2024
Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke
Sommer J, Dierksen F, Zeevi T, Tran A, Avery E, Mak A, Malhotra A, Matouk C, Falcone G, Torres-Lopez V, Aneja S, Duncan J, Sansing L, Sheth K, Payabvash S. Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke. Frontiers In Artificial Intelligence 2024, 7: 1369702. PMID: 39149161, PMCID: PMC11324606, DOI: 10.3389/frai.2024.1369702.Peer-Reviewed Original ResearchEnd-to-endComputed tomography angiographyLarge vessel occlusionConvolutional neural networkDeep learning pipelineTrain separate modelsLogistic regression modelsResNet-50Deep learningAdmission computed tomography angiographyNeural networkLearning pipelineAdmission CT angiographyPreprocessing stepDiagnosis of large vessel occlusionsLarge vessel occlusion strokeReceiver operating characteristic areaEnsemble modelAutomated modelPre-existing morbidityCT angiographyReperfusion successNeurological examCross-validationOcclusion strokeMonte-Carlo Frequency Dropout for Predictive Uncertainty Estimation in Deep Learning
Zeevi T, Venkataraman R, Staib L, Onofrey J. Monte-Carlo Frequency Dropout for Predictive Uncertainty Estimation in Deep Learning. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635511.Peer-Reviewed Original ResearchArtificial neural networkState-of-the-artMedical image dataPredictive uncertainty estimationBiomedical image dataImage dataOptimal artificial neural networkMC dropoutDropout approachSource-codeDrop-connectDeep learningNeural networkSignal spaceMonte-CarloPrediction uncertaintyUncertainty estimationDiverse setComprehensive comparisonPrediction scenariosDeepPosterior predictive distributionRepositoryDecision-makingNetworkImpact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning–Generated Biomarkers
Haider S, Zeevi T, Sharaf K, Gross M, Mahajan A, Kann B, Judson B, Prasad M, Burtness B, Aboian M, Canis M, Reichel C, Baumeister P, Payabvash S. Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning–Generated Biomarkers. Journal Of Nuclear Medicine 2024, 65: jnumed.123.266637. PMID: 38514087, DOI: 10.2967/jnumed.123.266637.Peer-Reviewed Original ResearchOropharyngeal squamous cell carcinomaSquamous cell carcinomaHuman papillomavirusRadiomic featuresIntraclass correlation coefficientCell carcinomaLentiform nucleusHuman papillomavirus statusReceiver-operating-characteristic analysisReceiver-operating-characteristic curvePET radiomic featuresUnivariate logistic regressionF-FDGPrimary tumorTraining cohortValidation cohortRadiomic biomarkersUnivariate analysisInterindividual comparabilityPredictive valueDegree of reproducibilityMedian areaRadiomic markersLogistic regressionAUCReliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology
Zeevi T, Leapman M, Sprenkle P, Venkataraman R, Staib L, Onofrey J. Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology. IEEE Transactions On Biomedical Engineering 2024, 71: 1084-1091. PMID: 37874731, PMCID: PMC10901528, DOI: 10.1109/tbme.2023.3326799.Peer-Reviewed Original ResearchMagnetic resonance imagingIndividual patientsBiopsy locationProstate biopsy dataBiopsy histopathologyHistopathology scoresPathology scoresBiopsy dataMRI biomarkersTreatment planPatientsResonance imagingProstate regionBiomarkersTherapy treatment plansPathologyRepresentative sampleScoresImaging analysisPrevious studiesHistopathologyProstateCancerCliniciansRadiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke
Avery E, Abou-Karam A, Abi-Fadel S, Behland J, Mak A, Haider S, Zeevi T, Sanelli P, Filippi C, Malhotra A, Matouk C, Falcone G, Petersen N, Sansing L, Sheth K, Payabvash S. Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke. Diagnostics 2024, 14: 485. PMID: 38472957, PMCID: PMC10930945, DOI: 10.3390/diagnostics14050485.Peer-Reviewed Original ResearchCollateral statusCollateral scoreLarge vessel occlusionAcute LVO strokeRadiomics modelTest cohortCT angiography of patientsAdmission computed tomography angiographyAnterior circulation territoryAngiography of patientsLong-term outcomesReceiver operating characteristic areaRadiomics-based predictionCollateral arterial circulationOperating characteristics areaAdmission CTACirculation territoryCT angiographyClinical outcomesRadiomic featuresTreatment triageOcclusion strokeVessel occlusionPatientsArterial circulationUncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
Tran A, Zeevi T, Haider S, Abou Karam G, Berson E, Tharmaseelan H, Qureshi A, Sanelli P, Werring D, Malhotra A, Petersen N, de Havenon A, Falcone G, Sheth K, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. Npj Digital Medicine 2024, 7: 26. PMID: 38321131, PMCID: PMC10847454, DOI: 10.1038/s41746-024-01007-w.Peer-Reviewed Original ResearchDeep learning modelsHematoma expansionIntracerebral hemorrhageICH expansionComputed tomographyNon-contrast head CTNon-contrast head computed tomographyHigh risk of HEHead computed tomographyHigh-confidence predictionsRisk of HENon-contrast headReceiver operating characteristic areaModifiable risk factorsMonte Carlo dropoutOperating characteristics areaPotential treatment targetHead CTVisual markersIdentified patientsAutomated deep learning modelDataset of patientsRisk factorsHigh riskPatientsPeri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage
Dierksen F, Tran A, Zeevi T, Maier I, Qureshi A, Sanelli P, Werring D, Malhotra A, Falcone G, Sheth K, Payabvash S. Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage. European Stroke Journal 2024, 9: 383-390. PMID: 38179883, PMCID: PMC11318427, DOI: 10.1177/23969873231223814.Peer-Reviewed Original ResearchNIH Stroke ScaleGlasgow Coma ScaleAssociation of baselineIntracerebral hemorrhagePerihematomal edemaHematoma volumeAcute supratentorial intracerebral hemorrhageNon-traumatic intracerebral hemorrhageSecondary brain injuryAcute ICH patientsIndependent prognostic factorSupratentorial intracerebral hemorrhageAdmission NIH Stroke ScalePotential treatment targetRankin scoreStroke ScaleIndependent predictorsPatient agePrognostic factorsComa ScaleMultivariable analysisICH patientsPrognostic valueBrain injuryFavorable outcome
2023
Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers
Haider S, Qureshi A, Jain A, Tharmaseelan H, Berson E, Zeevi T, Werring D, Gross M, Mak A, Malhotra A, Sansing L, Falcone G, Sheth K, Payabvash S. Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers. Frontiers In Neuroscience 2023, 17: 1225342. PMID: 37655013, PMCID: PMC10467422, DOI: 10.3389/fnins.2023.1225342.Peer-Reviewed Original ResearchIndependent validation cohortIntracerebral hemorrhageRadiomic featuresValidation cohortClinical variablesHematoma expansionSpontaneous supratentorial intracerebral hemorrhageNon-contrast head CTSupratentorial intracerebral hemorrhageTomography (CT) of patientsNon-contrast headFuture clinical trialsNon-contrast CTIntracerebral Hemorrhage ExpansionHigh predictive valueBAT scoreHypertensive patientsClinical predictorsPrognostic relevanceFunctional outcomeClinical trialsHead CTHemorrhage expansionClinical trial datasetDiscovery cohortPredicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Kucukkaya A, Zeevi T, Chai N, Raju R, Haider S, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Scientific Reports 2023, 13: 7579. PMID: 37165035, PMCID: PMC10172370, DOI: 10.1038/s41598-023-34439-7.Peer-Reviewed Original Research
2022
Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction
Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Bhawnani J, Zawalich M, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction. Frontiers In Neuroscience 2022, 16: 860208. PMID: 36312024, PMCID: PMC9606757, DOI: 10.3389/fnins.2022.860208.Peer-Reviewed Original ResearchBrain tumor segmentationMedical imagesFeature extractionTumor segmentationRadiomic feature extractionDiagnostic workstationDeep learning-based algorithmPatient's medical imagesLearning-based algorithmFeature extraction toolImage processing algorithmsYale New Haven HealthGround truth dataImage annotationAI-segmentationAI algorithmsArtificial intelligenceEnd workflowProcessing algorithmsPicture archivingLarge datasetsLarge expertManual modificationInternal datasetManual segmentationPrediction of Adverse Pathology at Radical Prostatectomy in Grade Group 2 and 3 Prostate Biopsies Using Machine Learning.
Paulson N, Zeevi T, Papademetris M, Leapman MS, Onofrey JA, Sprenkle PC, Humphrey PA, Staib LH, Levi AW. Prediction of Adverse Pathology at Radical Prostatectomy in Grade Group 2 and 3 Prostate Biopsies Using Machine Learning. JCO Clinical Cancer Informatics 2022, 6: e2200016. PMID: 36179281, DOI: 10.1200/cci.22.00016.Peer-Reviewed Original ResearchConceptsGrade group 2Prostate biopsyRadical prostatectomyAdverse outcomesGroup 2GG-2Core prostate biopsyProstate cancer outcomesPatient's clinical riskClinical risk assessmentCore needle biopsyOngoing clinical needAdverse outcome predictionRetrospective reviewAdverse pathologyCAPRA scoreEntire cohortCancer outcomesPathologic diagnosisNeedle biopsyClinical riskDisease outcomeProstate cancerBiopsyDisease oneMachine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study.
Iseke S, Zeevi T, Kucukkaya AS, Raju R, Gross M, Haider SP, Petukhova-Greenstein A, Kuhn TN, Lin M, Nowak M, Cooper K, Thomas E, Weber MA, Madoff DC, Staib L, Batra R, Chapiro J. Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study. American Journal Of Roentgenology 2022, 220: 245-255. PMID: 35975886, PMCID: PMC10015590, DOI: 10.2214/ajr.22.28077.Peer-Reviewed Original ResearchConceptsEarly-stage hepatocellular carcinomaLiver transplantHepatocellular carcinomaImaging featuresPosttreatment recurrenceOrgan allocationMean AUCLiver transplant eligibilityPretreatment clinical characteristicsPretreatment MRI examinationsKaplan-Meier analysisKaplan-Meier curvesClinical characteristicsImaging surveillanceTherapy allocationTransplant eligibilityUnderwent treatmentClinical parametersRetrospective studyUnpredictable complicationMRI dataConcept studyPoor survivalClinical impactPretreatment MRIDataset on acute stroke risk stratification from CT angiographic radiomics
Avery EW, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. Dataset on acute stroke risk stratification from CT angiographic radiomics. Data In Brief 2022, 44: 108542. PMID: 36060820, PMCID: PMC9428796, DOI: 10.1016/j.dib.2022.108542.Peer-Reviewed Original ResearchMachine Learning FrameworkImage processing technologyFeature selection algorithmField of radiomicsRadiomics-based analysisMachine learningMedical imagesSelection algorithmAssistance toolRadiomic featuresRadiomics dataProcessing technologyAnalysis frameworkRelevant informationRadiomics algorithmAlgorithmCT angiography imagesRadiomicsMethodological supportExternal testingFrameworkImagesAngiography imagesMachineFeaturesMachine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities
Merkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Petersen G, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers 2022, 14: 2623. PMID: 35681603, PMCID: PMC9179416, DOI: 10.3390/cancers14112623.Peer-Reviewed Original ResearchMachine learning toolsGrade predictionLearning toolsML applicationsClassifier algorithmML modelsClassification methodMedical imagingData sourcesPractices of radiologistsToolGlioma gradingNext stepWorkflowAlgorithmChallengesTechnological innovationImplementationPredictionModelLast decadeSpecific areasPET/CT radiomics potentially improves progression-free survival (PFS) and overall survival (OS) prognostication beyond UICC TNM staging in oropharyngeal squamous cell carcinoma (OPSCC) patients
Haider S, Sharaf K, Zeevi T, Mahajan A, Forghani R, Judson B, Kann B, Burtness B, Reichel C, Baumeister P, Payabvash S. PET/CT radiomics potentially improves progression-free survival (PFS) and overall survival (OS) prognostication beyond UICC TNM staging in oropharyngeal squamous cell carcinoma (OPSCC) patients. Laryngo-Rhino-Otologie 2022, 101: s184-s184. DOI: 10.1055/s-0042-1746471.Peer-Reviewed Original ResearchPET/CT-Radiomics zuzüglich zum UICC-Staging könnten die Prognostik des Progressionsfreien Überlebens (PFS) und Gesamtüberlebens (OS) beim Oropharyngealen Plattenepithelkarzinom (OPSCC) verbessern
Haider S, Sharaf K, Zeevi T, Mahajan A, Forghani R, Judson B, Kann B, Burtness B, Reichel C, Baumeister P, Payabvash S. PET/CT-Radiomics zuzüglich zum UICC-Staging könnten die Prognostik des Progressionsfreien Überlebens (PFS) und Gesamtüberlebens (OS) beim Oropharyngealen Plattenepithelkarzinom (OPSCC) verbessern. Laryngo-Rhino-Otologie 2022, 101: s13-s13. DOI: 10.1055/s-0042-1747116.Peer-Reviewed Original ResearchCT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke
Avery EW, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke. NeuroImage Clinical 2022, 34: 103034. PMID: 35550243, PMCID: PMC9108990, DOI: 10.1016/j.nicl.2022.103034.Peer-Reviewed Original ResearchConceptsLarge vessel occlusion strokeIndependent cohortPrognostication toolsMechanical thrombectomyRisk stratificationOcclusion strokeExternal cohortAnterior circulation large vessel occlusion strokeOutcome predictionAcute stroke triageAnterior circulation territoryRadiomic featuresTime of admissionGeisinger Medical CenterLVO stroke patientsReliable clinical informationSignificant differencesAdmission CTAStroke patientsPrognostic informationFavorable outcomeStroke triageTreatment decisionsMedical CenterRadiomics signatureIntegration of Machine Learning Into Clinical Radiology Practice – Development of a Machine Learning Tool for Preoperative Glioma Grade Prediction (P14-9.002)
Merkaj S, Zeevi T, Bousabarah K, Kazarian E, Lin M, Pala A, Petersen G, Jekel L, Bahar R, Tillmanns N, Cui J, Ikuta I, Bronen R, Fadel S, Westerhoff M, Omuro A, Aboian M. Integration of Machine Learning Into Clinical Radiology Practice – Development of a Machine Learning Tool for Preoperative Glioma Grade Prediction (P14-9.002). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3243.Peer-Reviewed Original ResearchSystematic Review of Machine Learning Models for Differentiation of Glioma from Brain Metastasis (P14-9.006)
Jekel L, Brim W, Petersen G, Merkaj S, Subramanian H, Zeevi T, Payabvash S, Khaled B, Lin M, Cui J, Brackett A, Johnson M, Omuro A, Scheffler B, Aboian M. Systematic Review of Machine Learning Models for Differentiation of Glioma from Brain Metastasis (P14-9.006). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3376.Peer-Reviewed Original ResearchDevelopment of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003)
Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Development of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3146.Peer-Reviewed Original Research