2023
Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging
Williams M, Bednarski B, Pieszko K, Miller R, Kwiecinski J, Shanbhag A, Liang J, Huang C, Sharir T, Dorbala S, Di Carli M, Einstein A, Sinusas A, Miller E, Bateman T, Fish M, Ruddy T, Acampa W, Hauser M, Kaufmann P, Dey D, Berman D, Slomka P. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 50: 2656-2668. PMID: 37067586, PMCID: PMC10317876, DOI: 10.1007/s00259-023-06218-z.Peer-Reviewed Original ResearchConceptsCoronary artery diseaseMyocardial perfusion imagingCause mortalityTotal perfusion deficitArtery diseaseRisk stratificationPerfusion deficitsHigher body mass indexTomography myocardial perfusion imagingMore diabetes mellitusSPECT myocardial perfusion imagingBetter risk stratificationRisk-stratify patientsBody mass indexREFINE SPECT registrySingle photon emissionDiabetes mellitusMass indexExternal cohortStress imagingMyocardial perfusionPatientsRisk phenotypePerfusion imagingImaging characteristics
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 perfusionDifferences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study
Tamarappoo BK, Otaki Y, Sharir T, Hu LH, Gransar H, Einstein AJ, Fish MB, Ruddy TD, Kaufmann P, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Eisenberg E, Liang JX, Dey D, Berman DS, Slomka PJ. Differences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study. Circulation Cardiovascular Imaging 2022, 15: e012741. PMID: 35727872, PMCID: PMC9307118, DOI: 10.1161/circimaging.121.012741.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsTotal perfusion defectPrognostic valuePerfusion defectsSingle photon emissionLarge international multicenter studyMyocardial perfusion single-photon emissionPerfusion single-photon emissionStress SPECT myocardial perfusion imagingSPECT myocardial perfusion imagingAdverse cardiac eventsMACE-free survivalMultivariable Cox modelSingle-center studyGreater prognostic valueInternational multicenter studyProportional hazards modelMyocardial perfusion defectsMyocardial perfusion imagingREFINE SPECT registryConventional single-photon emissionCardiac eventsHazard ratioEjection fractionMulticenter study
2021
Impact of age, sex, and cardiac size on the diagnostic performance of myocardial perfusion single-photon emission computed tomography: insights from the REFINE SPECT registry
Randazzo M, Elias P, Poterucha T, Sharir T, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Berman D, Slomka P, Einstein A. Impact of age, sex, and cardiac size on the diagnostic performance of myocardial perfusion single-photon emission computed tomography: insights from the REFINE SPECT registry. European Heart Journal 2021, 42: ehab724.0254. DOI: 10.1093/eurheartj/ehab724.0254.Peer-Reviewed Original ResearchCoronary artery diseaseElderly patientsImpact of ageSingle photon emissionDiagnostic performanceYounger patientsFemale patientsCardiac sizeSPECT-MPILower EDVCardiac volumesObstructive coronary artery diseasePrediction of CADTomography myocardial perfusion imagingMyocardial perfusion single-photon emissionDiagnostic accuracyPerfusion single-photon emissionCardiac event rateStress total perfusion deficitReceiver-operating characteristic curveInvasive coronary angiographyEnd-diastolic volumeCardiac chamber sizeAlternative diagnostic modalityTotal perfusion deficitPrognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry
Kuronuma K, Miller RJH, Otaki Y, Van Kriekinge SD, Diniz MA, Sharir T, Hu LH, Gransar H, Liang JX, Parekh T, Kavanagh PB, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry. Circulation Cardiovascular Imaging 2021, 14: e012386. PMID: 34281372, PMCID: PMC8978932, DOI: 10.1161/circimaging.120.012386.Peer-Reviewed Original ResearchMeSH KeywordsAgedCanadaCoronary CirculationDisease ProgressionFemaleHumansIncidenceIsraelMaleMiddle AgedMyocardial IschemiaMyocardial Perfusion ImagingPredictive Value of TestsPrognosisRegistriesRisk AssessmentRisk FactorsStroke VolumeTomography, Emission-Computed, Single-PhotonUnited StatesVentricular Function, LeftConceptsMajor adverse cardiac eventsVentricular ejection fractionTotal perfusion deficitSingle photon emissionAdverse cardiac eventsEjection fractionPerfusion deficitsCardiac eventsPrognostic valueTomography myocardial perfusion imagingVentricular ejection fraction assessmentHighest decile groupProportional hazards analysisTomography myocardial perfusionIndependent prognostic significanceIndependent prognostic valueLarge multinational registryEjection fraction assessmentMyocardial perfusion imagingREFINE SPECT registryAdditional radiation exposureConventional single-photon emissionMACE riskMACE rateMultinational registryDetermining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry
Rios R, Miller RJH, Hu LH, Otaki Y, Singh A, Diniz M, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, DiCarli M, Van Kriekinge S, Kavanagh P, Parekh T, Liang JX, Dey D, Berman DS, Slomka P. Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. Cardiovascular Research 2021, 118: 2152-2164. PMID: 34259870, PMCID: PMC9302886, DOI: 10.1093/cvr/cvab236.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsMyocardial perfusion imagingTotal perfusion deficitPrognostic accuracyREFINE SPECT registryImaging variablesTraditional multivariable modelsMultivariable modelStress total perfusion deficitAdverse cardiac eventsReceiver-operating characteristic curveOptimal risk stratificationTomography (SPECT) MPIComparable prognostic accuracyComparable prognostic performanceHigher prognostic accuracySingle photon emissionCardiovascular event predictionCardiac eventsRisk stratificationClinical variablesPerfusion deficitsPrognostic performancePerfusion imagingCollected variablesClinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease
Otaki Y, Singh A, Kavanagh P, Miller RJH, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease. JACC Cardiovascular Imaging 2021, 15: 1091-1102. PMID: 34274267, PMCID: PMC9020794, DOI: 10.1016/j.jcmg.2021.04.030.Peer-Reviewed Original ResearchConceptsTotal perfusion deficitMyocardial perfusion imagingStandard clinical softwareStress total perfusion deficitObstructive CADTomography myocardial perfusion imagingSPECT myocardial perfusion imagingStress myocardial perfusionCoronary artery diseaseReceiver-operating characteristic curveInvasive coronary angiographyClinical softwareReader diagnosisSingle photon emissionTypical clinical workflowArtery diseaseCoronary angiographyPerfusion deficitsDiagnostic findingsMyocardial perfusionPerfusion imagingVentricular volumeStandard clinical workstationPatientsDiagnostic accuracyDiagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning
Liu H, Wu J, Miller EJ, Liu C, Yaqiang, Liu, Liu YH. Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning. European Journal Of Nuclear Medicine And Molecular Imaging 2021, 48: 2793-2800. PMID: 33511425, DOI: 10.1007/s00259-021-05202-9.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingCoronary artery diseaseMyocardial perfusion abnormalitiesPerfusion abnormalitiesDiagnostic accuracyConvolutional neural networkTomography myocardial perfusion imagingYale-New Haven HospitalMyocardial perfusion defect sizeSPECT myocardial perfusion imagingAbnormal myocardial perfusionReceiver-operating characteristic curvePerfusion defect sizeNew Haven HospitalAUC valuesSingle photon emissionMyocardial perfusion SPECTDeep learningHigh diagnostic accuracyArtery diseaseDL methodsFinal diagnosisPatient genderMyocardial perfusionPerfusion SPECT
2020
Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia
Azadani PN, Miller RJH, Sharir T, Diniz MA, Hu LH, Otaki Y, Gransar H, Liang JX, Eisenberg E, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia. JACC Cardiovascular Imaging 2020, 14: 644-653. PMID: 32828784, PMCID: PMC7987223, DOI: 10.1016/j.jcmg.2020.05.039.Peer-Reviewed Original ResearchConceptsIschemic total perfusion deficitTotal perfusion deficitEarly revascularizationSPECT-MPIMajor adverse cardiovascular eventsPropensity scoreMultivariable Cox modelingAdverse cardiovascular eventsSingle-center dataContemporary cardiology practiceSingle photon emissionCardiovascular eventsPrimary outcomeCardiac deathConsecutive patientsCox modelingMulticenter studyPerfusion deficitsInternational registryRevascularizationCardiology practicePatientsIschemiaSignificant associationMACEPrognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT
Hu LH, Miller RJH, Sharir T, Commandeur F, Rios R, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Eisenberg E, Dey D, Berman DS, Slomka PJ. Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT. European Heart Journal - Cardiovascular Imaging 2020, 22: 705-714. PMID: 32533137, DOI: 10.1093/ehjci/jeaa134.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsPhysician interpretationMACE rateCancellation rateTomography myocardial perfusion imagingAdverse cardiac eventsInternational multicentre registryCause mortality ratesMyocardial perfusion imagingCurrent clinical approachesSingle photon emissionMulticentre registryCardiac eventsClinical dataMortality ratePerfusion imagingClinical approachPatientsML thresholdsRadiation exposureMPI scansPhoton emissionML scoreMLSafety
2019
Myocardial Ischemic Burden and Differences in Prognosis Among Patients With and Without Diabetes: Results From the Multicenter International REFINE SPECT Registry
Han D, Rozanski A, Gransar H, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Hu LH, Germano G, Dey D, Berman DS, Slomka PJ. Myocardial Ischemic Burden and Differences in Prognosis Among Patients With and Without Diabetes: Results From the Multicenter International REFINE SPECT Registry. Diabetes Care 2019, 43: 453-459. PMID: 31776140, PMCID: PMC6971784, DOI: 10.2337/dc19-1360.Peer-Reviewed Original ResearchMeSH KeywordsAgedAngina, UnstableCohort StudiesCoronary Artery DiseaseDiabetes MellitusDiabetic AngiopathiesFemaleHumansMaleMiddle AgedMyocardial InfarctionMyocardial IschemiaMyocardial Perfusion ImagingPrevalencePrognosisPropensity ScoreRegistriesRisk FactorsTomography, Emission-Computed, Single-PhotonConceptsMajor adverse cardiovascular eventsTotal perfusion deficitMACE riskMyocardial ischemic burdenAdverse cardiovascular eventsTomography myocardial perfusionREFINE SPECT registrySingle photon emissionIschemic burdenMinimal ischemiaCardiovascular eventsCause mortalityLate revascularizationPrognostic impactUnstable anginaSignificant ischemiaPerfusion deficitsMyocardial infarctionMyocardial ischemiaRisk factorsCardiovascular diseaseHigh riskMyocardial perfusionPatientsDiabetesMachine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.
Hu LH, Betancur J, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Commandeur F, Liang JX, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry. European Heart Journal - Cardiovascular Imaging 2019, 21: 549-559. PMID: 31317178, PMCID: PMC7167744, DOI: 10.1093/ehjci/jez177.Peer-Reviewed Original ResearchConceptsEarly coronary revascularizationMyocardial perfusion imagingStress TPDCoronary revascularizationTomography myocardial perfusion imagingTetrofosmin myocardial perfusion imagingSPECT myocardial perfusion imagingInvasive coronary angiographyReceiver operator characteristic curveREFINE SPECT registryPatient-specific explanationsOperator characteristic curveSingle photon emissionMyocardial perfusion SPECTCoronary angiographyIndividual patientsImaging variablesPatientsPerfusion SPECTPerfusion imagingClinical settingCharacteristic curveRevascularizationExpert interpretationStress testTransient ischaemic dilation and post-stress wall motion abnormality increase risk in patients with less than moderate ischaemia: analysis of the REFINE SPECT registry.
Miller RJH, Hu LH, Gransar H, Betancur J, Eisenberg E, Otaki Y, Sharir T, Fish MB, Ruddy TD, Dorbala S, Carli MD, Einstein AJ, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman T, Germano G, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Transient ischaemic dilation and post-stress wall motion abnormality increase risk in patients with less than moderate ischaemia: analysis of the REFINE SPECT registry. European Heart Journal - Cardiovascular Imaging 2019, 21: 567-575. PMID: 31302679, PMCID: PMC7167750, DOI: 10.1093/ehjci/jez172.Peer-Reviewed Original ResearchConceptsMajor adverse cardiovascular eventsTransient ischemic dilationWall motion abnormalitiesModerate ischaemiaMyocardial perfusion imagingMild ischaemiaIschemic dilationMultivariable Cox proportional hazards analysisHigh-risk imaging featuresCox proportional hazards analysisTomography myocardial perfusion imagingAdverse cardiovascular eventsHigh-risk featuresGroup of patientsProportional hazards analysisIncremental prognostic utilityTotal perfusion deficitREFINE SPECT registrySingle photon emissionMedian followCardiovascular eventsCardiovascular riskMultivariable analysisPrognostic utilityPerfusion deficits5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects Results From REFINE SPECT
Otaki Y, Betancur J, Sharir T, Hu LH, Gransar H, Liang JX, Azadani PN, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Tamarappoo BK, Germano G, Dey D, Berman DS, Slomka PJ. 5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects Results From REFINE SPECT. JACC Cardiovascular Imaging 2019, 13: 774-785. PMID: 31202740, PMCID: PMC6899217, DOI: 10.1016/j.jcmg.2019.02.028.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsTotal perfusion deficitStress total perfusion deficitMACE rateHazard ratioRate of MACECox proportional hazards analysisTomography myocardial perfusion imagingAdjusted hazard ratioAdverse cardiac eventsNonfatal myocardial infarctionProportional hazards analysisMyocardial perfusion imagingSingle photon emissionCardiac eventsLate revascularizationUnstable anginaRisk stratificationPrognostic valueKaplan-MeierPerfusion abnormalitiesPerfusion deficitsMyocardial infarctionPrognostic studiesMyocardial perfusion
2018
New Method for Quantification of the Left Ventricular Function from Low-dose Equilibrium Radionuclide Angiocardiography: Comparisons with Conventional Methods in Patients
Liu Y, Tsatkin V, Fazzone-Chettiar R, Miller E, Sinusas A. New Method for Quantification of the Left Ventricular Function from Low-dose Equilibrium Radionuclide Angiocardiography: Comparisons with Conventional Methods in Patients. 2018, 00: 1-3. DOI: 10.1109/nssmic.2018.8824749.Peer-Reviewed Original ResearchEnd-diastolic volumeEquilibrium radionuclide angiocardiographyLeft ventricular functionGated blood pool SPECTPeak filling rateBlood pool SPECTEjection fractionVentricular functionRadionuclide angiocardiographyLV end-diastolic volumeHigh-dose injectionLow-dose injectionsAnterior oblique viewSingle photon emissionRed blood cellsDose reductionReduced radiation dosePatientsShort-axis slicesBlood cellsRadiation exposureGold standardImaging modalitiesRadiation doseOblique viewDeep 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