2022
Dataset 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 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 review
2021
NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW
Bahar R, Merkaj S, Brim W, Subramanian H, Zeevi T, Kazarian E, Lin M, Bousabarah K, Payabvash S, Ivanidze J, Cui J, Tocino I, Malhotra A, Aboian M. NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW. Neuro-Oncology 2021, 23: vi133-vi133. PMCID: PMC8598529, DOI: 10.1093/neuonc/noab196.523.Peer-Reviewed Original ResearchClassical machine learningConvolutional neural networkDeep learningSupport vector machineMachine learningMachine learning technologiesHigher grading accuracyMachine learning methodsArtificial intelligenceML applicationsHighest performing modelLearning technologyNeural networkMultimodal sequencesLearning methodsVector machineCommon algorithmsML methodsTCIA datasetPrimary machinePrediction accuracyGrade predictionGrading accuracyMachinePerforming modelOTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review
Brim W, Jekel L, Petersen G, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Johnson M, Mahajan A, Aboian M. OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review. Neuro-Oncology Advances 2021, 3: iii17-iii17. PMCID: PMC8351249, DOI: 10.1093/noajnl/vdab071.067.Peer-Reviewed Original ResearchConvolutional neural networkDeep learningML algorithmsMachine Learning AlgorithmsApplication of machineClassical ML algorithmsDevelopment of machineSupport vector machine algorithmVector machine algorithmArtificial intelligenceMachine learningSearch strategyDL modelsLearning algorithmFeature extractionNeural networkMachine algorithmAverage accuracyML methodsCML algorithmAlgorithmHigh accuracyLearningMachineAccuracy
2020
PROOF-OF-CONCEPT USE OF MACHINE LEARNING TO PREDICT TUMOR RECURRENCE OF EARLY-STAGE HEPATOCELLULAR CARCINOMA BEFORE THERAPY USING BASELINE MAGNETIC RESONANCE IMAGING
Batra R, Kuecuekkaya A, Zeevi T, Raju R, Chai N, Haider S, Elbanan M, Petukhova A, Lin ,, Onofrey J, Nowak M, Cooper K, Thomas E, Gebauer B, Staib L, Chapiro J. PROOF-OF-CONCEPT USE OF MACHINE LEARNING TO PREDICT TUMOR RECURRENCE OF EARLY-STAGE HEPATOCELLULAR CARCINOMA BEFORE THERAPY USING BASELINE MAGNETIC RESONANCE IMAGING. Transplantation 2020, 104: s43-s44. DOI: 10.1097/01.tp.0000698472.65040.1e.Peer-Reviewed Original ResearchMachine learning