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
Irinotecan-Eluting 75–150-μm Embolics Lobar Chemoembolization in Patients with Colorectal Cancer Liver Metastases: A Prospective Single-Center Phase I Study
Fereydooni A, Letzen B, Ghani MA, Miszczuk MA, Huber S, Chapiro J, Schlachter T, Geschwind JF, Georgiades C. Irinotecan-Eluting 75–150-μm Embolics Lobar Chemoembolization in Patients with Colorectal Cancer Liver Metastases: A Prospective Single-Center Phase I Study. Journal Of Vascular And Interventional Radiology 2018, 29: 1646-1653.e5. PMID: 30337148, DOI: 10.1016/j.jvir.2018.08.010.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAntineoplastic AgentsBiomarkers, TumorChemoembolization, TherapeuticColorectal NeoplasmsConnecticutDrug CarriersFeasibility StudiesFemaleHumansIrinotecanLiver NeoplasmsMaleMicrospheresMiddle AgedPalliative CareProspective StudiesTime FactorsTomography, X-Ray ComputedTreatment OutcomeVascular Endothelial Growth Factor AVascular Endothelial Growth Factor Receptor-1Vascular Endothelial Growth Factor Receptor-2ConceptsVascular endothelial growth factor receptor 1Transarterial chemoembolizationAdverse eventsMetastatic diseaseObjective responseColorectal cancer liver metastasesLiver-dominant metastatic diseaseMetastatic colorectal cancer refractorySingle-center phase IColorectal cancer refractoryLines of chemotherapyMedian overall survivalPrimary end pointCancer liver metastasesResponse Evaluation CriteriaOnly grade 3Drug-eluting embolicsImaging-based criteriaGrowth factor receptor 1Factor receptor 1World Health OrganizationCancer refractoryAbdominal painSystemic chemotherapyLiver metastasesPredicting 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
Intra-arterial therapy of neuroendocrine tumour liver metastases: comparing conventional TACE, drug-eluting beads TACE and yttrium-90 radioembolisation as treatment options using a propensity score analysis model
Do Minh D, Chapiro J, Gorodetski B, Huang Q, Liu C, Smolka S, Savic LJ, Wainstejn D, Lin M, Schlachter T, Gebauer B, Geschwind JF. Intra-arterial therapy of neuroendocrine tumour liver metastases: comparing conventional TACE, drug-eluting beads TACE and yttrium-90 radioembolisation as treatment options using a propensity score analysis model. European Radiology 2017, 27: 4995-5005. PMID: 28677067, PMCID: PMC5675796, DOI: 10.1007/s00330-017-4856-2.Peer-Reviewed Original ResearchConceptsMultivariate Cox proportional hazards modelConventional transarterial chemoembolisationMedian overall survivalPropensity score analysisHepatic progression-free survivalDrug-eluting beads TACEYttrium-90 radioembolisationProgression-free survivalDEB-TACELiver metastasesOverall survivalBeads TACENeuroendocrine tumor liver metastasesWorld Health Organization criteriaCox proportional hazards modelIntra-arterial therapyMethodsThis retrospective analysisResponse Evaluation CriteriaScore analysisSignificant survival benefitLonger overall survivalGastroenteropancreatic neuroendocrine tumorsLog-rank testEntire study populationProportional hazards model
2011
AIRP Best Cases in Radiologic-Pathologic Correlation: Synovial Chondrosarcoma
Schlachter TR, Wu Q, Matlyuk-Urman Z. AIRP Best Cases in Radiologic-Pathologic Correlation: Synovial Chondrosarcoma. RadioGraphics 2011, 31: 1883-1888. PMID: 22084177, DOI: 10.1148/rg.317105210.Peer-Reviewed Original Research