2022
Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning
Singh A, Miller RJH, Otaki Y, Kavanagh P, Hauser MT, Tzolos E, Kwiecinski J, Van Kriekinge S, Wei CC, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Huang C, Han D, Dey D, Berman DS, Slomka PJ. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning. JACC Cardiovascular Imaging 2022, 16: 209-220. PMID: 36274041, PMCID: PMC10980287, DOI: 10.1016/j.jcmg.2022.07.017.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingTotal perfusion deficitNonfatal myocardial infarctionMyocardial infarctionPerfusion imagingTomography myocardial perfusion imagingIschemic total perfusion deficitStress total perfusion deficitTesting groupReceiver-operating characteristic curvePatient-level riskPrediction of deathSingle photon emissionLogistic regression modelsCause mortalityPrimary outcomeHighest quartileRisk stratificationAbnormal perfusionNormal perfusionPerfusion deficitsAdverse event predictionPrognostic accuracyHigh riskMyocardial perfusionMachine learning to predict abnormal myocardial perfusion from pre-test features
Miller RJH, Hauser MT, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Huang C, Liang JX, Han D, Dey D, Berman DS, Slomka PJ. Machine learning to predict abnormal myocardial perfusion from pre-test features. Journal Of Nuclear Cardiology 2022, 29: 2393-2403. PMID: 35672567, PMCID: PMC9588501, DOI: 10.1007/s12350-022-03012-6.Peer-Reviewed Original ResearchConceptsAbnormal myocardial perfusionAbnormal perfusionMyocardial perfusionDiamond-Forrester modelCAD consortiumConsecutive patientsInternational registryPre-test informationSPECT-MPIClinical informationPhysician's decisionPatientsPerfusionTesting populationExpert visual interpretationRegistryPopulationMethodsWePhysicians
2015
pH-weighted molecular imaging of gliomas using amine chemical exchange saturation transfer MRI
Harris R, Cloughesy T, Liau L, Prins R, Antonios J, Li D, Yong W, Pope W, Lai A, Nghiemphu P, Ellingson B. pH-weighted molecular imaging of gliomas using amine chemical exchange saturation transfer MRI. Neuro-Oncology 2015, 17: 1514-1524. PMID: 26113557, PMCID: PMC4648305, DOI: 10.1093/neuonc/nov106.Peer-Reviewed Original ResearchConceptsAmine chemical exchange saturation transferPH-weighted MRIShorter time to progressionAcid lesionsTime to progressionChemical exchange saturation transfer MRIMolecular imaging of gliomaIntracranial glioma modelBrain tumor physiologyImaging of gliomasActive tumorAbnormal perfusionGlioma modelPerfusion abnormalitiesGlioblastoma patientsMolecular imaging techniquesPET uptakeTumor physiologyMR spectroscopyTissue acidosisChemical exchange saturation transferHuman patientsPatientsTumorLesions
2008
Different cytokeratin and neuronal cell adhesion molecule staining patterns in focal nodular hyperplasia and hepatic adenoma and their significance
Iyer A, Robert ME, Bifulco CB, Salem RR, Jain D. Different cytokeratin and neuronal cell adhesion molecule staining patterns in focal nodular hyperplasia and hepatic adenoma and their significance. Human Pathology 2008, 39: 1370-1377. PMID: 18602664, PMCID: PMC3738023, DOI: 10.1016/j.humpath.2008.01.015.Peer-Reviewed Original ResearchConceptsFocal nodular hyperplasiaNeuronal cell adhesion moleculeHepatic adenomaNodular hyperplasiaCell adhesion moleculeCytokeratin 7Cytokeratin 19Biliary epitheliumProgenitor/stem cellsAdhesion moleculesTelangiectatic focal nodular hyperplasiaCytokeratin 7 stainsStem cellsRare positivityAbnormal perfusionBile ductHepatocellular carcinomaStrong positivityHepatic endotheliumImmunohistochemical analysisModerate stainingFibrous septaDifferent cytokeratinsHyperplasiaAdenomas
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