2020
Neutrophil-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
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
2014
Uveal Melanoma Metastatic to the Liver: The Role of Quantitative Volumetric Contrast-Enhanced MR Imaging in the Assessment of Early Tumor Response after Transarterial Chemoembolization
Duran R, Chapiro J, Frangakis C, Lin M, Schlachter TR, Schernthaner RE, Wang Z, Savic LJ, Tacher V, Kamel IR, Geschwind JF. Uveal Melanoma Metastatic to the Liver: The Role of Quantitative Volumetric Contrast-Enhanced MR Imaging in the Assessment of Early Tumor Response after Transarterial Chemoembolization. Translational Oncology 2014, 7: 447-455. PMID: 24953419, PMCID: PMC4202794, DOI: 10.1016/j.tranon.2014.05.004.Peer-Reviewed Original ResearchFirst transarterial chemoembolizationEarly tumor responseTransarterial chemoembolizationWorld Health OrganizationOverall survivalTumor responseTarget lesionsTumor volumeTumor enhancementUveal melanomaContrast-enhanced magnetic resonance imagingNon-target lesionsUveal melanoma metastaticMedian overall survivalResponse Evaluation CriteriaKaplan-Meier methodLog-rank testMetastatic uveal melanomaMagnetic resonance imagingMelanoma metastaticSurrogate biomarkerSolid tumorsResonance imagingResponse criteriaLesions