, compared with MRI as the reference standard. Materials and Methods A systematic literature search was performed on July 9, 2020, to identify studies evaluating DECT performance for in vivo detection of vertebral BME. A random-effects model was used to derive estimates of the diagnostic accuracy parameters of DECT. The impact of relevant covariates in technical, image interpretation, and study design parameters on the diagnostic performance of DECT was investigated using subgroup analyses. Results Seventeen studies (with 742 of 2468 vertebrae with BME at MRI) met inclusion criteria. Pooled estimates of sensitivity, specificity, and area under the curve of DECT for vertebral body BME were 89% (95% CI: 84%, 92%), 96% (95% CI: 92%, 98%), and 96% (95% CI: 94%, 97%), respectively. Single-source consecutive scanning showed poor specificity (78%) compared with the dual-source technique (98%, P < .001). Specificity was higher using bone and soft-tissue kernels (98%) compared with using only soft-tissue kernels (90%, P = .001). Qualitative assessment had a better specificity (97%) versus quantitative assessment (90%) of DECT images (P = .01). Experienced readers showed considerably higher specificity (96%) compared with trainees (79%, P = .01). DECT sensitivity improved using a higher difference between low- and high-energy spectra (90% vs 83%, P = .04). Conclusion Given its high specificity, the detection of vertebral bone marrow edema with dual-energy CT (DECT) associated with vertebral fracture may obviate confirmatory MRI in an emergency setting. Technical parameters, such as the dual-source technique, both bone and soft-tissue kernels, and qualitative assessment by experienced readers, can ensure the high specificity of DECT. © RSNA, 2021 Online supplemental material is available for this article.

Identification of the Somatomotor Network from Language Task-based fMRI Compared with Resting-State fMRI in Patients with Brain Lesions.

Background Resting-state functional MRI (rs-fMRI) is a potential alternative to task-based functional MRI (tb-fMRI) for somatomotor network (SMN) identification. Brain networks can also be generated from tb-fMRI by using independent component analysis (ICA). Purpose To investigate whether the SMN can be identified by using ICA from a language task without a motor component, the sentence completion functional MRI (sc-fMRI) task, compared with rs-fMRI. Materials and Methods The sc-fMRI and rs-fMRI scans in patients who underwent presurgical brain mapping between 2012 and 2016 were analyzed, using the same imaging parameters (other than scanning time) on a 3.0-T MRI scanner. ICA was performed on rs-fMRI and sc-fMRI scans with use of a tool to separate data sets into their spatial and temporal components. Two neuroradiologists independently determined the presence of the dorsal SMN (dSMN) and ventral SMN (vSMN) on each study. Groups were compared by using t tests, and logistic regression was performed to identify predictors of the presence of SMNs. Results One hundred patients (mean age, 40.9 years ± 14.8 [standard deviation]; 61 men) were evaluated. The dSMN and vSMN were identified in 86% (86 of 100) and 76% (76 of 100) of rs-fMRI scans and 85% (85 of 100) and 69% (69 of 100) of sc-fMRI scans, respectively. The concordance between rs-fMRI and sc-fMRI for presence of dSMN and vSMN was 75% (75 of 100 patients) and 53% (53 of 100 patients), respectively. In 10 of 14 patients (71%) where rs-fMRI did not show the dSMN, sc-fMRI demonstrated it. This rate was 67% for the vSMN (16 of 24 patients). Conclusion In the majority of patients, independent component analysis of sentence completion task functional MRI scans reliably demonstrated the somatomotor network compared with resting-state functional MRI scans. Identifying target networks with a single sentence completion scan could reduce overall functional MRI scanning times by eliminating the need for separate motor tasks. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Field and Birn in this issue.

The association of neuropsychiatric symptoms with regional brain volumes from patients in a tertiary multi-disciplinary memory clinic.

BACKGROUND: To examine the interaction between structural brain volume measures derived from a clinical magnetic resonance imaging (MRI) and occurrence of neuropsychiatric symptoms (NPS) in outpatient memory clinic patients. METHODS: Clinical and neuroimaging data were collected from the medical records of outpatient memory clinic patients who were seen by neurologists, geriatric neuropsychiatrists, and geriatricians. MRI scan acquisition was carried out on a 3 T Siemens Verio scanner at Johns Hopkins Bayview Medical Center. Image analyses used an automated multi-label atlas fusion method with a geriatric atlas inventory to generate 193 anatomical regions from which volumes were measured. Regions of interest were generated a priori based on previous literature review of NPS in dementia. Regional volumes for agitation, apathy, and delusions were carried forward in a linear regression analysis. RESULTS: Seventy-two patients had clinical and usable neuroimaging data that were analyzed and grouped by Mini-Mental State Exam (MMSE). Neuropsychiatric Inventory Questionnaire (NPI-Q) agitation was inversely associated with rostral anterior cingulate cortex (ACC) bilaterally and left subcallosal ACC volumes in the moderate severity group. Delusions were positively associated with left ACC volumes in both severe and mild groups but inversely associated with the right dorsolateral prefrontal cortex (DLPFC) in the moderate subgroup. CONCLUSIONS: Agitation, apathy, and delusions are associated with volumes of a priori selected brain regions using clinical data and clinically acquired MRI scans. The ACC is an anatomic region common to these symptoms, particularly agitation and delusions, which closely mirror the findings of research-quality studies and suggest its importance as a behavioral hub.