2024
P10.25.A STANDARDIZATION AND AUTOMATIZATION OF MEASURING AND REPORTING BRAIN METASTASIS OVER TIME BY LEVERAGING ARTIFICIAL INTELLIGENCE
Weiss D, Bousabarah K, Deuschl C, Chadha S, Ashraf N, Ramakrishnan D, Moawad A, Osenberg K, Schoenherr S, Lautenschlager J, Holler W, Westerhoff M, Schrickel E, Memon F, Moily N, Malhotra A, Lin M, Aboian M. P10.25.A STANDARDIZATION AND AUTOMATIZATION OF MEASURING AND REPORTING BRAIN METASTASIS OVER TIME BY LEVERAGING ARTIFICIAL INTELLIGENCE. Neuro-Oncology 2024, 26: v61-v61. PMCID: PMC11485790, DOI: 10.1093/neuonc/noae144.201.Peer-Reviewed Original ResearchReports of brain metastasesBrain metastasesInter-observer variabilityFollow-up imaging of patientsPost-Gamma knife radiosurgeryBrain tumorsRANO-BM criteriaFollow-up imagingBoard-certified neuroradiologistsTreatment response monitoringMean Dice coefficientImages of patientsNnU-Net segmentationManual diameter measurementsBM evaluationRANO-BMRetrospective studyTreatment regimenSpearman correlation coefficientInter-rater variabilityMRI reportsIdentified lesionsPercentual changePost-gammaNeuroradiologistsA large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information
Ramakrishnan D, Jekel L, Chadha S, Janas A, Moy H, Maleki N, Sala M, Kaur M, Petersen G, Merkaj S, von Reppert M, Baid U, Bakas S, Kirsch C, Davis M, Bousabarah K, Holler W, Lin M, Westerhoff M, Aneja S, Memon F, Aboian M. A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. Scientific Data 2024, 11: 254. PMID: 38424079, PMCID: PMC10904366, DOI: 10.1038/s41597-024-03021-9.Peer-Reviewed Original ResearchConceptsWhole-brain radiotherapyStereotactic radiosurgeryT1 post-contrastBrain metastasesPost-contrastSide effectsImage informationArtificial intelligenceAssociated with cognitive side effectsContrast-enhancing lesionsQuality of datasetsCognitive side effectsFLAIR MR imagesValidation of AI modelsBrain radiotherapyLimitations of algorithmsStandard treatmentAI modelsMR imagingAI networksContrast enhancementClinical settingSegmentation workflowDatasetClinical adoption
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
2022
NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET
Jekel L, Bousabarah K, Lin M, Merkaj S, Kaur M, Avesta A, Aneja S, Omuro A, Chiang V, Scheffler B, Aboian M. NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET. Neuro-Oncology 2022, 24: vii162-vii162. PMCID: PMC9661012, DOI: 10.1093/neuonc/noac209.622.Peer-Reviewed Original ResearchSystematic Review of Machine Learning Models for Differentiation of Glioma from Brain Metastasis (P14-9.006)
Jekel L, Brim W, Petersen G, Merkaj S, Subramanian H, Zeevi T, Payabvash S, Khaled B, Lin M, Cui J, Brackett A, Johnson M, Omuro A, Scheffler B, Aboian M. Systematic Review of Machine Learning Models for Differentiation of Glioma from Brain Metastasis (P14-9.006). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3376.Peer-Reviewed Original ResearchMachine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
Jekel L, Brim WR, von Reppert M, Staib L, Petersen G, Merkaj S, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Omuro A, Johnson MH, Chiang VL, Malhotra A, Scheffler B, Aboian MS. Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review. Cancers 2022, 14: 1369. PMID: 35326526, PMCID: PMC8946855, DOI: 10.3390/cancers14061369.Peer-Reviewed Original ResearchBrain metastasesDifferentiation of gliomasMagnetic resonance imagingEligible studiesSystematic reviewSingle-center institutionConventional magnetic resonance imagingSpecific clinical circumstancesNon-invasive differentiationQuality of reportingClinical circumstancesPoor reportingClinical practiceModel assessmentResonance imagingMetastasisStudy designGliomasTRIPOD StatementMultiple studiesExternal validationClinical translationAdherenceDifferentiationReviewReal-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery
Petersen G, Bousabarah K, Verma T, von Reppert M, Jekel L, Gordem A, Jang B, Merkaj S, Fadel S, Owens R, Omuro A, Chiang V, Ikuta I, Lin M, Aboian MS. Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery. Neuro-Oncology Advances 2022, 4: vdac116. PMID: 36043121, PMCID: PMC9412827, DOI: 10.1093/noajnl/vdac116.Peer-Reviewed Original ResearchGamma KnifeTreatment responseBrain metastasis patientsFurther treatment planningNumber of lesionsMean followBrain metastasesMetastasis patientsMultiple lesionsDiagnostic followSingle patientStereotactic radiosurgeryPatientsLesionsIndividual lesionsPrevalenceAccurate surveillanceHeterogenous responseTreatment planningTreatmentFollowRadiosurgerySize assessmentComprehensive assessmentResponse
2021
OTHR-06. PACS Lesion Tracking Tool provides real time automatic information on brain tumor metastasis growth curves and RECIST criteria
Jang B, Lin M, Owens R, Bousabarah K, Mahajan A, Fadel S, Ikuta I, Tocino I, Aboian M. OTHR-06. PACS Lesion Tracking Tool provides real time automatic information on brain tumor metastasis growth curves and RECIST criteria. Neuro-Oncology Advances 2021, 3: iii15-iii15. DOI: 10.1093/noajnl/vdab071.061.Peer-Reviewed Original ResearchFirst brain MRIRECIST evaluationRECIST criteriaPatient managementBrain MRIResponse Evaluation CriteriaRadiology reportsBrain MRI studiesAdditional useful informationPost-gadolinium sequencesRECIST outcomeStable diseaseBrain metastasesClinical responsePartial responseProgressive diseaseComplete responseRadiologic impressionNew lesionsIntracranial metastasesClinical differencesTarget lesionsTreatment responseMRI studiesLesion sizeOTHR-15. Assessment of TRIPOD adherence in articles developing machine learning models for differentiation of glioma from brain metastasis
Jekel L, Brim W, Petersen G, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Johnson M, Malhotra A, Aboian M. OTHR-15. Assessment of TRIPOD adherence in articles developing machine learning models for differentiation of glioma from brain metastasis. Neuro-Oncology Advances 2021, 3: iii17-iii18. PMCID: PMC8351195, DOI: 10.1093/noajnl/vdab071.070.Peer-Reviewed Original Research