2022
Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI.
Borde T, Nezami N, Laage Gaupp F, Savic LJ, Taddei T, Jaffe A, Strazzabosco M, Lin M, Duran R, Georgiades C, Hong K, Chapiro J. Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI. Radiology 2022, 304: 228-237. PMID: 35412368, PMCID: PMC9270683, DOI: 10.1148/radiol.212426.Peer-Reviewed Original ResearchConceptsMedian overall survivalAdvanced-stage hepatocellular carcinomaTransarterial chemoembolizationHepatocellular carcinomaBCLC BBCLC COverall survivalTumor burdenBarcelona Clinic Liver Cancer (BCLC) staging systemLiver Cancer staging systemCancer (AJCC) staging systemConventional transarterial chemoembolizationDrug-eluting beadsAllocation of patientsContrast-enhanced MRIBackground PatientsSurvival benefitRetrospective studyStaging systemC tumorsTumor volumePatientsHeterogeneous patientsMonthsChemoembolization
2021
Role of 3D quantitative tumor analysis for predicting overall survival after conventional chemoembolization of intrahepatic cholangiocarcinoma
Rexha I, Laage-Gaupp F, Chapiro J, Miszczuk MA, van Breugel JMM, Lin M, Konstantinidis M, Duran R, Gebauer B, Georgiades C, Hong K, Nezami N. Role of 3D quantitative tumor analysis for predicting overall survival after conventional chemoembolization of intrahepatic cholangiocarcinoma. Scientific Reports 2021, 11: 9337. PMID: 33927226, PMCID: PMC8085245, DOI: 10.1038/s41598-021-88426-x.Peer-Reviewed Original ResearchConceptsTotal tumor volumeConventional transarterial chemoembolizationTumor diameterIntrahepatic cholangiocarcinomaOverall survivalTumor areaICC patientsTumor volumeHigh tumor burden groupTumor analysisOS of patientsHazard ratioTransarterial chemoembolizationTumor burdenBurden groupConventional chemoembolizationHTB groupRetrospective analysisPatientsSurvival curvesMultivariate analysisChemoembolizationCholangiocarcinomaETVBaseline images
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 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 ResearchConceptsIntra-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