2020
Prospective study of Lipiodol distribution as an imaging marker for doxorubicin pharmacokinetics during conventional transarterial chemoembolization of liver malignancies
Savic LJ, Chapiro J, Funai E, Bousabarah K, Schobert IT, Isufi E, Geschwind JH, Stark S, He P, Rudek MA, Perez Lozada JC, Ayyagari R, Pollak J, Schlachter T. Prospective study of Lipiodol distribution as an imaging marker for doxorubicin pharmacokinetics during conventional transarterial chemoembolization of liver malignancies. European Radiology 2020, 31: 3002-3014. PMID: 33063185, DOI: 10.1007/s00330-020-07380-w.Peer-Reviewed Original ResearchConceptsConventional transarterial chemoembolizationProspective clinical trialsTumor responseConventional TACEAdverse eventsTransarterial chemoembolizationLiver malignanciesClinical trialsMarker predictiveTumor volumeExact testChild-Pugh B classVolumetric tumor responseChild-Pugh BPeak concentrationNon-compartmental analysisFisher's exact testDose normalizationQuantitative European AssociationStandard non-compartmental analysisTime-concentration curveLiver criteriaLobar distributionDoxorubicin pharmacokineticsNeuroendocrine metastases
2019
Quantitative Imaging Biomarkers for 90Y Distribution on Bremsstrahlung SPECT After Resin-Based Radioembolization
Schobert I, Chapiro J, Nezami N, Hamm CA, Gebauer B, Lin M, Pollak J, Saperstein L, Schlachter T, Savic LJ. Quantitative Imaging Biomarkers for 90Y Distribution on Bremsstrahlung SPECT After Resin-Based Radioembolization. Journal Of Nuclear Medicine 2019, 60: 1066-1072. PMID: 30655331, PMCID: PMC6681698, DOI: 10.2967/jnumed.118.219691.Peer-Reviewed Original ResearchMeSH KeywordsAgedAngiographyBiomarkersCarcinoma, HepatocellularEmbolization, TherapeuticFeasibility StudiesFemaleHumansImaging, Three-DimensionalLiverLiver NeoplasmsMagnetic Resonance ImagingMaleMicrospheresMiddle AgedMultimodal ImagingPrognosisRegression AnalysisRetrospective StudiesTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedTreatment OutcomeYttrium RadioisotopesConceptsNon-HCC patientsTransarterial radioembolizationHepatocellular carcinomaBaseline imagingTumor responseTumor volumeChild-Pugh class B patientsBaseline imaging featuresClass B patientsNormal liver ratiosTotal tumor volumeSPECT/CTContrast-enhanced MRIQuantitative European AssociationMultiphasic contrast-enhanced MRIInstitutional review boardHigh TNRPreprocedural MRIChild-PughB patientsLiver criteriaTumor burdenClinical parametersHepatic malignanciesRetrospective study
2018
Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning—An Artificial Intelligence Concept
Abajian A, Murali N, Savic LJ, Laage-Gaupp FM, Nezami N, Duncan JS, Schlachter T, Lin M, Geschwind JF, Chapiro J. Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning—An Artificial Intelligence Concept. Journal Of Vascular And Interventional Radiology 2018, 29: 850-857.e1. PMID: 29548875, PMCID: PMC5970021, DOI: 10.1016/j.jvir.2018.01.769.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntineoplastic AgentsCarcinoma, HepatocellularChemoembolization, TherapeuticContrast MediaDoxorubicinEthiodized OilFemaleHumansLiver NeoplasmsMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeoplasm StagingPredictive Value of TestsRetrospective StudiesSensitivity and SpecificityTreatment OutcomeConceptsTransarterial chemoembolizationHepatocellular carcinomaTreatment responseLogistic regressionClinical patient dataPatient dataIntra-arterial therapyQuantitative European AssociationMagnetic resonance imagingLiver criteriaBaseline imagingClinical variablesTumor responseTherapeutic featuresTreatment respondersBaseline MRClinical informationImaging variablesChemoembolizationTherapeutic outcomesResonance imagingResponse criteriaEuropean AssociationPatientsMR imaging