2024
In vivo neuropil density from anatomical MRI and machine learning
Akif A, Staib L, Herman P, Rothman D, Yu Y, Hyder F. In vivo neuropil density from anatomical MRI and machine learning. Cerebral Cortex 2024, 34: bhae200. PMID: 38771239, PMCID: PMC11107380, DOI: 10.1093/cercor/bhae200.Peer-Reviewed Original ResearchConceptsMagnetic resonance imagingSynaptic densityNeuropil densityCellular densityArtificial neural networkNeural networkPositron emission tomographyAnatomical magnetic resonance imagingHealthy subjectsSynaptic activityMRI scansMachine learning algorithmsBrain's energy budgetEmission tomographyIn vivo MRI scansResonance imagingTissue cellularityLearning algorithmsDiffusion magnetic resonance imagingMachine learningMicroscopic interpretationInterpretation of functional neuroimaging dataIndividual predictionsSubjectsMulticenter Quantification of Radiation Exposure and Associated Risks for Prostatic Artery Embolization in 1476 Patients.
Ayyagari R, Rahman S, Grizzard K, Mustafa A, Staib L, Makkia R, Bhatia S, Bilhim T, Carnevale F, Davis C, Fischman A, Isaacson A, McClure T, McWilliams J, Nutting C, Richardson A, Salem R, Sapoval M, Yu H. Multicenter Quantification of Radiation Exposure and Associated Risks for Prostatic Artery Embolization in 1476 Patients. Radiology 2024, 310: e231877. PMID: 38441098, DOI: 10.1148/radiol.231877.Peer-Reviewed Original ResearchConceptsProstatic artery embolizationCumulative air kermaRadiation-related adverse eventsBody mass indexAdverse eventsEffective doseFluoroscopy timeArtery embolizationRadiation doseKerma area product valuesMedian cumulative air kermaMedian effective radiation doseFluoroscopy unitPatient body mass indexRadiation exposureTwo-sample <i>t</i> test wasRadiation dose dataBenign prostatic hyperplasiaFluoroscopy-guided proceduresPatient radiation exposureEffective radiation doseRadiation field areaRadiation effective doseWilcoxon rank sum testInvasive angiographic procedures
2023
A Multiclass Radiomics Method–Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans
Henao J, Depotter A, Bower D, Bajercius H, Todorova P, Saint-James H, de Mortanges A, Barroso M, He J, Yang J, You C, Staib L, Gange C, Ledda R, Caminiti C, Silva M, Cortopassi I, Dela Cruz C, Hautz W, Bonel H, Sverzellati N, Duncan J, Reyes M, Poellinger A. A Multiclass Radiomics Method–Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans. Investigative Radiology 2023, 58: 882-893. PMID: 37493348, PMCID: PMC10662611, DOI: 10.1097/rli.0000000000001005.Peer-Reviewed Original ResearchConceptsCOVID-19 positive patientsClinical Progression ScaleLung lesionsLesion modelDisease severityGround-glass opacitiesCOVID-19 patientsRadiologist assessmentExpert thoracic radiologistsMulticenter cohortPleural effusionDisease extentRetrospective studyDevelopment cohortPatient assessmentTomography scanCT scanSeverity ScalePatient's diseaseTissue lesionsThoracic radiologistsLesionsPatientsRadiomics modelRadiomic featuresPredicting 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
Operator Learning Curve for Prostatic Artery Embolization and Its Impact on Outcomes in 296 Patients
Powell T, Rahman S, Staib L, Bhatia S, Ayyagari R. Operator Learning Curve for Prostatic Artery Embolization and Its Impact on Outcomes in 296 Patients. CardioVascular And Interventional Radiology 2022, 46: 229-237. PMID: 36456689, DOI: 10.1007/s00270-022-03321-w.Peer-Reviewed Original ResearchPrediction 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 MRICryoablation of Venous Malformations: A Systematic Review
Fish A, Moushey A, Chan SM, Staib L, Marino A, Schlachter T. Cryoablation of Venous Malformations: A Systematic Review. Journal Of Vascular And Interventional Radiology 2022, 33: 993-1000. PMID: 35469956, DOI: 10.1016/j.jvir.2022.04.010.Peer-Reviewed Original ResearchConceptsPain scoresVenous malformationsPercutaneous cryoablationLesion volumeClinical studiesLesion sizePostprocedural pain scoresWeighted mean reductionMajor adverse eventsCases of cryoablationPersistent dysesthesiaPostprocedural changesPostprocedural decreaseRandom effects analysisAdverse eventsPatient characteristicsCryoablation techniqueAdverse outcomesTechnical successComplete resolutionPostprocedural symptomsMean reductionCryoablationPrior treatmentSystematic reviewMR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features
Petukhova-Greenstein A, Zeevi T, Yang J, Chai N, DiDomenico P, Deng Y, Ciarleglio M, Haider SP, Onyiuke I, Malpani R, Lin M, Kucukkaya AS, Gottwald LA, Gebauer B, Revzin M, Onofrey J, Staib L, Gunabushanam G, Taddei T, Chapiro J. MR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features. Journal Of Vascular And Interventional Radiology 2022, 33: 814-824.e3. PMID: 35460887, PMCID: PMC9335926, DOI: 10.1016/j.jvir.2022.04.006.Peer-Reviewed Original ResearchConceptsProgression-free survivalPoor progression-free survivalLiver Imaging ReportingHepatocellular carcinomaMR imaging biomarkersRadiomics signatureRadiofrequency ablationRadiomic featuresImaging biomarkersImaging ReportingFirst follow-up imagingMedian progression-free survivalRF ablationEarly-stage hepatocellular carcinomaPretreatment magnetic resonanceFirst-line treatmentMultifocal hepatocellular carcinomaSelection operator Cox regression modelTherapy-naïve patientsEarly-stage diseaseKaplan-Meier analysisCox regression modelLog-rank testFollow-up imagingPrediction of outcomeMachine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
Petersen G, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. American Journal Of Neuroradiology 2022, 43: 526-533. PMID: 35361577, PMCID: PMC8993193, DOI: 10.3174/ajnr.a7473.Peer-Reviewed Original ResearchConceptsMachine learning-based methodsLearning-based methodsBalanced data setData setsVector machine modelMachine learningClassification algorithmsMachine modelMachineAlgorithmData basesPrediction modelPromising resultsPrimary CNS lymphomaPrediction model study RiskRisk of biasRadiomic featuresClassifierSetCNS lymphomaWebLearningFeaturesQualitySystematic reviewCumulative diagnostic imaging radiation exposure in premature neonates.
Khattab M, Hagan J, Staib LH, Mustafa A, Goodman TR. Cumulative diagnostic imaging radiation exposure in premature neonates. Journal Of Neonatal-Perinatal Medicine 2022, 15: 95-103. PMID: 33843704, DOI: 10.3233/npm-210726.Peer-Reviewed Original ResearchConceptsLength of stayCumulative effective radiation doseNeonatal intensive care unitIntensive care unitGestational ageBirth weightEffective radiation doseIntestinal perforationCare unitPremature infantsRadiological studiesRadiation doseLevel 4 neonatal intensive care unitRadiation exposureMedian birth weightMedian gestational ageRetrospective chart reviewVulnerable patient populationPredictors of exposureIdentifies risk factorsTotal radiation doseChart reviewPremature neonatesPatient populationRisk factors
2021
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib LH, Ventola P, Duncan JS. BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis. Medical Image Analysis 2021, 74: 102233. PMID: 34655865, PMCID: PMC9916535, DOI: 10.1016/j.media.2021.102233.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagesGraph neural network frameworkMedical image analysisGraph neural networkGraph convolutional layersNeural network frameworkDifferent evaluation metricsSpecific task statesIndependent fMRI datasetsPooling layerConvolutional layersConsistency lossNetwork frameworkNeural networkFMRI datasetsImage analysis methodEvaluation metricsDetection resultsBrain graphsSubjects releaseROI selectionImage analysisCognitive stimuliTask statesFMRI analysisVoiding and Storage Domain-Specific Symptom Score Outcomes After Prostate Artery Embolization for Lower Urinary Tract Symptoms and Urinary Retention
Powell T, Staib L, Liu B, Bhatia S, Chai T, Ayyagari R. Voiding and Storage Domain-Specific Symptom Score Outcomes After Prostate Artery Embolization for Lower Urinary Tract Symptoms and Urinary Retention. Urology 2021, 156: 216-224. PMID: 33961894, DOI: 10.1016/j.urology.2021.02.046.Peer-Reviewed Original ResearchConceptsLower urinary tract symptomsProstate artery embolizationBenign prostatic hyperplasiaUrinary tract symptomsLUTS patientsIPSS-VUrinary retentionRetention patientsArtery embolizationTract symptomsTotal International Prostate Symptom ScoreDomain scoresInternational Prostate Symptom ScoreComponent scoresProstate Symptom ScoreSingle centerSymptom scoresLife scoresProstatic hyperplasiaIPSSPatientsPre-PAEQoLMonthsScore outcomesCurrent state of radiology report release in electronic patient portals
Holder J, Tocino I, Facchini D, Nardecchia N, Staib L, Crawley D, Pahade JK. Current state of radiology report release in electronic patient portals. Clinical Imaging 2021, 74: 22-26. PMID: 33429142, DOI: 10.1016/j.clinimag.2020.12.020.Peer-Reviewed Original Research
2020
Creating a Radiology Call Center Hotline and “HOT” Sites: Centralizing Radiology Questions and Cohorting Out-patient Care During the COVID-19 Pandemic
Jang B, Facchini D, Staib L, Fernandez A, Pye S, Goodman RT, Granucci C, Nardecchia N, Pahade JK. Creating a Radiology Call Center Hotline and “HOT” Sites: Centralizing Radiology Questions and Cohorting Out-patient Care During the COVID-19 Pandemic. Current Problems In Diagnostic Radiology 2020, 50: 665-668. PMID: 33036812, PMCID: PMC7519410, DOI: 10.1067/j.cpradiol.2020.09.018.Peer-Reviewed Original ResearchConceptsPatient encountersMost common reasonsCOVID-19 symptomsOut-patient careCOVID-19High-quality patientDate of studyCOVID-19 screeningQuality patientCOVID-19 transmissionX-ray examsCommon reasonRadiology technologistsPatientsCOVID-19 pandemicTime of examImaging sitesCareRadiology operationsExamTotalMulti-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
Li X, Gu Y, Dvornek N, Staib LH, Ventola P, Duncan JS. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Medical Image Analysis 2020, 65: 101765. PMID: 32679533, PMCID: PMC7569477, DOI: 10.1016/j.media.2020.101765.Peer-Reviewed Original ResearchConceptsDeep learning modelsFederated LearningPrivacy-preserving federated learningLearning modelFederated learning approachPrivacy-preserving strategyDomain adaptation methodsData analysis problemsLocal model weightsIterative optimization algorithmEntity dataDomain adaptationLearning approachLearning formulationMulti-site dataRandomization mechanismAdaptation methodNeuroimage analysisDifferent tasksModel weightsModel optimizationOptimization algorithmPrivate informationTraining strategyAnalysis problemAutomated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning
Bousabarah K, Letzen B, Tefera J, Savic L, Schobert I, Schlachter T, Staib LH, Kocher M, Chapiro J, Lin M. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology 2020, 46: 216-225. PMID: 32500237, PMCID: PMC7714704, DOI: 10.1007/s00261-020-02604-5.Peer-Reviewed Original ResearchConceptsDeep convolutional neural networkAverage false positive rateDice similarity coefficientU-NetDeep learning algorithmsConvolutional neural networkTest setMean Dice similarity coefficientRandom forest classifierDCNN methodDCNN approachDeep learningNet architectureLearning algorithmNeural networkLiver segmentationManual 3D segmentationForest classifierGround truthManual segmentationFalse positive rateCorresponding segmentationSegmentationMultiphasic contrast-enhanced MRIThresholdingScientific Collaboration across Time and Space: Bibliometric Analysis of the American Journal of Neuroradiology, 1980–2018
Zohrabian VM, Staib LH, Castillo M, Wang L. Scientific Collaboration across Time and Space: Bibliometric Analysis of the American Journal of Neuroradiology, 1980–2018. American Journal Of Neuroradiology 2020, 41: 766-771. PMID: 32299800, PMCID: PMC7228174, DOI: 10.3174/ajnr.a6523.Peer-Reviewed Original ResearchSparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original ResearchImpact of Radiologist-Driven Change-Order Requests on Outpatient CT and MRI Examinations
Pourjabbar S, Cavallo JJ, Arango J, Tocino I, Staib LH, Imanzadeh A, Forman HP, Pahade JK. Impact of Radiologist-Driven Change-Order Requests on Outpatient CT and MRI Examinations. Journal Of The American College Of Radiology 2020, 17: 1014-1024. PMID: 31954708, DOI: 10.1016/j.jacr.2019.12.017.Peer-Reviewed Original Research