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 responseIdarubicin-Loaded ONCOZENE Drug-Eluting Bead Chemoembolization in a Rabbit Liver Tumor Model: Investigating Safety, Therapeutic Efficacy, and Effects on Tumor Microenvironment
Borde T, Gaupp F, Geschwind JF, Savic LJ, Miszczuk M, Rexha I, Adam L, Walsh JJ, Huber S, Duncan JS, Peters DC, Sinusas A, Schlachter T, Gebauer B, Hyder F, Coman D, van Breugel JMM, Chapiro J. Idarubicin-Loaded ONCOZENE Drug-Eluting Bead Chemoembolization in a Rabbit Liver Tumor Model: Investigating Safety, Therapeutic Efficacy, and Effects on Tumor Microenvironment. Journal Of Vascular And Interventional Radiology 2020, 31: 1706-1716.e1. PMID: 32684417, PMCID: PMC7541537, DOI: 10.1016/j.jvir.2020.04.010.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAntibiotics, AntineoplasticBiosensing TechniquesCell Line, TumorCell ProliferationChemoembolization, TherapeuticDiffusion Magnetic Resonance ImagingHydrogen-Ion ConcentrationIdarubicinLiver Neoplasms, ExperimentalMaleMicrospheresMultidetector Computed TomographyParticle SizeRabbitsTumor MicroenvironmentConceptsMultiparametric magnetic resonanceRabbit liver tumor modelDiffusion-weighted imagingLiver tumor modelDEE chemoembolizationDrug-eluting embolic transarterial chemoembolizationTumor microenvironmentTumor modelMale New Zealand white rabbitsTumor acidosisNew Zealand white rabbitsVX2 liver tumorsZealand white rabbitsLaboratory parametersTransarterial chemoembolizationBead chemoembolizationMultiparametric MRDCE MR imagingLiver enzymesPostprocedural increaseIntratumoral hypoxiaLiver tumorsEntire lesionTherapeutic mechanismChemoembolizationMolecular MRI of the Immuno-Metabolic Interplay in a Rabbit Liver Tumor Model: A Biomarker for Resistance Mechanisms in Tumor-targeted Therapy?
Savic LJ, Doemel LA, Schobert IT, Montgomery RR, Joshi N, Walsh JJ, Santana J, Pekurovsky V, Zhang X, Lin M, Adam L, Boustani A, Duncan J, Leng L, Bucala RJ, Goldberg SN, Hyder F, Coman D, Chapiro J. Molecular MRI of the Immuno-Metabolic Interplay in a Rabbit Liver Tumor Model: A Biomarker for Resistance Mechanisms in Tumor-targeted Therapy? Radiology 2020, 296: 575-583. PMID: 32633675, PMCID: PMC7434651, DOI: 10.1148/radiol.2020200373.Peer-Reviewed Original ResearchConceptsImmuno-oncologic therapiesConventional transarterial chemoembolizationTransarterial chemoembolizationIntratumoral immune cell infiltrationMR spectroscopyRabbit liver tumor modelPrussian blue iron stainingAntigen-presenting immune cellsIntra-arterial infusionImmune cell infiltrationNew Zealand white rabbitsLiver tumor modelImmune cell exclusionLiver cancer modelContrast material administrationT2-weighted MRIZealand white rabbitsT2-weighted imagingResistance mechanismsImmunosuppressive tumorHLA-DRCell infiltrationImmune cellsImmunohistochemistry stainingRing enhancementMolecular Imaging of Extracellular Tumor pH to Reveal Effects of Locoregional Therapy on Liver Cancer Microenvironment
Savic LJ, Schobert I, Peters D, Walsh JJ, Laage-Gaupp F, Hamm CA, Tritz N, Doemel LA, Lin M, Sinusas A, Schlachter T, Duncan JS, Hyder F, Coman D, Chapiro J. Molecular Imaging of Extracellular Tumor pH to Reveal Effects of Locoregional Therapy on Liver Cancer Microenvironment. Clinical Cancer Research 2020, 26: 428-438. PMID: 31582517, PMCID: PMC7244230, DOI: 10.1158/1078-0432.ccr-19-1702.Peer-Reviewed Original ResearchConceptsMR spectroscopic imagingLocoregional therapyLiver cancer microenvironmentConventional transarterial chemoembolizationNew Zealand white rabbitsTumor pHMost liver tumorsZealand white rabbitsMolecular imaging paradigmsPositive therapeutic outcomesTumor residualsTransarterial chemoembolizationTumor devascularizationHistopathologic markersViable tumorSurrogate biomarkerLiver tumorsLiver cancerTumor enhancementLiver parenchymaMetabolic markersMultiparametric MRITherapeutic outcomesHIF-1αVX2 tumors
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
Brain responses to biological motion predict treatment outcome in young children with autism
Yang D, Pelphrey KA, Sukhodolsky DG, Crowley MJ, Dayan E, Dvornek NC, Venkataraman A, Duncan J, Staib L, Ventola P. Brain responses to biological motion predict treatment outcome in young children with autism. Translational Psychiatry 2016, 6: e948-e948. PMID: 27845779, PMCID: PMC5314125, DOI: 10.1038/tp.2016.213.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderYoung childrenSocial information processingMultivariate pattern analysisMotivation/rewardBiological motionCore deficitComplex neurodevelopmental disorderBrain responsesResponse treatmentSpectrum disorderNeurobiological markersNeural predictorsInformation processingBehavioral interventionsIndividual childrenNeurodevelopmental disordersCurrent findingsNeural circuitsBehavioral deficitsEarly childhoodChildrenUnsuccessful interventionsNeurobiomarkersPattern analysisPivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder
Venkataraman A, Yang D, Dvornek N, Staib LH, Duncan JS, Pelphrey KA, Ventola P. Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder. Neuroreport 2016, 27: 1081-1085. PMID: 27532879, PMCID: PMC5007196, DOI: 10.1097/wnr.0000000000000662.Peer-Reviewed Original ResearchConceptsPivotal Response TreatmentAutism spectrum disorderOccipital-temporal cortexAttentional systemResponse treatmentSpectrum disorderOrbitofrontal cortexPosterior cingulateHigh-level objectsBehavioral interventionsLearning mechanismPerception shiftProcessing areasNeural circuitsFunctional rewiringCortexTreatment regimenAutismInterventionNovel Bayesian frameworkCingulateFunctional changesIndividualsDisordersObjectsCMR-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
2015
An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism
Venkataraman A, Duncan JS, Yang D, Pelphrey KA. An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism. NeuroImage Clinical 2015, 8: 356-366. PMID: 26106561, PMCID: PMC4474177, DOI: 10.1016/j.nicl.2015.04.021.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderAutism Brain Imaging Data ExchangeSuperior temporal sulcusMiddle temporal gyrusTemporal sulcusTemporal gyrusRight posterior superior temporal sulcusPosterior superior temporal sulcusFunctional magnetic resonance imaging studyFunctional connectomicsTemporo-parietal junctionResting-state functional magnetic resonance imaging studyRight temporal poleIntrinsic functional networksDefault mode networkPossible neural mechanismsPosterior cingulate cortexMeta-analytic databaseIntra-hemispheric connectivityInter-hemispheric connectivityMagnetic resonance imaging studyASD patientsResonance imaging studyNeural mechanismsSpectrum disorder
2014
Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography
Compas C, Wong EY, Huang X, Sampath S, Lin BA, Pal P, Papademetris X, Thiele K, Dione DP, Stacy M, Staib LH, Sinusas AJ, O'Donnell M, Duncan JS. Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography. IEEE Transactions On Medical Imaging 2014, 33: 1275-1289. PMID: 24893257, PMCID: PMC4283552, DOI: 10.1109/tmi.2014.2308894.Peer-Reviewed Original Research
2012
Assessment of left ventricular 2D flow pathlines during early diastole using spatial modulation of magnetization with polarity alternating velocity encoding: A study in normal volunteers and canine animals with myocardial infarction
Zhang Z, Friedman D, Dione DP, Lin BA, Duncan JS, Sinusas AJ, Sampath S. Assessment of left ventricular 2D flow pathlines during early diastole using spatial modulation of magnetization with polarity alternating velocity encoding: A study in normal volunteers and canine animals with myocardial infarction. Magnetic Resonance In Medicine 2012, 70: 766-775. PMID: 23044637, PMCID: PMC3844046, DOI: 10.1002/mrm.24517.Peer-Reviewed Original Research
2011
An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy
Lu C, Chelikani S, Papademetris X, Knisely JP, Milosevic MF, Chen Z, Jaffray DA, Staib LH, Duncan JS. An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy. Medical Image Analysis 2011, 15: 772-785. PMID: 21646038, PMCID: PMC3164526, DOI: 10.1016/j.media.2011.05.010.Peer-Reviewed Original ResearchAlgorithmsBayes TheoremFemaleHumansImaging, Three-DimensionalMaleProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueSystems IntegrationTomography, X-Ray ComputedUterine Cervical NeoplasmsTargeted Imaging of the Spatial and Temporal Variation of Matrix Metalloproteinase Activity in a Porcine Model of Postinfarct Remodeling
Sahul ZH, Mukherjee R, Song J, McAteer J, Stroud RE, Dione DP, Staib L, Papademetris X, Dobrucki LW, Duncan JS, Spinale FG, Sinusas AJ. Targeted Imaging of the Spatial and Temporal Variation of Matrix Metalloproteinase Activity in a Porcine Model of Postinfarct Remodeling. Circulation Cardiovascular Imaging 2011, 4: 381-391. PMID: 21505092, PMCID: PMC3140564, DOI: 10.1161/circimaging.110.961854.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 acquisitionErrorEstimationIntegrated Segmentation and Nonrigid Registration for Application in Prostate Image-Guided Radiotherapy
Lu C, Chelikani S, Chen Z, Papademetris X, Staib LH, Duncan JS. Integrated Segmentation and Nonrigid Registration for Application in Prostate Image-Guided Radiotherapy. Lecture Notes In Computer Science 2010, 13: 53-60. PMID: 20879214, DOI: 10.1007/978-3-642-15705-9_7.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansImaging, Three-DimensionalMaleProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueSystems IntegrationTomography, X-Ray ComputedConceptsManual segmentationAutomatic segmentationImportant treatment parametersNonrigid registrationImage-guided radiotherapy systemReal patient dataNon-rigid registrationIntegrated SegmentationRegistration partRadiotherapy linear acceleratorSegmentationTreatment imagesImage qualityCone-beam CTTreatment parametersImagesPromising resultsPatient dataKey anatomical structuresLinear acceleratorRegistrationPrevious workRadiotherapy system
2009
2D‐3D registration for prostate radiation therapy based on a statistical model of transmission images
Munbodh R, Tagare HD, Chen Z, Jaffray DA, Moseley DJ, Knisely JP, Duncan JS. 2D‐3D registration for prostate radiation therapy based on a statistical model of transmission images. Medical Physics 2009, 36: 4555-4568. PMID: 19928087, DOI: 10.1118/1.3213531.Peer-Reviewed Original ResearchAlgorithmsData Interpretation, StatisticalHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalMalePattern Recognition, AutomatedPhantoms, ImagingProstatic NeoplasmsRadiographic Image EnhancementRadiotherapy, ConformalReproducibility of ResultsSensitivity and SpecificitySubtraction Technique