2024
Effect of Incomplete Cryoablation and Matrix Metalloproteinase Inhibition on Intratumoral CD8+ T-Cell Infiltration in Murine Hepatocellular Carcinoma.
Shewarega A, Santana J, Nam D, Berz A, Tefera J, Kahl V, Mishra S, Coman D, Duncan J, Roberts S, Wetter A, Madoff D, Chapiro J. Effect of Incomplete Cryoablation and Matrix Metalloproteinase Inhibition on Intratumoral CD8+ T-Cell Infiltration in Murine Hepatocellular Carcinoma. Radiology 2024, 310: e232365. PMID: 38349244, PMCID: PMC10902598, DOI: 10.1148/radiol.232365.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCarcinoma, HepatocellularCD8-Positive T-LymphocytesCryosurgeryFemaleLiver NeoplasmsMatrix Metalloproteinase InhibitorsMatrix MetalloproteinasesMiceConceptsT cell infiltrationCD8<sup>+</sup> T cellsMatrix metalloproteinase inhibitionT cellsHepatocellular carcinomaMatrix metalloproteinase inhibitorsMatrix metalloproteinasesResidual tumorCXCR3<sup>+</sup> CD8<sup>+</sup> T cellsCytotoxic CD8<sup>+</sup> T cell infiltrationIntratumoral CD8+ T cell infiltrationCD8+ T cell infiltrationCD8<sup>+</sup> T cell infiltrationMouse model of hepatocellular carcinomaEarly-stage hepatocellular carcinomaImage-guided tumor ablationUnpaired Student's <i>t</i> testModel of hepatocellular carcinomaFirst-line therapyMurine hepatocellular carcinomaT cell subsetsTumor-associated macrophagesMurine HCC modelLocal immune responseFemale BALB/c mice
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 ResearchAnatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration
Zhou B, Augenfeld Z, Chapiro J, Zhou SK, Liu C, Duncan JS. Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration. Medical Image Analysis 2021, 71: 102041. PMID: 33823397, PMCID: PMC8184611, DOI: 10.1016/j.media.2021.102041.Peer-Reviewed Original ResearchConceptsMultimodal registrationLiver segmentationLarge-scale manual annotationGround truthMultimodal image registrationMultimodal registration methodSegmentation networkDomain adaptationManual annotationSource modalityImage registrationRegistration frameworkSegmentationImage-guided interventionsRegistration methodMedical imagingDiagnostic medical imagingCorrect transformationLimited FOVStructure informationIntraprocedural CBCTImage qualitySegmenterExperimental resultsPatient dataDeep 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
Automated 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 responseMolecular MRI of the Immuno-Metabolic Interplay in a Rabbit Liver Tumor Model: A Biomarker for Resistance Mechanisms in Tumor-targeted Therapy?
Savic LJ, Doemel LA, Schobert IT, Montgomery RR, Joshi N, Walsh JJ, Santana J, Pekurovsky V, Zhang X, Lin M, Adam L, Boustani A, Duncan J, Leng L, Bucala RJ, Goldberg SN, Hyder F, Coman D, Chapiro J. Molecular MRI of the Immuno-Metabolic Interplay in a Rabbit Liver Tumor Model: A Biomarker for Resistance Mechanisms in Tumor-targeted Therapy? Radiology 2020, 296: 575-583. PMID: 32633675, PMCID: PMC7434651, DOI: 10.1148/radiol.2020200373.Peer-Reviewed Original ResearchConceptsImmuno-oncologic therapiesConventional transarterial chemoembolizationTransarterial chemoembolizationIntratumoral immune cell infiltrationMR spectroscopyRabbit liver tumor modelPrussian blue iron stainingAntigen-presenting immune cellsIntra-arterial infusionImmune cell infiltrationNew Zealand white rabbitsLiver tumor modelImmune cell exclusionLiver cancer modelContrast material administrationT2-weighted MRIZealand white rabbitsT2-weighted imagingResistance mechanismsImmunosuppressive tumorHLA-DRCell infiltrationImmune cellsImmunohistochemistry stainingRing enhancement
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 States
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 imaging
2017
The impact of antiangiogenic therapy combined with Transarterial Chemoembolization on enhancement based quantitative tumor response assessment in patients with hepatocellular carcinoma
Smolka S, Chapiro J, Manzano W, Treilhard J, Reiner E, Deng Y, Zhao Y, Hamm B, Duncan JS, Gebauer B, Lin M, Geschwind JF. The impact of antiangiogenic therapy combined with Transarterial Chemoembolization on enhancement based quantitative tumor response assessment in patients with hepatocellular carcinoma. Clinical Imaging 2017, 46: 1-7. PMID: 28668723, PMCID: PMC5720941, DOI: 10.1016/j.clinimag.2017.05.007.Peer-Reviewed Original ResearchConceptsEarly response assessmentTransarterial chemoembolizationImaging-based criteriaResponse assessmentHepatocellular carcinomaTumor response assessmentAnti-angiogenic therapyQuantitative European AssociationTherapy armOverall survivalLiver criteriaAntiangiogenic therapyTreatment groupsPatientsSimilar associationEuropean AssociationBevacizumabChemoembolizationCarcinomaTherapyAssociationAssessmentFollowCriteriaBaseline