2024
Tumor response assessment in hepatocellular carcinoma treated with immunotherapy: imaging biomarkers for clinical decision-making
Sobirey R, Matuschewski N, Gross M, Lin M, Kao T, Kasolowsky V, Strazzabosco M, Stein S, Savic L, Gebauer B, Jaffe A, Duncan J, Madoff D, Chapiro J. Tumor response assessment in hepatocellular carcinoma treated with immunotherapy: imaging biomarkers for clinical decision-making. European Radiology 2024, 1-11. PMID: 39033181, DOI: 10.1007/s00330-024-10955-6.Peer-Reviewed Original ResearchMedian overall survivalTumor response criteriaTumor response assessmentHepatocellular carcinoma patientsHepatocellular carcinomaTumor responseOverall survivalResponse criteriaResponse assessmentNon-respondersPoorer median overall survivalPrediction of tumor responsePredictive valueHepatocellular carcinoma immunotherapyDisease controlPrognostic of survivalClinical baseline parametersLog-rank testKaplan-Meier curvesMultivariate Cox regressionPredicting overall survivalCox regression modelsSurvival benefitStratify patientsMRI pre-
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 response
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