Ryan Bahar, MD
Hospital ResidentAbout
Research
Publications
2023
Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial
von Reppert M, Ramakrishnan D, Brüningk S, Memon F, Fadel S, Maleki N, Bahar R, Avesta A, Jekel L, Sala M, Lost J, Tillmanns N, Kaur M, Aneja S, Kazerooni A, Nabavizadeh A, Lin M, Hoffmann K, Bousabarah K, Swanson K, Haas-Kogan D, Mueller S, Aboian M. Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial. Neuro-Oncology Advances 2023, 6: vdad172. PMID: 38221978, PMCID: PMC10785766, DOI: 10.1093/noajnl/vdad172.Peer-Reviewed Original ResearchSystematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction
Lost J, Verma T, Jekel L, von Reppert M, Tillmanns N, Merkaj S, Petersen G, Bahar R, Gordem A, Haider M, Subramanian H, Brim W, Ikuta I, Omuro A, Conte G, Marquez-Nostra B, Avesta A, Bousabarah K, Nabavizadeh A, Kazerooni A, Aneja S, Bakas S, Lin M, Sabel M, Aboian M. Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction. American Journal Of Neuroradiology 2023, 44: 1126-1134. PMID: 37770204, PMCID: PMC10549943, DOI: 10.3174/ajnr.a8000.Peer-Reviewed Original Research
2022
Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction
Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Bhawnani J, Zawalich M, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction. Frontiers In Neuroscience 2022, 16: 860208. PMID: 36312024, PMCID: PMC9606757, DOI: 10.3389/fnins.2022.860208.Peer-Reviewed Original ResearchBrain tumor segmentationMedical imagesFeature extractionTumor segmentationRadiomic feature extractionDiagnostic workstationDeep learning-based algorithmPatient's medical imagesLearning-based algorithmFeature extraction toolImage processing algorithmsYale New Haven HealthGround truth dataImage annotationAI-segmentationAI algorithmsArtificial intelligenceEnd workflowProcessing algorithmsPicture archivingLarge datasetsLarge expertManual modificationInternal datasetManual segmentationThe pipeline starts in medical school: characterizing clinician-educator training programs for U.S. medical students
Bahar RC, O’Shea A, Li ES, Swallow MA, Allocco AA, Spak JM, Hafler JP. The pipeline starts in medical school: characterizing clinician-educator training programs for U.S. medical students. Medical Education Online 2022, 27: 2096841. PMID: 35796419, PMCID: PMC9272942, DOI: 10.1080/10872981.2022.2096841.Peer-Reviewed Original ResearchConceptsUndergraduate medical educationU.S. allopathic medical schoolsAllopathic medical schoolsClinician-educator trackMedical schoolsGraduate Medical EducationMedical educationTraining programU.S. medical studentsMedical school rankingEducational theorySchool rankingsMedical studentsSchoolsInstitutional websitesAcademic medicineEducationSustainable outputProgramStudentsCompetenciesWebsitesCurrent stateGeographyCallsMachine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities
Merkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Petersen G, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers 2022, 14: 2623. PMID: 35681603, PMCID: PMC9179416, DOI: 10.3390/cancers14112623.Peer-Reviewed Original ResearchMachine learning toolsGrade predictionLearning toolsML applicationsClassifier algorithmML modelsClassification methodMedical imagingData sourcesPractices of radiologistsToolGlioma gradingNext stepWorkflowAlgorithmChallengesTechnological innovationImplementationPredictionModelLast decadeSpecific areasMachine 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 studiesMachine 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 reviewIdentifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries
Tillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neuro-Oncology Advances 2022, 4: vdac093. PMID: 36071926, PMCID: PMC9446682, DOI: 10.1093/noajnl/vdac093.Peer-Reviewed Original ResearchGlioma segmentationResearch algorithmSegmentation of gliomasHigh accuracy resultsML algorithmsApplicable machineAccuracy resultsTCIA datasetSegmentationAlgorithmMachinePatient dataSystematic literature reviewOverfittingData extractionDatasetBratDatabaseRecent advancesResearch literatureLimitationsExtractionCurrent research literatureMethodDeciphering the Clinical Trials of Immunotherapy in Glioblastoma: What a Neuroradiologist Needs to Know
Varzaneh FN, Merkaj S, Petersen GC, Bahar RC, Jekel L, Pala A, Malhotra A, Ivanidze J, Aboian M (2022) Deciphering the Clinical Trials of Immunotherapy in Glioblastoma: What a Neuroradiologist Needs to Know. Neurographics. 12(4):176-187. doi:10.3174/ng.2100055.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
2019
Acetylcholine Regulates Olfactory Perceptual Learning through Effects on Adult Neurogenesis.
Schilit Nitenson A, Manzano Nieves G, Poeta DL, Bahar R, Rachofsky C, Mandairon N, Bath KG. Acetylcholine Regulates Olfactory Perceptual Learning through Effects on Adult Neurogenesis. IScience 2019, 22: 544-556. PMID: 31855767, PMCID: PMC6926271, DOI: 10.1016/j.isci.2019.11.016.Peer-Reviewed Original Research