2024
Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging
Lost J, Ashraf N, Jekel L, von Reppert M, Tillmanns N, Willms K, Merkaj S, Petersen G, Avesta A, Ramakrishnan D, Omuro A, Nabavizadeh A, Bakas S, Bousabarah K, Lin M, Aneja S, Sabel M, Aboian M. Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging. Neuro-Oncology Advances 2024, 6: vdae157. PMID: 39659829, PMCID: PMC11630777, DOI: 10.1093/noajnl/vdae157.Peer-Reviewed Original ResearchIsocitrate dehydrogenase mutation statusArea under the curveMagnetic resonance imagingMutation statusML modelsMachine learningSemi-automated tumour segmentationsPre-surgical magnetic resonance imagingCare of glioma patientsMachine learning modelsClinical care of glioma patientsIsocitrate dehydrogenase statusAnnotated datasetsFeature extractionPrediction taskMulti-institutional dataModel trainingIDH mutationsPredicting IDH mutationLearning modelsRetrospective studyHeterogeneous datasetsTumor segmentationGlioma patientsBrain tumors
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 segmentationDevelopment of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003)
Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Development of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3146.Peer-Reviewed Original Research