Dataset Information

Potentially Reversible and Recognizable Acute Encephalopathic Syndromes: Disease Categorization and MRI Appearances.

ABSTRACT: "Encephalopathy" is a vague term that encompasses varying definitions, often with a nonspecific clinical presentation and numerous possible pathophysiologic causes. Hence, MR imaging plays a crucial role in the early diagnosis and treatment by identifying imaging patterns when there is limited clinical history in such patients with acute encephalopathy. The aim of this review was to aid in remembrance of etiologies of potentially reversible acute encephalopathic syndromes on MR imaging. The differential includes vascular (reversible cerebral vasoconstriction syndrome, transient global amnesia, disseminated intravascular coagulation, and thrombotic microangiopathy), infection (meningitis, encephalitis), toxic (posterior reversible encephalopathy syndrome, acute toxic leukoencephalopathy; carbon monoxide, alcohol-related, medication- and illicit drug-related toxic encephalopathies), autoimmune, metabolic (osmotic demyelination syndrome, uremic, acute hepatic encephalopathy), idiopathic/inflammatory (stroke-like migraine attacks after radiation therapy syndrome), neoplasm-related encephalopathy, and seizure-related encephalopathy.

SUBMITTER: Koksel Y

PROVIDER: S-EPMC7658879 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Potentially Reversible and Recognizable Acute Encephalopathic Syndromes: Disease Categorization and MRI Appearances.

Koksel Y Y McKinney A M AM

AJNR. American journal of neuroradiology 20200702 8

"Encephalopathy" is a vague term that encompasses varying definitions, often with a nonspecific clinical presentation and numerous possible pathophysiologic causes. Hence, MR imaging plays a crucial role in the early diagnosis and treatment by identifying imaging patterns when there is limited clinical history in such patients with acute encephalopathy. The aim of this review was to aid in remembrance of etiologies of potentially reversible acute encephalopathic syndromes on MR imaging. The diff ...[more]

PMID: 32616580

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Project description:BACKGROUND:Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. METHODS:In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). RESULTS:The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. CONCLUSION:This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.

Project description:BackgroundBackground parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations.PurposeTo develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations.Study typeRetrospective.PopulationConsecutive high-risk patients (i.e. >20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal).Field strength/sequenceA 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging.AssessmentBreast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards.Statistical testsModel performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α* = 0.025).ResultsThe Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs.Data conclusionFully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports.Level of evidence4 TECHNICAL EFFICACY STAGE: 3.

Dataset Information

Potentially Reversible and Recognizable Acute Encephalopathic Syndromes: Disease Categorization and MRI Appearances.

Publications

Potentially Reversible and Recognizable Acute Encephalopathic Syndromes: Disease Categorization and MRI Appearances.

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