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
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