Tal Zeevi, MSc
Postgraduate AssociateAbout
Research
Publications
2025
Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies
Chen F, Esmaili R, Khajir G, Zeevi T, Gross M, Leapman M, Sprenkle P, Justice A, Arora S, Weinreb J, Spektor M, Huber S, Humphrey P, Levi A, Staib L, Venkataraman R, Martin D, Onofrey J. Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies. European Urology Oncology 2025 PMID: 39924390, DOI: 10.1016/j.euo.2025.01.005.Peer-Reviewed Original ResearchProstate cancerPrediction of clinically significant prostate cancerClinical dataProstate-specific antigen levelClinically significant prostate cancerProstate magnetic resonance imagingSeverity of prostate cancerCombination of clinical featuresPrediction of csPCaSignificant prostate cancerProstate Imaging-ReportingCore needle biopsyRetrospective analysis of dataDecision curve analysisReducing unnecessary biopsiesProstate cancer diagnosisReceiver operating characteristic curveArea under the receiver operating characteristic curveFalse-negative rateMagnetic resonance imagingPersonalized risk assessmentAntigen levelsNeedle biopsyPatient ageUnnecessary biopsies
2024
Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography
Tran A, Desser D, Zeevi T, Karam G, Zietz J, Dell’Orco A, Chen M, Malhotra A, Qureshi A, Murthy S, Majidi S, Falcone G, Sheth K, Nawabi J, Payabvash S. Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography. Applied Sciences 2024, 15: 111. PMID: 40046237, PMCID: PMC11882137, DOI: 10.3390/app15010111.Peer-Reviewed Original ResearchIntracerebral hemorrhageHematoma expansionFollow-up CT scansFollow-up head computed tomographyPredictors of poor outcomeDeep learning classification modelFollow-up scansHead computed tomographyFalse-negative resultsHematoma segmentationAutomated segmentationMulticentre cohortCT scanValidation cohortPoor outcomeComputed tomographyFollow-upClassification modelOptimizational methodHematomaAnnotationA Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
Tran A, Desser D, Zeevi T, Abou Karam G, Dierksen F, Dell'Orco A, Kniep H, Hanning U, Fiehler J, Zietz J, Sanelli P, Malhotra A, Duncan J, Aneja S, Falcone G, Qureshi A, Sheth K, Nawabi J, Payabvash S. A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence. Bioengineering 2024, 11: 1274. PMID: 39768092, PMCID: PMC11672977, DOI: 10.3390/bioengineering11121274.Peer-Reviewed Original ResearchNon-contrast head computed tomographyPerihematomal edemaHead computed tomographyIntracerebral hemorrhageComputed tomographyVolume similarityUniversity Medical Center Hamburg-EppendorfSecondary brain injuryYale cohortInfratentorial locationMulticentre trialCT scanTreatment planningNon-contrastHamburg-EppendorfImaging markersHemorrhagic strokeHemorrhageEdemaCohortBrain injuryDice coefficientNeuroimaging Correlates of the NIH-Toolbox-Driven Cognitive Metrics in Children
Acosta-Rodriguez H, Yuan C, Bobba P, Stephan A, Zeevi T, Malhotra A, Tran A, Kaltenhauser S, Payabvash S. Neuroimaging Correlates of the NIH-Toolbox-Driven Cognitive Metrics in Children. Journal Of Integrative Neuroscience 2024, 23: 217. PMID: 39735971, PMCID: PMC11851640, DOI: 10.31083/j.jin2312217.Peer-Reviewed Original ResearchConceptsCognitive composite scoreAdolescent Brain Cognitive DevelopmentFluid cognition composite scoresStructural magnetic resonance imagingComposite scoreDiffusion tensor imagingNeuroimaging correlatesCognitive functionRs-fMRINational Institutes of Health (NIH) Toolbox Cognition BatteryCognitive scoresMicrostructural integrityResting-state functional connectivityCrystallized cognition composite scoreCortical surface areaTotal cognitive scoreWM microstructural integrityCognitive batteryCrystallized cognitionNeuroanatomical correlatesWhite matterCognitive performanceNeuroimaging metricsFunctional connectivityNeuroimaging dataDeep 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-makingNetworkDifferentiation of IDH-mutant Glioma Subtypes Using Unsupervised Dimensionality Reduction of MRI Biomarkers
Willms K, Zeevi T, Chadha S, von Reppert M, Lost J, Tillmanns N, Merkaj S, Huttner A, Aneja S, Aboian M. Differentiation of IDH-mutant Glioma Subtypes Using Unsupervised Dimensionality Reduction of MRI Biomarkers. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/3094.Peer-Reviewed Original ResearchAutomated MR Spectroscopy single-voxel placement in suspected diffuse glioma based on tumor biology
Chadha S, Jacobs S, Zeevi T, Tillmanns N, Merkaj S, Lost J, Lin M, Bousabarah K, Holler W, Memon F, Aneja S, Aboian M. Automated MR Spectroscopy single-voxel placement in suspected diffuse glioma based on tumor biology. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/5120.Peer-Reviewed Original ResearchTumor biologyDiffuse gliomasSingle-voxel magnetic resonance spectroscopyManagement of diffuse gliomasMagnetic resonance spectroscopyNon-invasive diagnosisVoxel placementMetabolite quantificationSingle-voxelMR imagingRadiology techniciansTumorGliomaPlacementResonance spectroscopyPoor-quality spectraClinicDiagnosisImpact 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, PMCID: PMC11927063, 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 studiesHistopathologyProstateCancerClinicians
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