2023
Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas
Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay E, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo I, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas. Scientific Reports 2023, 13: 22942. PMID: 38135704, PMCID: PMC10746716, DOI: 10.1038/s41598-023-48918-4.Peer-Reviewed Original ResearchConceptsInformatics platformDeep learning algorithmsImaging featuresCDKN2A alterationsLearning algorithmHeterozygous lossHomozygous deletionLarge datasetsDeep white matter invasionGBM molecular subtypesNew informaticsQualitative imaging biomarkersWhole-exome sequencingQualitative imaging featuresGBM resectionRadiographic evidenceWorse prognosisPACSMolecular subtypesPial invasionImaging biomarkersCDKN2A mutationsAllele statusNoninvasive identificationMagnetic resonance imagesP13.02.A APPLICATION OF NOVEL PACS-BASED INFORMATICS PLATFORM TO IDENTIFY IMAGING BASED PREDICTORS OF CDKN2A ALLELIC STATUS IN GLIOBLASTOMAS
Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay Z, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo I, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. P13.02.A APPLICATION OF NOVEL PACS-BASED INFORMATICS PLATFORM TO IDENTIFY IMAGING BASED PREDICTORS OF CDKN2A ALLELIC STATUS IN GLIOBLASTOMAS. Neuro-Oncology 2023, 25: ii100-ii101. PMCID: PMC10489329, DOI: 10.1093/neuonc/noad137.336.Peer-Reviewed Original ResearchImaging featuresPial invasionQualitative imaging biomarkersQualitative imaging featuresWorse prognosisImaging biomarkersCDKN2A mutationsMethods Sixty-nine patientsCDKN2A alterationsHomozygous deletionHeterozygous lossSixty-nine patientsDeep white matterDeep white matter invasionGBM molecular subtypesWhole-exome sequencingNine patientsGBM resectionRadiographic evidenceMolecular subtypesBACKGROUND GliomasWhite matterAllele statusNoninvasive identificationGliomasComparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation
Avesta A, Hossain S, Lin M, Aboian M, Krumholz H, Aneja S. Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering 2023, 10: 181. PMID: 36829675, PMCID: PMC9952534, DOI: 10.3390/bioengineering10020181.Peer-Reviewed Original ResearchLimited training dataDice scoreComputational memoryTraining dataBrain imagesDeep-learning methodsHigher Dice scoresSegmentation accuracyAuto-segmentation modelComputational speedPerformance metricsOne-sliceAuto-SegmentationBetter performanceConsecutive slicesImagesDeploymentLowest Dice scoresMemoryPerformanceTrainingMetricsModelAccuracyDataComparison 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 ResearchP13.05.B INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET
Tillmanns N, Lost J, Merkaj S, von Reppert M, Chadha S, Lin M, Bousabarah K, Huttner A, Aneja S, Avesta A, Omuro A, Aboian M. P13.05.B INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET. Neuro-Oncology 2023, 25: ii101-ii101. PMCID: PMC10489908, DOI: 10.1093/neuonc/noad137.339.Peer-Reviewed Original ResearchComparing 3D, 2.5D, and 2D Approaches to Brain MRI Segmentation
Avesta A, Hossain S, Lin M, Aboian M, Krumholz H, Aneja S. Comparing 3D, 2.5D, and 2D Approaches to Brain MRI Segmentation. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0804.Peer-Reviewed Original Research
2024
Acceleration of Volumetric Abdominal Aortic Aneurysm Measurements by Leveraging Artificial Intelligence
Weiss D, Hager T, Aboian M, Lin M, Bousabarah K, Renninghoff D, Holler W, Simmons K, Loh S, Fischer U, Deuschl C, Aneja S, Aboian E. Acceleration of Volumetric Abdominal Aortic Aneurysm Measurements by Leveraging Artificial Intelligence. Journal Of Vascular Surgery 2024, 80: e37-e38. DOI: 10.1016/j.jvs.2024.06.066.Peer-Reviewed Original ResearchArtificial Intelligence-based Morpho-volumetric Analysis of Pre- and Post-EVAR Infrarenal Abdominal Aortic Aneurysms Characterized on Computed Tomography Angiography
Weiss D, Hager T, Aboian M, Lin M, Renninghoff D, Holler W, Fischer U, Deuschl C, Aneja S, Aboian E. Artificial Intelligence-based Morpho-volumetric Analysis of Pre- and Post-EVAR Infrarenal Abdominal Aortic Aneurysms Characterized on Computed Tomography Angiography. Journal Of Vascular Surgery 2024, 79: e133-e134. DOI: 10.1016/j.jvs.2024.03.165.Peer-Reviewed Original ResearchComparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial
Ramakrishnan D, Brüningk S, von Reppert M, Memon F, Maleki N, Aneja S, Kazerooni A, Nabavizadeh A, Lin M, Bousabarah K, Molinaro A, Nicolaides T, Prados M, Mueller S, Aboian M. Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial. American Journal Of Neuroradiology 2024, 45: 475-482. PMID: 38453411, PMCID: PMC11288571, DOI: 10.3174/ajnr.a8189.Peer-Reviewed Original ResearchArea under the curvePediatric gliomasBT-RADSResponse assessmentPartial responseClinical trialsVolumetric analysisReceiver operating characteristic analysisBrain Tumor ReportingReceiver operating characteristic curveModel estimation timeOperating characteristic analysisEvaluate treatment efficacyStable diseasePartial respondersManual volumetric segmentationNo significant differenceSolid tumorsProspective studyTumor ReportingClinical decision-makingTreatment efficacyGliomaSignificant differenceCharacteristic curveA 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
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 ResearchNIMG-20. INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET
Lost J, Tillmans N, Merkaj S, von Reppert M, Lin M, Bousabarah K, Huttner A, Aneja S, Omuro A, Aboian M, Avesta A. NIMG-20. INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET. Neuro-Oncology 2022, 24: vii165-vii166. DOI: 10.1093/neuonc/noac209.638.Peer-Reviewed Original Research