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
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 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
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
2016
CMR-Verified Lower LA Strain in the Presence of Regional Atrial Fibrosis in Atrial Fibrillation
Peters DC, Duncan JS, Grunseich K, Marieb MA, Cornfeld D, Sinusas AJ, Chelikani S. CMR-Verified Lower LA Strain in the Presence of Regional Atrial Fibrosis in Atrial Fibrillation. JACC Cardiovascular Imaging 2016, 10: 207-208. PMID: 27085430, PMCID: PMC5600154, DOI: 10.1016/j.jcmg.2016.01.015.Peer-Reviewed Original Research
2010
Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery
DeLorenzo C, Papademetris X, Staib LH, Vives KP, Spencer DD, Duncan JS. Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery. IEEE Transactions On Medical Imaging 2010, 29: 322-338. PMID: 20129844, PMCID: PMC2824434, DOI: 10.1109/tmi.2009.2027993.Peer-Reviewed Original ResearchConceptsDeformation estimationSurface deformationBrain surface deformationSurface deformation estimationPreoperative brain imagesCortical surface deformationSurface trackingCamera calibration parametersDisplacement errorStereo vision systemBrain deformationDeformationCalibration parametersBiomechanical modelIntraoperative brainCalibration errorsPhysical processesVision systemVivo casesCamera calibrationStereo systemInitial conditionsImage acquisitionErrorEstimation
2008
Effects of the Brain-Derived Neurotrophic Growth Factor Val66Met Variation on Hippocampus Morphology in Bipolar Disorder
Chepenik LG, Fredericks C, Papademetris X, Spencer L, Lacadie C, Wang F, Pittman B, Duncan JS, Staib LH, Duman RS, Gelernter J, Blumberg HP. Effects of the Brain-Derived Neurotrophic Growth Factor Val66Met Variation on Hippocampus Morphology in Bipolar Disorder. Neuropsychopharmacology 2008, 34: 944-951. PMID: 18704093, PMCID: PMC2837582, DOI: 10.1038/npp.2008.107.Peer-Reviewed Original ResearchConceptsSmaller hippocampus volumesHippocampus volumeBipolar disorderBDNF genotypeBD diagnosisMood disorder pathophysiologyBDNF Val66Met polymorphismHigh-resolution magnetic resonanceHealthy comparison subjectsVal/Val homozygotesEffect of diagnosisLinear mixed model analysisVal66Met polymorphismGrowth factor proteinBD subgroupsDisorder pathophysiologyHC subjectsHippocampal developmentComparison subjectsMixed model analysisHippocampus structureBDNFHippocampus morphologyAnterior hippocampusVal homozygotes
1997
Positron Emission Tomography Measurement of Cerebral Metabolic Correlates of Yohimbine Administration in Combat-Related Posttraumatic Stress Disorder
Bremner JD, Innis RB, Ng CK, Staib LH, Salomon RM, Bronen RA, Duncan J, Southwick SM, Krystal JH, Rich D, Zubal G, Dey H, Soufer R, Charney DS. Positron Emission Tomography Measurement of Cerebral Metabolic Correlates of Yohimbine Administration in Combat-Related Posttraumatic Stress Disorder. JAMA Psychiatry 1997, 54: 246-254. PMID: 9075465, DOI: 10.1001/archpsyc.1997.01830150070011.Peer-Reviewed Original ResearchConceptsPosttraumatic stress disorderAdministration of yohimbineNorepinephrine releaseHealthy subjectsHealthy age-matched control subjectsAge-matched control subjectsStress disorderDouble-blind fashionPositron emission tomography (PET) measurementsBrain metabolic responsesCerebral metabolic correlatesBrain norepinephrine releaseSymptoms of anxietyPositron emission tomographyBrain metabolismControl subjectsYohimbine administrationPreclinical studiesMetabolic correlatesCaudate nucleusPatientsYohimbineVietnam combat veteransEmission tomographyOrbitofrontal cortex