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 predictionsSubjects
2023
Predicting 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
Prediction 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 ResearchMeSH KeywordsBiopsyBiopsy, Large-Core NeedleEosine Yellowish-(YS)HematoxylinHumansMachine LearningMaleProstateProstatectomyProstatic NeoplasmsConceptsGrade 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 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 ResearchMeSH KeywordsGliomaHumansLymphomaMachine LearningMagnetic Resonance ImagingReproducibility of ResultsConceptsMachine 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 review
2020
Sparse 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 Research
2019
Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning
Johnson KM, Johnson HE, Zhao Y, Dowe DA, Staib LH. Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning. Radiology 2019, 292: 182061. PMID: 31237495, DOI: 10.1148/radiol.2019182061.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overComputed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseCoronary VesselsFemaleHumansImage Interpretation, Computer-AssistedMachine LearningMaleMiddle AgedPredictive Value of TestsReproducibility of ResultsRisk FactorsSeverity of Illness IndexYoung AdultConceptsCoronary CT angiographyCoronary Artery Disease ReportingNonfatal myocardial infarctionHeart disease deathCT angiographyCause mortalityDisease deathsMyocardial infarctionCoronary heart disease deathDisease reportingCoronary artery diseaseNational Death IndexData System scoreCardiovascular eventsCoronary deathAdverse eventsArtery diseaseCoronary diseaseDeath IndexCoronary segmentsPrognostic informationVessel scorePatientsSystem scoreSubsequent death