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
2023
Developing an Open Access Brain Metastasis Database: Yale Brain Metastasis Database
Ramakrishnan D, Jekel L, Sala M, Kaur M, Janas A, Petersen G, Bousabarah K, Lin M, Merkaj S, von Reppert M, Aboian M. Developing an Open Access Brain Metastasis Database: Yale Brain Metastasis Database. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0403.Peer-Reviewed Original Research
2021
OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review
Brim W, Jekel L, Petersen G, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Johnson M, Mahajan A, Aboian M. OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review. Neuro-Oncology Advances 2021, 3: iii17-iii17. PMCID: PMC8351249, DOI: 10.1093/noajnl/vdab071.067.Peer-Reviewed Original ResearchConvolutional neural networkDeep learningML algorithmsMachine Learning AlgorithmsApplication of machineClassical ML algorithmsDevelopment of machineSupport vector machine algorithmVector machine algorithmArtificial intelligenceMachine learningSearch strategyDL modelsLearning algorithmFeature extractionNeural networkMachine algorithmAverage accuracyML methodsCML algorithmAlgorithmHigh accuracyLearningMachineAccuracy
2020
PROOF-OF-CONCEPT USE OF MACHINE LEARNING TO PREDICT TUMOR RECURRENCE OF EARLY-STAGE HEPATOCELLULAR CARCINOMA BEFORE THERAPY USING BASELINE MAGNETIC RESONANCE IMAGING
Batra R, Kuecuekkaya A, Zeevi T, Raju R, Chai N, Haider S, Elbanan M, Petukhova A, Lin ,, Onofrey J, Nowak M, Cooper K, Thomas E, Gebauer B, Staib L, Chapiro J. PROOF-OF-CONCEPT USE OF MACHINE LEARNING TO PREDICT TUMOR RECURRENCE OF EARLY-STAGE HEPATOCELLULAR CARCINOMA BEFORE THERAPY USING BASELINE MAGNETIC RESONANCE IMAGING. Transplantation 2020, 104: s43-s44. DOI: 10.1097/01.tp.0000698472.65040.1e.Peer-Reviewed Original ResearchMachine learning
2018
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Abajian A, Murali N, Savic L, Laage-Gaupp F, Nezami N, Duncan J, Schlachter T, Lin M, Geschwind J, Chapiro J. Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma. Journal Of Visualized Experiments 2018 DOI: 10.3791/58382-v.Peer-Reviewed Original ResearchMachine learningImage-guided therapy