2022
Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis
Bahar RC, Merkaj S, Petersen G, Tillmanns N, Subramanian H, Brim WR, Zeevi T, Staib L, Kazarian E, Lin M, Bousabarah K, Huttner AJ, Pala A, Payabvash S, Ivanidze J, Cui J, Malhotra A, Aboian MS. Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis. Frontiers In Oncology 2022, 12: 856231. PMID: 35530302, PMCID: PMC9076130, DOI: 10.3389/fonc.2022.856231.Peer-Reviewed Original ResearchMachine learning modelsLearning modelConvolutional neural networkDeep learning studiesLarge training datasetsGrade predictionSupport vector machineApplication of MLNeural networkConventional machineVector machineTraining datasetBest performing modelCommon algorithmsModel performanceEssential metricMean prediction accuracyHigh predictive accuracyPrediction accuracyPerforming modelMachinePrediction modelDiagnosis statementsAccuracy statementsLearning studies
2020
Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings
Payabvash S, Aboian M, Tihan T, Cha S. Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. Frontiers In Oncology 2020, 10: 71. PMID: 32117728, PMCID: PMC7018938, DOI: 10.3389/fonc.2020.00071.Peer-Reviewed Original ResearchDecision tree modelDifferent machineTree modelAccurate classification rateAveraged AUCClassification algorithmsTraining datasetRandom forestDecision treeClassification rateRandom forest modelMachineAlgorithmTerminal nodesHigh accuracyForest modelDecision modelHistogram analysisDichotomized classificationClassificationIntra-axial posterior fossa tumorsAccuracyDatasetGreater accuracyNodes