2023
Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging
Feher A, Pieszko K, Shanbhag A, Lemley M, Miller R, Huang C, Miras L, Liu Y, Gerber J, Sinusas A, Miller E, Slomka P. Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging. The International Journal Of Cardiovascular Imaging 2023, 40: 185-193. PMID: 37845406, PMCID: PMC466934, DOI: 10.1007/s10554-023-02980-1.Peer-Reviewed Original ResearchConceptsMajor adverse cardiovascular eventsCoronary artery calcificationMyocardial perfusion imagingCT myocardial perfusion imagingSPECT/CT myocardial perfusion imagingArtery calcificationPrognostic valuePrior coronary stentingAdverse cardiovascular eventsSimilar prognostic valueSPECT myocardial perfusionREFINE SPECT registrySPECT/CTCardiovascular eventsCAC scoringCalcium scoreCoronary calcificationCoronary stentingPrognostic utilitySingle centerCoronary arteryLow doseMyocardial perfusionPerfusion imagingSurvival analysisUnsupervised 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 ResearchMeSH KeywordsCoronary Artery DiseaseExercise TestFemaleHumansMaleMyocardial Perfusion ImagingPrognosisTomography, Emission-Computed, Single-PhotonUnsupervised Machine LearningConceptsCoronary 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 characteristicsThe Association Between Obstructive Sleep Apnea and Abnormal 82Rubidium Cardiac PET Perfusion Myocardial Flow Reserve
Aneni E, Thorn S, Feher A, Hong Chen J, Sinusas A, Yaggi H, Miller E. The Association Between Obstructive Sleep Apnea and Abnormal 82Rubidium Cardiac PET Perfusion Myocardial Flow Reserve. JACC Cardiovascular Imaging 2023, 16: 856-858. PMID: 36881426, PMCID: PMC10718199, DOI: 10.1016/j.jcmg.2022.11.024.Peer-Reviewed Original Research
2022
PET Myocardial Blood Flow for Post-transplant Surveillance and Cardiac Allograft Vasculopathy in Heart Transplant Recipients
Feher A, Miller EJ. PET Myocardial Blood Flow for Post-transplant Surveillance and Cardiac Allograft Vasculopathy in Heart Transplant Recipients. Current Cardiology Reports 2022, 24: 1865-1871. PMID: 36279035, DOI: 10.1007/s11886-022-01804-3.Peer-Reviewed Original ResearchMeSH KeywordsAllograftsCoronary AngiographyCoronary Artery DiseaseHeart TransplantationHumansMyocardial Perfusion ImagingPositron-Emission TomographyConceptsHeart transplant recipientsAllograft vasculopathyTransplant recipientsPositron emission tomography myocardial perfusionOverall graft survivalPost-transplant surveillanceCardiac allograft vasculopathyHeart transplant patientsTomography myocardial perfusionRecent FindingsMultiple studiesMyocardial perfusion imagingMyocardial blood flowMBF quantificationGraft survivalHeart transplantationTransplant patientsTransplant rejectionSerial monitoringPrognostic performancePET myocardial blood flowBlood flowMyocardial perfusionPerfusion imagingMBF assessmentBlood flow quantificationDirect 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 perfusionDeep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events
Miller R, Pieszko K, Shanbhag A, Feher A, Lemley M, Killekar A, Kavanagh PB, Van Kriekinge SD, Liang JX, Huang C, Miller EJ, Bateman T, Berman DS, Dey D, Slomka PJ. Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events. Journal Of Nuclear Medicine 2022, 64: 652-658. PMID: 36207138, PMCID: PMC10071789, DOI: 10.2967/jnumed.122.264423.Peer-Reviewed Original ResearchMitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images
Miller RJH, Singh A, Otaki Y, Tamarappoo BK, Kavanagh P, Parekh T, Hu LH, Gransar H, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli MF, Liang JX, Dey D, Berman DS, Slomka PJ. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 50: 387-397. PMID: 36194270, PMCID: PMC10042590, DOI: 10.1007/s00259-022-05972-w.Peer-Reviewed Original ResearchConceptsCoronary artery diseaseMyocardial perfusion imagingArtery diseaseInvasive angiographyObstructive coronary artery diseaseDisease probabilityLow-risk patientsLow-risk populationHigh-risk populationTotal perfusion deficitHigh diagnostic accuracyS-TPDPerfusion deficitsPatient managementPatientsPerfusion imagingDiagnostic accuracyPerfusion SPECT imagesLower likelihoodGood calibrationCharacteristic curveAngiographySPECT imagesSelection biasDiseaseIntegration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging
Feher A, Pieszko K, Miller R, Lemley M, Shanbhag A, Huang C, Miras L, Liu YH, Sinusas AJ, Miller EJ, Slomka PJ. Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging. Journal Of Nuclear Cardiology 2022, 30: 590-603. PMID: 36195826, DOI: 10.1007/s12350-022-03099-x.Peer-Reviewed Original ResearchConceptsMajor adverse cardiovascular eventsMyocardial perfusion imagingAdverse cardiovascular eventsSPECT myocardial perfusion imagingCAC scoringCardiovascular eventsPrediction of MACECoronary artery calcification (CAC) scoringMACE-free survivalClinical risk factorsCoronary artery calciumCT myocardial perfusion imagingReceiver operator characteristic curveSPECT/CT myocardial perfusion imagingSPECT/CTOperator characteristic curveCT myocardial perfusionArtery calciumCAC scoreAnalysis patientsMACE predictionSingle centerHigher event ratesRisk factorsRisk scoreDeep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.
Shanbhag AD, Miller RJH, Pieszko K, Lemley M, Kavanagh P, Feher A, Miller EJ, Sinusas AJ, Kaufmann PA, Han D, Huang C, Liang JX, Berman DS, Dey D, Slomka PJ. Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT. Journal Of Nuclear Medicine 2022, 64: 472-478. PMID: 36137759, PMCID: PMC10071806, DOI: 10.2967/jnumed.122.264429.Peer-Reviewed Original ResearchDeep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events
Pieszko K, Shanbhag A, Killekar A, Miller RJH, Lemley M, Otaki Y, Singh A, Kwiecinski J, Gransar H, Van Kriekinge SD, Kavanagh PB, Miller EJ, Bateman T, Liang JX, Berman DS, Dey D, Slomka PJ. Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events. JACC Cardiovascular Imaging 2022, 16: 675-687. PMID: 36284402, DOI: 10.1016/j.jcmg.2022.06.006.Peer-Reviewed Original ResearchDifferences 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 ResearchMeSH KeywordsCoronary Artery DiseaseFemaleHumansMaleMyocardial InfarctionMyocardial Perfusion ImagingPerfusionPrognosisTomography, Emission-Computed, Single-PhotonConceptsMajor 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 studyThe Prognostic Value of CAC Zero Among Individuals Presenting With Chest Pain A Meta-Analysis
Agha AM, Pacor J, Grandhi GR, Mszar R, Khan SU, Parikh R, Agrawal T, Burt J, Blankstein R, Blaha MJ, Shaw LJ, Al-Mallah MH, Brackett A, Cainzos-Achirica M, Miller EJ, Nasir K. The Prognostic Value of CAC Zero Among Individuals Presenting With Chest Pain A Meta-Analysis. JACC Cardiovascular Imaging 2022, 15: 1745-1757. PMID: 36202453, DOI: 10.1016/j.jcmg.2022.03.031.Peer-Reviewed Original ResearchMeSH KeywordsCalciumChest PainCoronary AngiographyCoronary Artery DiseaseHumansPredictive Value of TestsPrognosisRisk AssessmentRisk FactorsConceptsObstructive coronary artery diseaseAcute chest painCoronary artery calciumCoronary artery diseaseStable chest painNonobstructive coronary artery diseaseMajor adverse cardiac eventsAdverse cardiac eventsChest painCardiac eventsLow prevalenceAbsence of CACHealth care delivery modelsCare delivery modelsA Meta-AnalysisNegative predictive valueCAC assessmentArtery calciumCAC scoreIntermediate riskArtery diseaseCoronary CTACTA assessmentPrognostic valueTomography angiographyExplainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.
Miller RJH, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, Kavanagh P, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Carli MD, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2022, 63: 1768-1774. PMID: 35512997, PMCID: PMC9635672, DOI: 10.2967/jnumed.121.263686.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceCoronary AngiographyCoronary Artery DiseaseDeep LearningHumansMyocardial Perfusion ImagingPhysiciansTomography, Emission-Computed, Single-PhotonConceptsMyocardial perfusion imagingCoronary artery diseaseObstructive coronary artery diseasePhysician interpretationDiagnostic accuracyPerfusion imagingLeft main arteryOverall net reclassification improvementInvasive coronary angiographyNet reclassification improvementTotal perfusion deficitArtery diseaseCoronary angiographyMedian agePhysician diagnosisReclassification improvementPerfusion deficitsClinical historyCoronary segmentsRepresentative cohortMeaningful improvementsMain arteryPatientsDL resultsQuantitative perfusionPrevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry
Han D, Rozanski A, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Dey D, Berman DS, Slomka PJ. Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry. Journal Of Nuclear Cardiology 2022, 29: 3221-3232. PMID: 35174442, PMCID: PMC9378748, DOI: 10.1007/s12350-021-02829-x.Peer-Reviewed Original ResearchMeSH KeywordsCoronary Artery DiseaseFemaleHumansMaleMyocardial IschemiaMyocardial Perfusion ImagingPrevalenceRegistriesTomography, Emission-Computed, Single-PhotonConceptsTotal perfusion deficitMyocardial ischemiaStress testingCAD risk factorsMulticenter international registryPredictor of ischemiaCardiac stress testingNuclear stress testingChi-square testConclusionThe prevalenceMulticenter registryOverall cohortTypical anginaCAD statusPerfusion deficitsInternational registryMale genderRisk factorsLVEFIschemiaPatientsImaging characteristicsPotent predictorPrevalenceRegistry
2021
Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry
Han D, Rozanski A, Gransar H, Tzolos E, Miller RJH, 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, Dey D, Berman DS, Slomka PJ. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry. Journal Of Nuclear Cardiology 2021, 29: 3003-3014. PMID: 34757571, PMCID: PMC9085969, DOI: 10.1007/s12350-021-02810-8.Peer-Reviewed Original ResearchMeSH KeywordsCoronary Artery DiseaseDiabetes MellitusHumansMyocardial Perfusion ImagingPrognosisRegistriesRisk FactorsTomography, Emission-Computed, Single-PhotonConceptsMajor adverse cardiovascular eventsCoronary artery diseaseTotal perfusion deficitCardiac stress testingStress testingComparison of diabetesAdverse cardiovascular eventsStress test patientsCardiac stress test patientsREFINE SPECT registryDM statusMACE riskBackgroundDiabetes mellitusCardiovascular eventsArtery diseaseVentricular functionPrognostic predictorClinical variablesPerfusion deficitsChi-square analysisSPECT-MPIPatientsTest patientsPropensity scorePotent predictorAssociation Between Impaired Myocardial Flow Reserve on 82Rubidium Positron Emission Tomography Imaging and Adverse Events in Patients With Autoimmune Rheumatic Disease
Feher A, Boutagy NE, Oikonomou EK, Liu YH, Miller EJ, Sinusas AJ, Hinchcliff M. Association Between Impaired Myocardial Flow Reserve on 82Rubidium Positron Emission Tomography Imaging and Adverse Events in Patients With Autoimmune Rheumatic Disease. Circulation Cardiovascular Imaging 2021, 14: e012208. PMID: 34503339, PMCID: PMC8475774, DOI: 10.1161/circimaging.120.012208.Peer-Reviewed Original ResearchConceptsAutoimmune rheumatic diseasesMyocardial flow reserveLower myocardial flow reserveCox regression analysisAdverse eventsARDS diagnosisIndependent predictorsRheumatic diseasesPerfusion defectsFlow reserveNormal myocardial flow reserveLeft ventricular ejection fractionEnd pointGlobal myocardial flow reserveImpaired Myocardial Flow ReserveComorbidity-matched patientsSevere coronary calcificationCombined end pointCoronary microvascular dysfunctionHeart failure admissionsComposite end pointEvent-free survivalFramingham risk scoreLarge perfusion defectsRetrospective cohort analysisDetermining 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 ResearchMeSH KeywordsCardiovascular DiseasesCoronary Artery DiseaseHumansMachine LearningMyocardial Perfusion ImagingPrognosisRegistriesTomography, Emission-Computed, Single-PhotonConceptsMajor 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 safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT
Eisenberg E, Miller RJH, Hu LH, Rios R, Betancur J, Azadani P, Han D, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT. Journal Of Nuclear Cardiology 2021, 29: 2295-2307. PMID: 34228341, PMCID: PMC9020793, DOI: 10.1007/s12350-021-02698-4.Peer-Reviewed Original ResearchConceptsObstructive coronary artery diseaseCoronary artery diseaseHigh-risk coronary artery diseaseMyocardial perfusion imagingPatient selection algorithmTriple-vessel coronary artery diseasePrediction of CADML thresholdsStress-first protocolInvasive coronary angiographyReceiver operator characteristic curveOperator characteristic curveMyocardial perfusion SPECTArtery diseaseCoronary angiographyAnterior descendingClinical variablesClinical algorithmReader diagnosisRest imagingPerfusion SPECTPerfusion imagingDiagnostic safetyRadiation doseCharacteristic curveA phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST)
Oikonomou EK, Van Dijk D, Parise H, Suchard MA, de Lemos J, Antoniades C, Velazquez EJ, Miller EJ, Khera R. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). European Heart Journal 2021, 42: 2536-2548. PMID: 33881513, PMCID: PMC8488385, DOI: 10.1093/eurheartj/ehab223.Peer-Reviewed Original ResearchMeSH KeywordsChest PainComputed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseHumansProspective StudiesConceptsStable chest painChest painPrimary endpointMajor adverse cardiovascular eventsNon-fatal myocardial infarctionAdverse cardiovascular eventsStudy's primary endpointCoronary artery diseaseClinical trial populationsCox regression modelParticipant-level dataSCOT-HEARTCardiovascular eventsCause mortalityHazard ratioPatients 5Artery diseaseFunctional testingPROMISE trialTrial populationMyocardial infarctionLower incidenceStudy populationPainCollected variables