Anesthesiology · 2012 · 150 citations · 28 references
MedicineNeurologyNeuroscienceBiomedical ImagingDiagnostic ImagingDiagnostic RadiologyBrain ImagingNeurologic PrognosticationRecovery After Brain InjuryTraumatic Brain InjuryComposite Score ConstructionMulticenter ValidationNeurological PrognosticationOutcome PrognosticationPrognostic Score ValidationTraumatic Brain Injury PrognosisWhite Matter Injury ImagingWhite Matter Tract Injury
BACKGROUND: Existing methods to predict recovery after severe traumatic brain injury lack accuracy. The aim of this study is to determine the prognostic value of quantitative diffusion tensor imaging (DTI). METHODS: In a multicenter study, the authors prospectively enrolled 105 patients who remained comatose at least 7 days after traumatic brain injury. Patients underwent brain magnetic resonance imaging, including DTI in 20 preselected white matter tracts. Patients were evaluated at 1 yr with a modified Glasgow Outcome Scale. A composite DTI score was constructed for outcome prognostication on this training database and then validated on an independent database (n=38). DTI score was compared with the International Mission for Prognosis and Analysis of Clinical Trials Score. RESULTS: Using the DTI score for prediction of unfavorable outcome on the training database, the area under the receiver operating characteristic curve was 0.84 (95% CI: 0.75-0.91). The DTI score had a sensitivity of 64% and a specificity of 95% for the prediction of unfavorable outcome. On the validation-independent database, the area under the receiver operating characteristic curve was 0.80 (95% CI: 0.54-0.94). On the training database, reclassification methods showed significant improvement of classification accuracy (P < 0.05) compared with the International Mission for Prognosis and Analysis of Clinical Trials score. Similar results were observed on the validation database. CONCLUSIONS: White matter assessment with quantitative DTI increases the accuracy of long-term outcome prediction compared with the available clinical/radiographic prognostic score.
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Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology · 2011 · 41.3K citations
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Stephen M. Smith, Mark Jenkinson, Heidi Johansen‐Berg et al. · NeuroImage · 2006 · 6.6K citations
Toward a quantitative assessment of diffusion anisotropy
Carlo Pierpaoli, Peter J. Basser · Magnetic Resonance in Medicine · 1996 · 2.4K citations · Full text
Medicine, Neurobiology, Neuroscience +15