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