2022
Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries
Tillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neuro-Oncology Advances 2022, 4: vdac093. PMID: 36071926, PMCID: PMC9446682, DOI: 10.1093/noajnl/vdac093.Peer-Reviewed Original ResearchGlioma segmentationResearch algorithmSegmentation of gliomasHigh accuracy resultsML algorithmsApplicable machineAccuracy resultsTCIA datasetSegmentationAlgorithmMachinePatient dataSystematic literature reviewOverfittingData extractionDatasetBratDatabaseRecent advancesResearch literatureLimitationsExtractionCurrent research literatureMethod
2015
Screening for Pediatric Blunt Cerebrovascular Injury: Review of Literature and a Cost-Effectiveness Analysis
Malhotra A, Wu X, Kalra VB, Goodman TR, Schindler J, Forman HP. Screening for Pediatric Blunt Cerebrovascular Injury: Review of Literature and a Cost-Effectiveness Analysis. Journal Of Pediatric Surgery 2015, 50: 1751-1757. PMID: 26546389, DOI: 10.1016/j.jpedsurg.2015.05.005.Peer-Reviewed Original ResearchConceptsBlunt cerebrovascular injuryPediatric blunt cerebrovascular injurySelective computed tomography angiographyComputed tomography angiographyOptimal imaging strategyCost-effectiveness analysisAnticoagulation therapyCerebrovascular injuryManagement of BCVIAppropriate anticoagulation therapyHigh-risk patientsHigh-risk factorsImaging strategiesPossible screening strategiesReview of literatureSelective anticoagulationPediatric patientsAnticoagulation complicationsTomography angiographySystematic reviewPatientsSocietal perspectiveCost-effective strategyData extractionScreening strategy