2022
Impact of Chemoembolic Regimen on Immune Cell Recruitment and Immune Checkpoint Marker Expression following Transcatheter Arterial Chemoembolization in a VX2 Rabbit Liver Tumor Model
Berz AM, Santana JG, Iseke S, Gross M, Pekurovsky V, Laage Gaupp F, Savic LJ, Borde T, Gottwald LA, Boustani AM, Gebauer B, Lin M, Zhang X, Schlachter T, Madoff DC, Chapiro J. Impact of Chemoembolic Regimen on Immune Cell Recruitment and Immune Checkpoint Marker Expression following Transcatheter Arterial Chemoembolization in a VX2 Rabbit Liver Tumor Model. Journal Of Vascular And Interventional Radiology 2022, 33: 764-774.e4. PMID: 35346859, PMCID: PMC9344951, DOI: 10.1016/j.jvir.2022.03.026.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAntibiotics, AntineoplasticBicarbonatesCarcinoma, HepatocellularChemoembolization, TherapeuticDoxorubicinLiver NeoplasmsProgrammed Cell Death 1 ReceptorRabbitsConceptsTranscatheter arterial chemoembolizationCytotoxic T-lymphocyte-associated protein 4Rabbit liver tumor modelConventional TACEImmune checkpoint marker expressionLiver tumor modelVX2 rabbit liver tumor modelArterial chemoembolizationBicarbonate infusionImmune responseDifferentiation 3T-lymphocyte-associated protein 4Conventional transcatheter arterial chemoembolizationTumor modelCell death protein 1Marker expressionIntratumoral T cellsImmune checkpoint markersT cell infiltrationDeath protein 1Antigen-presenting cellsImmune cell recruitmentNew Zealand white rabbitsZealand white rabbitsAPC infiltration
2021
Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach
Malpani R, Petty CW, Yang J, Bhatt N, Zeevi T, Chockalingam V, Raju R, Petukhova-Greenstein A, Santana JG, Schlachter TR, Madoff DC, Chapiro J, Duncan J, Lin M. Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach. Journal Of Vascular And Interventional Radiology 2021, 33: 324-332.e2. PMID: 34923098, PMCID: PMC8972393, DOI: 10.1016/j.jvir.2021.12.017.Peer-Reviewed Original ResearchComparison of metabolic and immunologic responses to transarterial chemoembolization with different chemoembolic regimens in a rabbit VX2 liver tumor model
Doemel LA, Santana JG, Savic LJ, Gaupp FML, Borde T, Petukhova-Greenstein A, Kucukkaya AS, Schobert IT, Hamm CA, Gebauer B, Walsh JJ, Rexha I, Hyder F, Lin M, Madoff DC, Schlachter T, Chapiro J, Coman D. Comparison of metabolic and immunologic responses to transarterial chemoembolization with different chemoembolic regimens in a rabbit VX2 liver tumor model. European Radiology 2021, 32: 2437-2447. PMID: 34718844, PMCID: PMC9359419, DOI: 10.1007/s00330-021-08337-3.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCarcinoma, HepatocellularChemoembolization, TherapeuticDoxorubicinEthiodized OilLiver NeoplasmsRabbitsTumor MicroenvironmentConceptsDrug-eluting embolicsImmune cell infiltrationAntigen-presenting cellsBicarbonate infusionConventional TACEDEE-TACETransarterial chemotherapyCell infiltrationImmune cellsPeritumoral infiltrationT lymphocytesIntratumoral immune cell infiltrationTumor modelRabbit VX2 liver tumor modelImmunological tumor microenvironmentVX2 liver tumor modelIntra-arterial therapyIntra-arterial treatmentRabbit VX2 tumor modelLiver tumor modelTumor-bearing rabbitsVX2 tumor modelMann-Whitney U testIntratumoral presenceTransarterial chemoembolizationLipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial
Letzen BS, Malpani R, Miszczuk M, de Ruiter QMB, Petty CW, Rexha I, Nezami N, Laage-Gaupp F, Lin M, Schlachter TR, Chapiro J. Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial. Clinical Imaging 2021, 78: 194-200. PMID: 34022765, PMCID: PMC8364875, DOI: 10.1016/j.clinimag.2021.05.007.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkersCarcinoma, HepatocellularChemoembolization, TherapeuticEthiodized OilHumansLiver NeoplasmsProspective StudiesRetrospective StudiesTreatment OutcomeConceptsConventional trans-arterial chemoembolizationMedian overall survivalProspective clinical trialsLipiodol depositionTumor responsePredictive biomarkersClinical trialsModified Response Evaluation CriteriaPost-TACE CTResponse Evaluation CriteriaMetastatic liver cancerKaplan-Meier analysisTrans-arterial chemoembolizationTumor response criteriaLiver tumor responsePrediction of survivalSelective drug targetingArterial embolizationLiver metastasesOverall survivalBland-Altman plotsTransarterial chemoembolizationPortal veinTumor respondersHepatocellular carcinomaThermal ablation alone vs thermal ablation combined with transarterial chemoembolization for patients with small (<3 cm) hepatocellular carcinoma
Chai NX, Chapiro J, Petukhova A, Gross M, Kucukkaya A, Raju R, Zeevi T, Elbanan M, Lin M, Perez-Lozada JC, Schlachter T, Strazzabosco M, Pollak JS, Madoff DC. Thermal ablation alone vs thermal ablation combined with transarterial chemoembolization for patients with small (<3 cm) hepatocellular carcinoma. Clinical Imaging 2021, 76: 123-129. PMID: 33592550, PMCID: PMC8217099, DOI: 10.1016/j.clinimag.2021.01.043.Peer-Reviewed Original ResearchMeSH KeywordsAgedCarcinoma, HepatocellularChemoembolization, TherapeuticCombined Modality TherapyFemaleHumansLiver NeoplasmsMaleMiddle AgedRetrospective StudiesTreatment OutcomeConceptsOverall survivalTransarterial chemoembolizationHepatocellular carcinomaThermal ablationTA groupEarly-stage hepatocellular carcinomaMedian overall survivalTherapy-naïve patientsKaplan-Meier analysisMaximum tumor diameterStage hepatocellular carcinomaLog-rank testDrug-eluting beadsSmall hepatocellular carcinomaTerms of TTPHIPAA-compliant IRBSignificant differencesLipiodol-TACELocoregional therapyBCLC stageComplication rateTreatment cohortsTumor diameterAFP levelsPatient groupDeep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver
Oestmann PM, Wang CJ, Savic LJ, Hamm CA, Stark S, Schobert I, Gebauer B, Schlachter T, Lin M, Weinreb JC, Batra R, Mulligan D, Zhang X, Duncan JS, Chapiro J. Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver. European Radiology 2021, 31: 4981-4990. PMID: 33409782, PMCID: PMC8222094, DOI: 10.1007/s00330-020-07559-1.Peer-Reviewed Original ResearchMeSH KeywordsCarcinoma, HepatocellularContrast MediaDeep LearningHumansLiver NeoplasmsMagnetic Resonance ImagingRetrospective StudiesConceptsNon-HCC lesionsHepatocellular carcinomaHCC lesionsAtypical imagingGrading systemLI-RADS criteriaAtypical imaging featuresPrimary liver cancerTypical hepatocellular carcinomaAtypical hepatocellular carcinomaContrast-enhanced MRISensitivity/specificityLiver transplantMethodsThis IRBRetrospective studyLiver malignanciesImaging featuresLiver cancerAtypical featuresConclusionThis studyLesionsMRIClinical applicationCarcinomaImage-based diagnosis
2020
Reliable prediction of survival in advanced-stage hepatocellular carcinoma treated with sorafenib: comparing 1D and 3D quantitative tumor response criteria on MRI
Doemel LA, Chapiro J, Laage Gaupp F, Savic LJ, Kucukkaya AS, Petukhova A, Tefera J, Zeevi T, Lin M, Schlachter T, Jaffe A, Strazzabosco M, Patel T, Stein SM. Reliable prediction of survival in advanced-stage hepatocellular carcinoma treated with sorafenib: comparing 1D and 3D quantitative tumor response criteria on MRI. European Radiology 2020, 31: 2737-2746. PMID: 33123796, PMCID: PMC8043967, DOI: 10.1007/s00330-020-07381-9.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic AgentsCarcinoma, HepatocellularHumansLiver NeoplasmsMagnetic Resonance ImagingPhenylurea CompoundsRetrospective StudiesSorafenibTreatment OutcomeConceptsTumor response criteriaOverall survivalAdvanced-stage HCCDisease progressionSorafenib therapyDisease controlResponse criteriaCox proportional hazards regression modelAdvanced-stage hepatocellular carcinomaProportional hazards regression modelsDCE-MRIInitiation of sorafenibTumor response analysisMultivariable Cox regressionIndependent risk factorMethodsThis retrospective analysisIndependent prognostic factorInitiation of treatmentKaplan-Meier analysisKaplan-Meier curvesHazards regression modelsLog-rank testStratification of patientsTotal tumor volumeArterial phase MRIAutomated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer
Stark S, Wang C, Savic LJ, Letzen B, Schobert I, Miszczuk M, Murali N, Oestmann P, Gebauer B, Lin M, Duncan J, Schlachter T, Chapiro J. Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer. Scientific Reports 2020, 10: 18026. PMID: 33093524, PMCID: PMC7582153, DOI: 10.1038/s41598-020-75120-7.Peer-Reviewed Original ResearchConceptsConventional transarterial chemoembolizationLipiodol depositionTransarterial chemoembolizationLiver cancerPeripheral depositionLipiodol depositsTherapeutic efficacyNecrotic tumor areasBaseline MRITherapy optionsTumor responseTreatment responseTumor volumeLiver lesionsLipiodolH postTumor areaH-CTHounsfield unitsBiomarkersChemoembolizationHigh rateTumorsCancerImproved responseProspective 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 ResearchMeSH KeywordsCarcinoma, HepatocellularChemoembolization, TherapeuticDoxorubicinEthiodized OilHumansLiver NeoplasmsProspective StudiesTreatment OutcomeConceptsConventional 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 metastasesAutomated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning
Bousabarah K, Letzen B, Tefera J, Savic L, Schobert I, Schlachter T, Staib LH, Kocher M, Chapiro J, Lin M. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology 2020, 46: 216-225. PMID: 32500237, PMCID: PMC7714704, DOI: 10.1007/s00261-020-02604-5.Peer-Reviewed Original ResearchMeSH KeywordsCarcinoma, HepatocellularDeep LearningHumansLiver NeoplasmsMagnetic Resonance ImagingRetrospective StudiesConceptsDeep convolutional neural networkAverage false positive rateDice similarity coefficientU-NetDeep learning algorithmsConvolutional neural networkTest setMean Dice similarity coefficientRandom forest classifierDCNN methodDCNN approachDeep learningNet architectureLearning algorithmNeural networkLiver segmentationManual 3D segmentationForest classifierGround truthManual segmentationFalse positive rateCorresponding segmentationSegmentationMultiphasic contrast-enhanced MRIThresholdingNeutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictors of tumor response in hepatocellular carcinoma after DEB-TACE
Schobert IT, Savic LJ, Chapiro J, Bousabarah K, Chen E, Laage-Gaupp F, Tefera J, Nezami N, Lin M, Pollak J, Schlachter T. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictors of tumor response in hepatocellular carcinoma after DEB-TACE. European Radiology 2020, 30: 5663-5673. PMID: 32424595, PMCID: PMC7483919, DOI: 10.1007/s00330-020-06931-5.Peer-Reviewed Original ResearchMeSH KeywordsAgedBlood PlateletsCarcinoma, HepatocellularChemoembolization, TherapeuticFemaleHumansInflammationKaplan-Meier EstimateLiver NeoplasmsLymphocytesMagnetic Resonance ImagingMaleMiddle AgedMultivariate AnalysisNeutrophilsPrognosisProgression-Free SurvivalProportional Hazards ModelsRetrospective StudiesTreatment OutcomeConceptsProgression-free survivalTreatment-naïve hepatocellular carcinomaShorter progression-free survivalPoor tumor responseDEB-TACELymphocyte ratioTumor responseHepatocellular carcinomaMagnetic resonance imagingTumor growthInflammatory biomarkersDrug-eluting bead transarterial chemoembolizationContrast-enhanced magnetic resonance imagingHigher baseline NLRHigher baseline plateletsRadiomic featuresVolumetric tumor responseLoco-regional therapyAlpha-fetoprotein levelsBead transarterial chemoembolizationKaplan-Meier analysisMethodsThis retrospective studyDifferential blood countQuantitative European AssociationNodular tumor growth
2019
Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features
Wang CJ, Hamm CA, Savic LJ, Ferrante M, Schobert I, Schlachter T, Lin M, Weinreb JC, Duncan JS, Chapiro J, Letzen B. Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. European Radiology 2019, 29: 3348-3357. PMID: 31093705, PMCID: PMC7243989, DOI: 10.1007/s00330-019-06214-8.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAlgorithmsBile Duct NeoplasmsBile Ducts, IntrahepaticCarcinoma, HepatocellularCholangiocarcinomaDeep LearningFemaleHumansImage Interpretation, Computer-AssistedLiver NeoplasmsMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeural Networks, ComputerPredictive Value of TestsProof of Concept StudyRetrospective StudiesConceptsDeep learning systemConvolutional neural networkLearning systemRelevance scoresFeature mapsPre-trained CNN modelsFeature relevance scoresMulti-phasic MRINeural network interpretationEvidence-based decision supportDeep NeuralDeep learningCNN modelLesion classifierLearning prototypeNeural networkOriginal imageSystem prototypeDecision supportLesion classificationNetwork interpretationImage voxelsIncorrect featuresLesion classesTest setDeep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI
Hamm CA, Wang CJ, Savic LJ, Ferrante M, Schobert I, Schlachter T, Lin M, Duncan JS, Weinreb JC, Chapiro J, Letzen B. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. European Radiology 2019, 29: 3338-3347. PMID: 31016442, PMCID: PMC7251621, DOI: 10.1007/s00330-019-06205-9.Peer-Reviewed Original ResearchAdultAgedBile Duct NeoplasmsBile Ducts, IntrahepaticCarcinoma, HepatocellularCholangiocarcinomaDeep LearningFemaleHumansImage Interpretation, Computer-AssistedLiver NeoplasmsMagnetic Resonance ImagingMaleMiddle AgedNeural Networks, ComputerReproducibility of ResultsROC CurveSensitivity and SpecificityUnited StatesQuantitative 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 Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma.
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 Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma. Journal Of Visualized Experiments 2018 PMID: 30371657, PMCID: PMC6235502, DOI: 10.3791/58382.Peer-Reviewed Original ResearchMeSH KeywordsCarcinoma, HepatocellularHumansInjections, Intra-ArterialLiver NeoplasmsMachine LearningMaleMiddle AgedSurgery, Computer-AssistedConceptsIntra-arterial therapyN patientsHepatocellular carcinomaTrans-arterial therapiesIntra-arterial treatmentCohort of patientsStandard of careLikelihood of responseClinical research questionsSurgical resectionNew patientsTreatment responseUnivariate associationsPatientsTraining patientsInterventional radiologyTherapyCarcinomaTreatmentImage-guided therapyOutcomesFinal modelImaging dataResectionResponsePredicting 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 imagingSensitivity and Reproducibility of Automated Feeding Artery Detection Software during Transarterial Chemoembolization of Hepatocellular Carcinoma
Chiaradia M, Izamis ML, Radaelli A, Prevoo W, Maleux G, Schlachter T, Mayer J, Luciani A, Kobeiter H, Tacher V. Sensitivity and Reproducibility of Automated Feeding Artery Detection Software during Transarterial Chemoembolization of Hepatocellular Carcinoma. Journal Of Vascular And Interventional Radiology 2018, 29: 425-431. PMID: 29402612, DOI: 10.1016/j.jvir.2017.10.025.Peer-Reviewed Original Research
2016
Advanced-stage hepatocellular carcinoma with portal vein thrombosis: conventional versus drug-eluting beads transcatheter arterial chemoembolization
Gorodetski B, Chapiro J, Schernthaner R, Duran R, Lin M, Lee H, Lenis D, Stuart EA, Nonyane BA, Pekurovsky V, Tamrazi A, Gebauer B, Schlachter T, Pawlik TM, Geschwind JF. Advanced-stage hepatocellular carcinoma with portal vein thrombosis: conventional versus drug-eluting beads transcatheter arterial chemoembolization. European Radiology 2016, 27: 526-535. PMID: 27277261, PMCID: PMC5470590, DOI: 10.1007/s00330-016-4445-9.Peer-Reviewed Original ResearchConceptsMedian overall survivalPortal venous thrombosisAdvanced-stage hepatocellular carcinomaConventional TACEDEB-TACEHepatocellular carcinomaOverall survivalAdverse eventsBeads TACEDrug-eluting bead transcatheter arterial chemoembolizationConventional trans-arterial chemoembolizationPropensity scoreDrug-eluting beads TACECommon adverse eventsConclusionOur retrospective studyEqual safety profileChild-Pugh classMethodsThis retrospective analysisPortal vein thrombosisPost-embolization syndromeTranscatheter arterial chemoembolizationStage hepatocellular carcinomaTrans-arterial chemoembolizationSub-group analysisArterial chemoembolization