FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2895519295> ?p ?o ?g. }

• W2895519295 abstract "BACKGROUND CONTEXT The anatomic changes characterized by magnetic resonance imaging (MRI) in patients with lumbar spinal stenosis weakly correlate with pain intensity and functional limitation in patients who endorse the classic evoked symptom pattern of neurogenic claudication (NC). PURPOSE To identify MRI features that correlate with the evoked symptom pattern of neurogenic claudication using a novel machine learning algorithm. STUDY DESIGN/SETTING Subjects with chronic low back pain and a lumbar MRI study were recruited from a single academic neurosurgery department and evaluated using a standardized protocol of validated, disease specific questionnaires and a treadmill-based functional assessment. PATIENT SAMPLE A total of 159 subjects with lumbar spinal stenosis were categorized into NC and nonNC cohorts based on clinical assessment. An independent radiologist blinded to clinical cohort analyzed the MRI data. Subjects with nonstandard MRI imaging data or studies compromised by artifact (eg lack of fat-suppression or presence of obscuring artifact) were excluded. 61 subjects (28 females) with 33 classified as NC+ were included. The mean age was 68.4±11. OUTCOME MEASURES Classification of NC based on imaging variables. METHODS The T2 weighted sagittal MRI FS images were segmented into posterior fat, subcutaneous fat, muscle, vertebral bodies, spinal canal and paravertebral space, then further segmented into seven sub-regions corresponding to levels T12 through S1. Image data was quantified for topography of each ROI or sub-ROI (area, compactness, and mass eccentricity); MRI signal (mean, standard deviation, skewness, kurtosis, entropy, and energy) and texture (gray-level co-occurrence matrix) (angular second moment, contrast, mutual information, correlation, dissimilarity, entropy, homogeneity, marginal entropy, and variance). Together the regions and measurements generated 1,512 features. Left and right mean and absolute difference were calculated to generate features representing asymmetry. All measurements were adjusted for gender and age differences; calibrated for gain differences using the characteristics of the background and normalized using the inverse rank transform. The normalized features were used to construct a diagnostic model based on logistic models. The model was constructed using a feature selection algorithm under a 10-fold cross-validation scheme repeated 5times. RESULTS Differences texture in the vertebral body and the spinal canal were strongly associated with the clinical diagnosis. The model validation predicts that MRI-based logistic models can separate NC with a sensitivity of 0.75 (95% CI 0.55–0.89) with a specificity of 0.97 (95% CI 0.84–1.00) and a diagnostic odds ratio of 96. CONCLUSIONS A machine learning model of MRI features can determine the likelihood of the clinical presentation of NC. Differences in vertebral body signal and the spinal canal show the strongest association to the clinical diagnosis. Further validation of the model may help facilitate pain management strategies." @default.
• W2895519295 created "2018-10-12" @default.
• W2895519295 creator A5006825522 @default.
• W2895519295 creator A5025332098 @default.
• W2895519295 creator A5039668849 @default.
• W2895519295 creator A5040382900 @default.
• W2895519295 creator A5058411478 @default.
• W2895519295 creator A5062876982 @default.
• W2895519295 creator A5072577017 @default.
• W2895519295 creator A5075381913 @default.
• W2895519295 creator A5075416557 @default.
• W2895519295 date "2018-08-01" @default.
• W2895519295 modified "2023-10-01" @default.
• W2895519295 title "Wednesday, September 26, 2018 7:35 AM–9:00 AM ePosters" @default.
• W2895519295 doi "https://doi.org/10.1016/j.spinee.2018.06.580" @default.
• W2895519295 hasPublicationYear "2018" @default.
• W2895519295 type Work @default.
• W2895519295 sameAs 2895519295 @default.
• W2895519295 citedByCount "0" @default.
• W2895519295 crossrefType "journal-article" @default.
• W2895519295 hasAuthorship W2895519295A5006825522 @default.
• W2895519295 hasAuthorship W2895519295A5025332098 @default.
• W2895519295 hasAuthorship W2895519295A5039668849 @default.
• W2895519295 hasAuthorship W2895519295A5040382900 @default.
• W2895519295 hasAuthorship W2895519295A5058411478 @default.
• W2895519295 hasAuthorship W2895519295A5062876982 @default.
• W2895519295 hasAuthorship W2895519295A5072577017 @default.
• W2895519295 hasAuthorship W2895519295A5075381913 @default.
• W2895519295 hasAuthorship W2895519295A5075416557 @default.
• W2895519295 hasConcept C126838900 @default.
• W2895519295 hasConcept C141071460 @default.
• W2895519295 hasConcept C142724271 @default.
• W2895519295 hasConcept C143409427 @default.
• W2895519295 hasConcept C151730666 @default.
• W2895519295 hasConcept C178910020 @default.
• W2895519295 hasConcept C204787440 @default.
• W2895519295 hasConcept C2777466421 @default.
• W2895519295 hasConcept C2778963770 @default.
• W2895519295 hasConcept C2779343474 @default.
• W2895519295 hasConcept C2779631646 @default.
• W2895519295 hasConcept C2780175798 @default.
• W2895519295 hasConcept C2780907711 @default.
• W2895519295 hasConcept C2781310810 @default.
• W2895519295 hasConcept C2989005 @default.
• W2895519295 hasConcept C3018348675 @default.
• W2895519295 hasConcept C44575665 @default.
• W2895519295 hasConcept C71924100 @default.
• W2895519295 hasConcept C86803240 @default.
• W2895519295 hasConceptScore W2895519295C126838900 @default.
• W2895519295 hasConceptScore W2895519295C141071460 @default.
• W2895519295 hasConceptScore W2895519295C142724271 @default.
• W2895519295 hasConceptScore W2895519295C143409427 @default.
• W2895519295 hasConceptScore W2895519295C151730666 @default.
• W2895519295 hasConceptScore W2895519295C178910020 @default.
• W2895519295 hasConceptScore W2895519295C204787440 @default.
• W2895519295 hasConceptScore W2895519295C2777466421 @default.
• W2895519295 hasConceptScore W2895519295C2778963770 @default.
• W2895519295 hasConceptScore W2895519295C2779343474 @default.
• W2895519295 hasConceptScore W2895519295C2779631646 @default.
• W2895519295 hasConceptScore W2895519295C2780175798 @default.
• W2895519295 hasConceptScore W2895519295C2780907711 @default.
• W2895519295 hasConceptScore W2895519295C2781310810 @default.
• W2895519295 hasConceptScore W2895519295C2989005 @default.
• W2895519295 hasConceptScore W2895519295C3018348675 @default.
• W2895519295 hasConceptScore W2895519295C44575665 @default.
• W2895519295 hasConceptScore W2895519295C71924100 @default.
• W2895519295 hasConceptScore W2895519295C86803240 @default.
• W2895519295 hasLocation W28955192951 @default.
• W2895519295 hasOpenAccess W2895519295 @default.
• W2895519295 hasPrimaryLocation W28955192951 @default.
• W2895519295 hasRelatedWork W1559150528 @default.
• W2895519295 hasRelatedWork W180082951 @default.
• W2895519295 hasRelatedWork W2051626723 @default.
• W2895519295 hasRelatedWork W2055143862 @default.
• W2895519295 hasRelatedWork W2258098147 @default.
• W2895519295 hasRelatedWork W2895519295 @default.
• W2895519295 hasRelatedWork W2921219214 @default.
• W2895519295 hasRelatedWork W2931576117 @default.
• W2895519295 hasRelatedWork W3211917658 @default.
• W2895519295 hasRelatedWork W4281725838 @default.
• W2895519295 isParatext "false" @default.
• W2895519295 isRetracted "false" @default.
• W2895519295 magId "2895519295" @default.
• W2895519295 workType "article" @default.