2018
Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study
Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann P, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study. JACC Cardiovascular Imaging 2018, 11: 1654-1663. PMID: 29550305, PMCID: PMC6135711, DOI: 10.1016/j.jcmg.2018.01.020.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overCoronary CirculationCoronary StenosisDeep LearningFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedMyocardial Perfusion ImagingOrganophosphorus CompoundsOrganotechnetium CompoundsPredictive Value of TestsRadiopharmaceuticalsRegistriesTechnetium Tc 99m SestamibiTomography, Emission-Computed, Single-PhotonConceptsTotal perfusion deficitMyocardial perfusion imagingCoronary artery diseaseObstructive diseaseArtery diseaseVessel coronary artery diseaseTomography myocardial perfusion imagingTetrofosmin myocardial perfusion imagingLarge multicenter populationStress total perfusion deficitReceiver-operating characteristic curveInvasive coronary angiographyLeft ventricular myocardiumSingle photon emissionMyocardial perfusion SPECTMonths of MPIMulticenter populationObstructive stenosisCoronary angiographyMulticenter studyCoronary arteryPerfusion deficitsNormal limitsVessel sensitivityCurrent clinical methods
2017
Joint SNMMI–ASNC Expert Consensus Document on the Role of 18F-FDG PET/CT in Cardiac Sarcoid Detection and Therapy Monitoring
Chareonthaitawee P, Beanlands RS, Chen W, Dorbala S, Miller EJ, Murthy VL, Birnie DH, Chen ES, Cooper LT, Tung RH, White ES, Borges-Neto S, Di Carli MF, Gropler RJ, Ruddy TD, Schindler TH, Blankstein R, GROUP N, Bateman T, Cerqueira M, Dilsizian V, Heller G, Moller D, Osborne M, Sadeghi M, Soman P. Joint SNMMI–ASNC Expert Consensus Document on the Role of 18F-FDG PET/CT in Cardiac Sarcoid Detection and Therapy Monitoring. Journal Of Nuclear Medicine 2017, 58: 1341-1353. PMID: 28765228, PMCID: PMC6944184, DOI: 10.2967/jnumed.117.196287.Peer-Reviewed Original ResearchMeSH KeywordsCardiomyopathiesConsensusDocumentationExpert TestimonyFluorodeoxyglucose F18HumansImage Interpretation, Computer-AssistedNuclear MedicinePositron Emission Tomography Computed TomographySarcoidosisSocieties, MedicalTreatment Outcome
2014
Quantitative interpretation of FDG PET/CT with myocardial perfusion imaging increases diagnostic information in the evaluation of cardiac sarcoidosis
Ahmadian A, Brogan A, Berman J, Sverdlov AL, Mercier G, Mazzini M, Govender P, Ruberg FL, Miller EJ. Quantitative interpretation of FDG PET/CT with myocardial perfusion imaging increases diagnostic information in the evaluation of cardiac sarcoidosis. Journal Of Nuclear Cardiology 2014, 21: 925-939. PMID: 24879453, DOI: 10.1007/s12350-014-9901-9.Peer-Reviewed Original ResearchMeSH KeywordsFemaleFluorodeoxyglucose F18Heart DiseasesHumansImage EnhancementImage Interpretation, Computer-AssistedMaleMiddle AgedMultimodal ImagingMyocardial Perfusion ImagingPositron-Emission TomographyRadiopharmaceuticalsReproducibility of ResultsRetrospective StudiesSarcoidosisSensitivity and SpecificityTomography, X-Ray ComputedConceptsFDG PET/CTPET/CTCardiac sarcoidosisStandardized uptake valueMyocardial perfusion imagingClinical eventsPerfusion imagingCardiac FDG PET/CTAdverse clinical eventsOnly independent predictorNegative control studyCS correlatesImmunosuppressive treatmentPrednisone treatmentImmunosuppression treatmentIndependent predictorsFDG uptakeOncologic indicationsPerfusion defectsNegative studiesRepeat examinationLow EFUptake valueControl studyMultivariate analysis