Patent Foramen Ovale Closure Decreases the Incidence but Not the Size of New Brain Infarction on Magnetic Resonance Imaging: An Analysis of the REDUCE Trial.

Background and Purpose: Randomized patent foramen ovale closure trials have used open-label end point ascertainment which increases the risk of bias and undermines confidence in the conclusions. The Gore REDUCE trial prospectively performed baseline and follow-up magnetic resonance imaging (MRIs) for all subjects providing an objective measure of the effectiveness of closure. Methods: We performed blinded evaluations of the presence, location, and volume of new infarct on diffusion-weighted imaging of recurrent clinical stroke or new infarct (>3 mm) on T2/fluid attenuated inversion recovery from baseline to follow-up MRI at 2 years, comparing closure to medical therapy alone. We also examined the effect of shunt size and the development of atrial fibrillation on infarct burden at follow-up. Results: At follow-up, new clinical stroke or silent MRI infarct occurred in 18/383 (4.7%) patients who underwent closure and 19/177 (10.7%) medication-only patients (relative risk, 0.44 [95% CI, 0.24­0.81], P=0.02). Clinical strokes were less common in closure patients compared with medically treated patients, 5 (1.3%) versus 12 (6.8%), P=0.001, while silent MRI infarcts were similar, 13 (3.4%) versus 7 (4.0%), P=0.81. There were no differences in number, volumes, and distribution of new infarct comparing closure patients to those treated with medication alone. There were also no differences of number, volumes, and distribution comparing silent infarcts to clinical strokes. Infarct burden was also similar for patients who developed atrial fibrillation and for those with large shunts. Conclusions: The REDUCE trial demonstrates that patent foramen ovale closure prevents recurrent brain infarction based on the objective outcome of new infarcts on MRI. Only clinical strokes were reduced by closure while silent infarctions were similar between study arms, and there were no differences in infarct volume or location comparing silent infarcts to clinical strokes. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00738894.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training.

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma.

BACKGROUND: Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represent the effects of treatment, or pseudo-progression (PsP). We hypothesize that quantitative machine learning analysis of clinically acquired multiparametric magnetic resonance imaging (mpMRI) can identify subvisual imaging characteristics to provide robust, noninvasive imaging signatures that can distinguish true progression (TP) from PsP. METHODS: We evaluated independent discovery (n = 40) and replication (n = 23) cohorts of glioblastoma patients who underwent second resection due to progressive radiographic changes suspicious for recurrence. Deep learning and conventional feature extraction methods were used to extract quantitative characteristics from the mpMRI scans. Multivariate analysis of these features revealed radiophenotypic signatures distinguishing among TP, PsP, and mixed response that compared with similar categories blindly defined by board-certified neuropathologists. Additionally, interinstitutional validation was performed on 20 new patients. RESULTS: Patients who demonstrate TP on neuropathology are significantly different (P < .0001) from those with PsP, showing imaging features reflecting higher angiogenesis, higher cellularity, and lower water concentration. The accuracy of the proposed signature in leave-one-out cross-validation was 87% for predicting PsP (area under the curve [AUC], 0.92) and 84% for predicting TP (AUC, 0.83), whereas in the discovery/replication cohort, the accuracy was 87% for predicting PsP (AUC, 0.84) and 78% for TP (AUC, 0.80). The accuracy in the interinstitutional cohort was 75% (AUC, 0.80). CONCLUSION: Quantitative mpMRI analysis via machine learning reveals distinctive noninvasive signatures of TP versus PsP after treatment of glioblastoma. Integration of the proposed method into clinical studies can be performed using the freely available Cancer Imaging Phenomics Toolkit.

Association between urinary symptom severity and white matter plaque distribution in women with multiple sclerosis.

AIMS: Multiple sclerosis (MS) is characterized by demyelinated white matter plaque throughout the central nervous system. Plaque involvement in regions that regulate micturition may be associated with urinary symptom severity in patients with MS. The aim of this prospective study is to investigate the relationship between cerebral plaque volume (PV), location, and urinary symptoms in women with MS. METHODS: We conducted a case-control pilot study of women with MS undergoing routine yearly brain MRI. Women were administered the American Urologic Association-Symptom Index (AUA-SI) and divided into two groups: severe urinary symptoms (AUA-SI ≥20) and mild symptoms (AUA-SI ≤7). PV and location in the brain were determined using a validated automated white matter lesion segmentation algorithm. RESULTS: This study of 36 women found that the median total PV did not differ between groups. Women with severe urinary symptoms had larger median PV in the left frontal lobe (LFL) and right limbic lobe (RLL) compared with women with mild urinary symptoms. Within the RLL, women with severe symptoms had a larger median PV in the right cingulate gyrus (RCG). There was a moderate correlation between LFL lesion volume and RLL lesion volume with the AUA emptying subscore; however, these regions did not correlate with the storage subscore. CONCLUSIONS: This preliminary study found urinary symptom severity in women with MS is associated with PV in the RCG and LFL, and not total cerebral PV. These findings may explain why disease burden alone is not a predictor of severity or type of voiding dysfunction in patients with MS.

Proposed Standardized Neurological Endpoints for Cardiovascular Clinical Trials: An Academic Research Consortium Initiative.

Surgical and catheter-based cardiovascular procedures and adjunctive pharmacology have an inherent risk of neurological complications. The current diversity of neurological endpoint definitions and ascertainment methods in clinical trials has led to uncertainties in the neurological risk attributable to cardiovascular procedures and inconsistent evaluation of therapies intended to prevent or mitigate neurological injury. Benefit-risk assessment of such procedures should be on the basis of an evaluation of well-defined neurological outcomes that are ascertained with consistent methods and capture the full spectrum of neurovascular injury and its clinical effect. The Neurologic Academic Research Consortium is an international collaboration intended to establish consensus on the definition, classification, and assessment of neurological endpoints applicable to clinical trials of a broad range of cardiovascular interventions. Systematic application of the proposed definitions and assessments will improve our ability to evaluate the risks of cardiovascular procedures and the safety and effectiveness of preventive therapies.

The impact of pontine disease on lower urinary tract symptoms in patients with multiple sclerosis.

AIMS: To investigate the relationship between pontine lesion characteristics on MRI and lower urinary tract symptoms (LUTS) in patients with multiple sclerosis (MS). METHODS: We performed a prospective cohort study of patients with MS and LUTS who were undergoing brain/spine MRI. Patients were administered the American Urological Association Symptom Score (AUA-SS), Medical, Epidemiologic, and Social Aspects of Aging questionnaire (MESA), and Urogenital Distress Inventory questionnaire (UDI-6), underwent Kurtzke Expanded Disability Status Scale (EDSS) scoring by a neurologist, and had their MRIs reviewed by a neuroradiologist. The relationships between symptom scores and lesion number, size, and location were analyzed. RESULTS: There were 42 patients that completed the study and 20 (48%) had one or more pontine lesions. Total AUA-SS and UDI-6 were related to multiple Short Form Health Survey (SF-36) scales and not EDSS scoring. Weak urinary stream measured on the AUA-SS (P = 0.028), and urgency incontinence measured on the MESA questionnaire (P = 0.034) were related to pontine lesion diameter. There was no difference in urinary symptoms according to the presence or absence of a pontine lesion, or according to lesion location within the pons. CONCLUSIONS: Pontine lesion size appears to be related to lower urinary tract symptoms (weak stream and urgency incontinence) in patients with MS. Therefore, CNS lesion characteristics may be able to phenotype voiding symptoms in patients with MS. Neurourol. Urodynam. 36:453-456, 2017. © 2016 Wiley Periodicals, Inc.

Effect of Cerebral Embolic Protection Devices on CNS Infarction in Surgical Aortic Valve Replacement: A Randomized Clinical Trial.

Importance: Stroke is a major complication of surgical aortic valve replacement (SAVR). Objective: To determine the efficacy and adverse effects of cerebral embolic protection devices in reducing ischemic central nervous system (CNS) injury during SAVR. Design, Setting, and Participants: A randomized clinical trial of patients with calcific aortic stenosis undergoing SAVR at 18 North American centers between March 2015 and July 2016. The end of follow-up was December 2016. Interventions: Use of 1 of 2 cerebral embolic protection devices (n = 118 for suction-based extraction and n = 133 for intra-aortic filtration device) vs a standard aortic cannula (control; n = 132) at the time of SAVR. Main Outcomes and Measures: The primary end point was freedom from clinical or radiographic CNS infarction at 7 days (± 3 days) after the procedure. Secondary end points included a composite of mortality, clinical ischemic stroke, and acute kidney injury within 30 days after surgery; delirium; mortality; serious adverse events; and neurocognition. Results: Among 383 randomized patients (mean age, 73.9 years; 38.4% women; 368 [96.1%] completed the trial), the rate of freedom from CNS infarction at 7 days was 32.0% with suction-based extraction vs 33.3% with control (between-group difference, -1.3%; 95% CI, -13.8% to 11.2%) and 25.6% with intra-aortic filtration vs 32.4% with control (between-group difference, -6.9%; 95% CI, -17.9% to 4.2%). The 30-day composite end point was not significantly different between suction-based extraction and control (21.4% vs 24.2%, respectively; between-group difference, -2.8% [95% CI, -13.5% to 7.9%]) nor between intra-aortic filtration and control (33.3% vs 23.7%; between-group difference, 9.7% [95% CI, -1.2% to 20.5%]). There were no significant differences in mortality (3.4% for suction-based extraction vs 1.7% for control; and 2.3% for intra-aortic filtration vs 1.5% for control) or clinical stroke (5.1% for suction-based extraction vs 5.8% for control; and 8.3% for intra-aortic filtration vs 6.1% for control). Delirium at postoperative day 7 was 6.3% for suction-based extraction vs 15.3% for control (between-group difference, -9.1%; 95% CI, -17.1% to -1.0%) and 8.1% for intra-aortic filtration vs 15.6% for control (between-group difference, -7.4%; 95% CI, -15.5% to 0.6%). Mortality and overall serious adverse events at 90 days were not significantly different across groups. Patients in the intra-aortic filtration group vs patients in the control group experienced significantly more acute kidney injury events (14 vs 4, respectively; P = .02) and cardiac arrhythmias (57 vs 30; P = .004). Conclusions and Relevance: Among patients undergoing SAVR, cerebral embolic protection devices compared with a standard aortic cannula did not significantly reduce the risk of CNS infarction at 7 days. Potential benefits for reduction in delirium, cognition, and symptomatic stroke merit larger trials with longer follow-up. Trial Registration: clinicaltrials.gov Identifier: NCT02389894.

What Makes New Ischemic Lesions Symptomatic after Aortic Valve Replacement?

BACKGROUND: Acute cerebral infarctions on diffusion-weighted magnetic resonance imaging (MRI) are common after cardiothoracic surgery. However, most are asymptomatic and we aimed to identify features associated with clinical stroke symptoms. METHODS: Patients over 65 years of age undergoing surgical aortic valve replacement (AVR) for calcific stenosis were prospectively recruited (N = 196). All patients underwent neurological evaluation preoperatively and on postoperative days 1, 3, and 7, and MRI on planned postoperative day 5. Among those with new postoperative DWI lesions, we performed univariate and multivariable analyses to identify clinical, demographic, surgical, and imaging factors associated with clinical stroke symptoms. RESULTS: Of the 129 patients who completed a postsurgical MRI, 79 (61%) had DWI lesions and 17 (21.5%) of these had new stroke symptoms concordant with the infarct distribution. In an exploratory multivariable analysis, focal neurological symptoms were associated with increased age, a longer bypass duration, and a larger pre-existing lesion burden on fluid-attenuated inversion recovery. Limiting the analysis to the 61 patients with analyzable volume and location data, logistic regression failed to identify any location-related determinant of symptomatic lesions. CONCLUSIONS: New DWI lesions are common after AVR, but most are asymptomatic. Patients are more likely to have symptoms with longer bypass durations, increasing age, and larger pre-existing lesion burdens.

Pathogenesis and Risk Factors for Cerebral Infarct After Surgical Aortic Valve Replacement.

BACKGROUND AND PURPOSE: Stroke is a potentially devastating complication of cardiac surgery. Identifying predictors of radiographic infarct may lead to improved stroke prevention for surgical patients. METHODS: We reviewed 129 postoperative brain magnetic resonance imagings from a prospective study of patients undergoing surgical aortic valve replacement. Acute infarcts were classified as watershed or embolic using prespecified criteria. RESULTS: Acute infarct on magnetic resonance imaging was seen in 79 of 129 patients (61%), and interrater reliability for stroke pathogenesis was high (κ=0.93). Embolic infarcts only were identified in 60 patients (46%), watershed only in 2 (2%), and both in 17 (13%). In multivariable logistic regression, embolic infarct was associated with aortic arch atheroma (odds ratio [OR], 3.4; 95% confidence interval [CI], 1.0-12.0; P=0.055), old subcortical infarcts (OR, 5.5; 95% CI, 1.1-26.6; P=0.04), no history of percutaneous transluminal coronary angioplasty or coronary artery bypass graft (OR, 4.0; 95% CI, 1.2-13.7; P=0.03), and higher aortic valve gradient (OR, 1.3 per 5 mm Hg; 95% CI, 1.09-1.6; P=0.004). Watershed infarct was associated with internal carotid artery stenosis ≥70% (OR, 11.7; 95% CI, 1.8-76.8; P=0.01) and increased left ventricular ejection fraction (OR, 1.6 per 5% increase; 95% CI, 1.08-2.4; P=0.02). CONCLUSIONS: The principal mechanism of acute cerebral infarction after aortic valve replacement is embolism. There are distinct factors associated with watershed and embolic infarct, some of which may be modifiable.

Limbic pathway lesions in patients with multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a well-known demyelinating disease to cause cognitive dysfunction. The limbic system, relevant to memory, can be easily overlooked in conventional magnetic resonance imaging (MRI). PURPOSE: To investigate the distribution and frequency of demyelinating lesions affecting white matter connections of the limbic system based on localization with diffusion tensor imaging (DTI)-derived fractional anisotropy (FA) color maps compared to three-dimensional T2-weighted (T2W) and FLAIR volumes in MS patients. MATERIAL AND METHODS: One hundred and fifty patients with a known diagnosis of MS were identified for this Health Insurance Portability and Accountability (HIPAA)-compliant retrospective cross-sectional study. DTI-derived FA color maps, co-registered to T2W and FLAIR images, were analyzed for lesions affecting the three white matter tracts of the limbic system including cingulum, fornix, and mammilothalamic tracts by two investigators. The approximate location of the lesions on FLAIR was always confirmed on the co-registered DTI-derived FA color maps. RESULTS: Of the 150 patients analyzed, 14.6% had cingulum lesions, 2.6% had fornix lesions, and 2.6% had mammilothalamic tract lesions; 21.3% of patients had at least one of the three tracts affected. CONCLUSION: A relatively high frequency of lesions involving the limbic tracts may explain memory deficits and emotional dysfunction commonly experienced by patients with MS. The combined information from T2W, FLAIR, and DTI-derived FA color map allowed for more accurate localization of lesions affecting the major white matter tracts of the limbic system.

Stroke after aortic valve surgery: results from a prospective cohort.

BACKGROUND: The incidence and impact of clinical stroke and silent radiographic cerebral infarction complicating open surgical aortic valve replacement (AVR) are poorly characterized. METHODS AND RESULTS: We performed a prospective cohort study of subjects ≥65 years of age who were undergoing AVR for calcific aortic stenosis. Subjects were evaluated by neurologists preoperatively and postoperatively and underwent postoperative magnetic resonance imaging. Over a 4-year period, 196 subjects were enrolled at 2 sites (mean age, 75.8±6.2 years; 36% women; 6% nonwhite). Clinical strokes were detected in 17%, transient ischemic attack in 2%, and in-hospital mortality was 5%. The frequency of stroke in the Society for Thoracic Surgery database in this cohort was 7%. Most strokes were mild; the median National Institutes of Health Stroke Scale was 3 (interquartile range, 1-9). Clinical stroke was associated with increased length of stay (median, 12 versus 10 days; P=0.02). Moderate or severe stroke (National Institutes of Health Stroke Scale ≥10) occurred in 8 (4%) and was strongly associated with in-hospital mortality (38% versus 4%; P=0.005). Of the 109 stroke-free subjects with postoperative magnetic resonance imaging, silent infarct was identified in 59 (54%). Silent infarct was not associated with in-hospital mortality or increased length of stay. CONCLUSIONS: Clinical stroke after AVR was more common than reported previously, more than double for this same cohort in the Society for Thoracic Surgery database, and silent cerebral infarctions were detected in more than half of the patients undergoing AVR. Clinical stroke complicating AVR is associated with increased length of stay and mortality.

Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity.

PURPOSE: To augment the analysis of dynamic susceptibility contrast material-enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. MATERIALS AND METHODS: Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. RESULTS: The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. CONCLUSION: Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication.

Effect of diabetes on brain structure: the action to control cardiovascular risk in diabetes MR imaging baseline data.

PURPOSE: To investigate the association of characteristics of type 2 diabetes mellitus (duration and biochemical severity of diabetes) to brain structure measured on magnetic resonance (MR) images, specifically testing whether more severity in metrics of diabetes is inversely correlated with brain volumes and positively correlated with ischemic lesion volumes. MATERIALS AND METHODS: This study protocol was approved by the institutional review board of each center and participants provided written informed consent. Baseline severity of diabetes was evaluated by testing fasting plasma glucose levels, hemoglobin A1c levels, and duration of diabetes. MR imaging was performed with fluid-attenuated inversion recovery, proton-density, T2-weighted, and T1-weighted sequences, which were postprocessed with an automated computer algorithm that classified brain tissue as gray or white matter and as normal or ischemic. Separate linear regression models adjusted for potential confounding factors were used to investigate the relationship of the diabetes measures to MR imaging outcomes in 614 participants (mean age, 62 years; mean duration of type 2 diabetes mellitus, 9.9 years). RESULTS: The mean volumes of total gray matter (463.9 cm(3)) and total white matter (463.6 cm(3)) were similar. The mean volume of abnormal tissue was 2.5 cm(3), mostly in the white matter (81% white matter, 5% gray matter, 14% deep gray and white matter). Longer duration of diabetes and higher fasting plasma glucose level were associated with lower normal (ß = -0.431 and -0.053, respectively; P < .01) and total gray matter volumes (ß = -0.428 and -0.053, respectively; P < .01). Fasting plasma glucose was also inversely correlated with ischemic lesion volume (ß = -0.006; P < .04). Hemoglobin A1c level was not associated with any MR imaging measure. CONCLUSION: Longer duration of diabetes is associated with brain volume loss, particularly in the gray matter, possibly reflecting direct neurologic insult; biochemical measures of glycemia were less consistently related to MR imaging changes. Contrary to common clinical belief, in this sample of patients with type 2 diabetes mellitus, there was no association of diabetes characteristics with small vessel ischemic disease in the brain.

Direct visualization of short transverse relaxation time component (ViSTa).

White matter of the brain has been demonstrated to have multiple relaxation components. Among them, the short transverse relaxation time component (T2<40 ms; T2⁎<25 ms at 3 T) has been suggested to originate from myelin water whereas long transverse relaxation time components have been associated with axonal and/or interstitial water. In myelin water imaging, T2 or T2⁎ signal decay is measured to estimate myelin water fraction based on T2 or T2⁎ differences among the water components. This method has been demonstrated to be sensitive to demyelination in the brain but suffers from low SNR and image artifacts originating from ill-conditioned multi-exponential fitting. In this study, a novel approach that selectively acquires short transverse relaxation time signal is proposed. The method utilizes a double inversion RF pair to suppress a range of long T1 signal. This suppression leaves short T2⁎ signal, which has been suggested to have short T1, as the primary source of the image. The experimental results confirm that after suppression of long T1 signals, the image is dominated by short T2⁎ in the range of myelin water, allowing us to directly visualize the short transverse relaxation time component in the brain. Compared to conventional myelin water imaging, this new method of direct visualization of short relaxation time component (ViSTa) provides high quality images. When applied to multiple sclerosis patients, chronic lesions show significantly reduced signal intensity in ViSTa images suggesting sensitivity to demyelination.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions.

Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system that affects nearly 1 million adults in the United States. Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis and treatment monitoring in MS patients. In particular, follow-up MRI with T2-FLAIR images of the brain, depicting white matter lesions, is the mainstay for monitoring disease activity and making treatment decisions. In this article, we present a computational approach that has been deployed and integrated into a real-world routine clinical workflow, focusing on two tasks: (a) detecting new disease activity in MS patients, and (b) determining the necessity for injecting Gadolinium Based Contract Agents (GBCAs). This computer-aided detection (CAD) software has been utilized for the former task on more than 19, 000 patients over the course of 10 years, while its added function of identifying patients who need GBCA injection, has been operative for the past 3 years, with > 85% sensitivity. The benefits of this approach are summarized in: (1) offering a reproducible and accurate clinical assessment of MS lesion patients, (2) reducing the adverse effects of GBCAs (and the deposition of GBCAs to the patient's brain) by identifying the patients who may benefit from injection, and (3) reducing healthcare costs, patients' discomfort, and caregivers' workload.

Economic impact of selective use of contrast for routine follow-up MRI of patients with multiple sclerosis.

BACKGROUND AND PURPOSE: Imaging and autopsy studies show intracranial gadolinium deposition in patients who have undergone serial contrast-enhanced MRIs. This observation has raised concerns when using contrast administration in patients who receive frequent MRIs. To address this, we implemented a contrast-conditional protocol wherein gadolinium is administered only for multiple sclerosis (MS) patients with imaging evidence of new disease activity on precontrast imaging. In this study, we explore the economic impact of our new MRI protocol. METHODS: We compared scanner time and Medicare reimbursement using our contrast-conditional methodology versus that of prior protocols where all patients received gadolinium. RESULTS: For 422 patients over 4 months, the contrast-conditional protocol amounted to 60% decrease in contrast injection and savings of approximately 20% of MRI scanner time. If the extra scanner time was used for performing MS follow-up MRIs in additional patients, the contrast-conditional protocol would amount to net revenue loss of $21,707 (∼3.7%). CONCLUSIONS: Implementation of a new protocol to limit contrast in MS follow-up MRIs led to a minimal decrease in revenue when controlled for scanner time utilized and is outweighed by other benefits, including substantial decreased gadolinium administration, increased patient comfort, and increased availability of scanner time, which depending on type of studies performed could result in additional financial benefit.

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research.

Objective.De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria.Approach.Towards this end, this manuscript describes theFederatedTumorSegmentation (FeTS) tool, in terms of software architecture and functionality.Main results.The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data.Significance.Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced athttps://github.com/FETS-AI/Front-End.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

The Medical Segmentation Decathlon.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.