2024
NIMG-30. DEUTERIUM METABOLIC IMAGING (DMI) SHOWS A STRONG RELATION BETWEEN TUMOR GRADE AND GLUCOSE METABOLISM IN PRIMARY BRAIN TUMORS
Thaw-Poon S, Blondin N, Liu Y, Corbin Z, Baehring J, Omuro A, Moliterno J, Omay S, Fulbright R, de Graaf R, De Feyter H. NIMG-30. DEUTERIUM METABOLIC IMAGING (DMI) SHOWS A STRONG RELATION BETWEEN TUMOR GRADE AND GLUCOSE METABOLISM IN PRIMARY BRAIN TUMORS. Neuro-Oncology 2024, 26: viii201-viii201. PMCID: PMC11553074, DOI: 10.1093/neuonc/noae165.0795.Peer-Reviewed Original ResearchGrade 2 lesionsTumor gradeDeuterium metabolic imagingMetabolic imagingNon-enhancing tumor regionsBrain tumorsTumor-to-brain contrastTumour-specific valuesActive tumor tissueImage contrastVOI-based analysisGrade 4Evaluate disease progressionTesla MRI scannerFDG-PETGrade 3Lesion gradeTumor tissuesDisease progressionDisease stageOral administrationTumorObservational studyNormal brainContrast enhancementJS07.4.A A PHASE 0/IA STUDY OF BRIGIMADLIN CONCENTRATION IN BRAIN TISSUE AND A DOSE ESCALATION STUDY OF BRIGIMADLIN PLUS RADIOTHERAPY IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA
Sarkaria J, Mrugala M, Jaeckle K, Burns T, Vaubel R, Parney I, Chaichana K, Clement P, Martínez-García M, Sanchez J, Omuro A, Pronk L, Ross H, Teufel M, Hesse R, Grempler R, Galanis E. JS07.4.A A PHASE 0/IA STUDY OF BRIGIMADLIN CONCENTRATION IN BRAIN TISSUE AND A DOSE ESCALATION STUDY OF BRIGIMADLIN PLUS RADIOTHERAPY IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA. Neuro-Oncology 2024, 26: v13-v13. PMCID: PMC11485993, DOI: 10.1093/neuonc/noae144.036.Peer-Reviewed Original ResearchNon-contrast-enhancedMouse double minute 2Contrast enhancementMGMT promoter unmethylated glioblastomaPatient-derived xenograft modelsUnbound concentrationsCalculated unbound concentrationsNewly diagnosed GBMMaximum tolerated doseNewly diagnosed glioblastomaTumor cell apoptosisSingle-arm trialMDM2-p53 antagonistsDouble minute 2Restore wild-typeBrain tissueBrain tumor tissueTP53 WTUnmethylated glioblastomaPhase 0Diagnosed glioblastomaTolerated doseOpen-labelP53 target gene expressionIDH-wtEnhancing Clinical Decision-Making: An Externally Validated Machine Learning Model for Predicting IDH Mutation in Gliomas using Radiomics from Pre-Surgical MRI
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, De Lin M, Aneja S, Sabel M, Aboian M. Enhancing Clinical Decision-Making: An Externally Validated Machine Learning Model for Predicting IDH Mutation in Gliomas using Radiomics from Pre-Surgical MRI. Neuro-Oncology Advances 2024, vdae157. 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 tumorsA phase (Ph) 0/Ia study of brigimadlin concentration in brain tissue and a non-randomized, open-label, dose escalation study of brigimadlin in combination with radiotherapy (RT) in patients (pts) with newly diagnosed glioblastoma (GBM).
Sarkaria J, Mrugala M, Jaeckle K, Burns T, Vaubel R, Parney I, Chaichana K, Clement P, Martinez-Garcia M, Sepulveda Sanchez J, Omuro A, Pronk L, Ross H, Teufel M, Hesse R, Grempler R, Galanis E. A phase (Ph) 0/Ia study of brigimadlin concentration in brain tissue and a non-randomized, open-label, dose escalation study of brigimadlin in combination with radiotherapy (RT) in patients (pts) with newly diagnosed glioblastoma (GBM). Journal Of Clinical Oncology 2024, 42: 2017-2017. DOI: 10.1200/jco.2024.42.16_suppl.2017.Peer-Reviewed Original ResearchNon-contrast-enhancedOpen-labelContrast enhancementMGMT promoter unmethylated glioblastomaUnbound concentrationsCalculated unbound concentrationsNewly diagnosed GBMDose-escalation studyMaximum tolerated doseTumor cell apoptosisSingle-arm trialMDM2-p53 antagonistsRestore wild-typeBrain tissueUnmethylated glioblastomaBrain tumor tissueEscalation studyDiagnosed glioblastomaTolerated doseP53 target gene expressionPrimary endpointIDH-wtSolid tumorsTumor tissuesXenograft modelRadiotherapy Plan Quality Assurance in NRG Oncology Trials for Brain and Head/Neck Cancers: An AI-Enhanced Knowledge-Based Approach
Wang D, Geng H, Gondi V, Lee N, Tsien C, Xia P, Chenevert T, Michalski J, Gilbert M, Le Q, Omuro A, Men K, Aldape K, Cao Y, Srinivasan A, Barani I, Sachdev S, Huang J, Choi S, Shi W, Battiste J, Wardak Z, Chan M, Mehta M, Xiao Y. Radiotherapy Plan Quality Assurance in NRG Oncology Trials for Brain and Head/Neck Cancers: An AI-Enhanced Knowledge-Based Approach. Cancers 2024, 16: 2007. PMID: 38893130, PMCID: PMC11171017, DOI: 10.3390/cancers16112007.Peer-Reviewed Original ResearchIntensity-modulated proton therapyPlan quality assuranceOrgan-at-riskRT planningQuality of radiation therapyClinical trialsIMPT plansKBP modelPlan QAPhoton plansRadiation therapyPhoton modelKBP plansTarget coveragePlan qualityProton therapyNasopharyngeal carcinomaMulti-institutional clinical trialsNRG Oncology trialsProton modelOutcomes of clinical trialsMulti-center clinical trialPhoton RTQuality assuranceHead/neck cancer
2023
CTNI-41. PHASE II AND PHASE 0 RESULTS OF ABTC 1801: A MULTI-ARM CLINICAL TRIAL OF THE PARP INHIBITOR PAMIPARIB WITH VERY LOW DOSE METRONOMIC TEMOZOLOMIDE IN RECURRENT IDH MUTANT GLIOMAS
Schiff D, Bindra R, Li J, Ye X, Ellingson B, Walbert T, Campian J, Nabors B, Lieberman F, Ozer B, Desai A, Omuro A, Wen P, Desideri S, Danda N, Grossman S. CTNI-41. PHASE II AND PHASE 0 RESULTS OF ABTC 1801: A MULTI-ARM CLINICAL TRIAL OF THE PARP INHIBITOR PAMIPARIB WITH VERY LOW DOSE METRONOMIC TEMOZOLOMIDE IN RECURRENT IDH MUTANT GLIOMAS. Neuro-Oncology 2023, 25: v84-v84. PMCID: PMC10639307, DOI: 10.1093/neuonc/noad179.0323.Peer-Reviewed Original ResearchObjective response rateArm AMetronomic temozolomideArm BGrade 3Non-enhancing tumorMeaningful objective response rateTumor/plasma ratioCumulative hematologic toxicityGrade 3 anemiaGrade 3 thrombocytopeniaGrade 4 neutropeniaPhase II componentArm clinical trialPhase IIKPS 90Median PFSHematologic toxicityPrimary endpointAlkylator therapyProgressive diseaseARM patientsMedian ageBRCAness phenotypeClinical trialsTrial in Progress: An Open-Label Expansion Trial Evaluating the Safety, PK/PD, and Clinical Activity of Emavusertib (CA-4948) + Ibrutinib in R/R Primary CNS Lymphoma
Grommes C, Nowakowski G, Rosenthal A, Lunning M, Ramchandren R, Regales L, Fowle M, Lane M, Wang C, Omuro A, Leslie L, Soussain C, Dabrowska-Iwanicka A, Ferreri A, Tun H. Trial in Progress: An Open-Label Expansion Trial Evaluating the Safety, PK/PD, and Clinical Activity of Emavusertib (CA-4948) + Ibrutinib in R/R Primary CNS Lymphoma. Blood 2023, 142: 3143. DOI: 10.1182/blood-2023-190391.Peer-Reviewed Original ResearchPrimary central nervous system lymphomaInterleukin-1 receptor-associated kinase 4FMS-like tyrosine kinase 3Toll-like receptorsExpansion cohortPCNSL patientsDisease progressionRefractory primary central nervous system lymphomaDiagnosis of PCNSLCentral nervous system lymphomaPathogenesis of PCNSLSafety/tolerabilityOpen-label trialBlood-brain barrier penetrationPrimary CNS lymphomaSufficient blood-brain barrier penetrationNervous system lymphomaInitial clinical dataKey inclusion criteriaPotent oral inhibitorCentral nervous systemBrain barrier penetrationPK/PDTyrosine kinase 3Further preclinical studiesBSBM-18 SINGLE-CELL PROFILING TUMOR-INFILTRATING IMMUNE CELLS REVEALS CXCL13+ FOLLICULAR HELPER-LIKE CD4+ T CELLS IN HUMAN BRAIN TUMORS
Lu B, Lucca L, DiStasio M, Liu Y, Pham G, Buitrago-Pocasangre N, Arnal-Estape A, Moliterno J, Chiang V, Omuro A, Hafler D. BSBM-18 SINGLE-CELL PROFILING TUMOR-INFILTRATING IMMUNE CELLS REVEALS CXCL13+ FOLLICULAR HELPER-LIKE CD4+ T CELLS IN HUMAN BRAIN TUMORS. Neuro-Oncology Advances 2023, 5: iii4-iii4. PMCID: PMC10402449, DOI: 10.1093/noajnl/vdad070.014.Peer-Reviewed Original ResearchT cell populationsT cell functionT cellsHigh-grade gliomasBrain metastasesHuman brain tumorsImmune cellsBrain tumorsNon-small cell lung cancer brain metastasesB cellsAnti-PD-1 therapy responseCell lung cancer brain metastasesLung cancer brain metastasesProductive antitumor immune responsesFollicular helper T cellsT-cell receptor sequencingTumor-infiltrating T cellsAntitumor T-cell functionCancer brain metastasesCo-inhibitory receptorsAntitumor immune responseCell receptor sequencingLonger overall survivalCell functionTertiary lymphoid structuresImaging metabolism of deuterated glucose in patients with primary brain tumors
Corbin Z, Liu Y, Fulbright R, Thaw-Poon S, Baehring J, Blondin N, Kim P, Omuro A, Chiang V, Moliterno J, Omay S, Piepmeier J, Rothman D, de Graaf R, De Feyter H. Imaging metabolism of deuterated glucose in patients with primary brain tumors. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0142.Peer-Reviewed Original ResearchThe Yale Glioma Dataset: Developing An Open Access, Annotated MRI Database
Sala M, Lost J, Tillmanns N, Merkaj S, von Reppert M, Ramakrishnan D, Bousabarah K, Huttner A, Aneja S, Avesta A, Omuro A, Aboian M. The Yale Glioma Dataset: Developing An Open Access, Annotated MRI Database. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/4511.Peer-Reviewed Original ResearchComparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging
Joel M, Avesta A, Yang D, Zhou J, Omuro A, Herbst R, Krumholz H, Aneja S. Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging. Cancers 2023, 15: 1548. PMID: 36900339, PMCID: PMC10000732, DOI: 10.3390/cancers15051548.Peer-Reviewed Original ResearchAdversarial imagesDeep learning modelsDL modelsDetection modelLearning modelConvolutional neural networkDetection schemeAdversarial detectionDefense techniquesMachine learningMedical imagesAdversarial perturbationsInput imageAdversarial trainingNeural networkArt performanceMagnetic resonance imagingGradient descentPixel valuesHigh accuracyImagesBrain magnetic resonance imagingAbsence of malignancyClassificationSchemeTop advances of the year: Neuro‐oncology
Barden M, Omuro A. Top advances of the year: Neuro‐oncology. Cancer 2023, 129: 1467-1472. PMID: 36825454, DOI: 10.1002/cncr.34711.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBrain tumorsRecent phase 3 trialAnti-PD-1 immunotherapyCentral nervous system dysfunctionSingle-agent pembrolizumabHigh-dose chemotherapyPhase 3 trialPrimary CNS lymphomaStem cell transplantationLong-term outcomesLimited therapeutic optionsNervous system dysfunctionOngoing clinical trialsClinical trial landscapeDrug Administration approvalBRAF V600E mutationExcellent disease controlConsolidation therapyCNS lymphomaImproved survivalLeptomeningeal metastasesTherapeutic optionsCell transplantationCraniospinal irradiationPatient populationMulticenter Phase 2 Trial of the PARP Inhibitor Olaparib in Recurrent IDH1 and IDH2-Mutant Glioma
Fanucci K, Pilat M, Shyr D, Shyr Y, Boerner S, Li J, Durecki D, Drappatz J, Puduvalli V, Lieberman F, Gonzalez J, Giglio P, Ivy S, Bindra R, Omuro A, LoRusso P. Multicenter Phase 2 Trial of the PARP Inhibitor Olaparib in Recurrent IDH1 and IDH2-Mutant Glioma. Cancer Research Communications 2023, 3: 192-201. PMID: 36968138, PMCID: PMC10035510, DOI: 10.1158/2767-9764.crc-22-0436.Peer-Reviewed Original ResearchConceptsProgression-free survivalMedian progression-free survivalProlonged stable diseaseStable diseasePhase II trialGrade 4 tumorsII trialOlaparib monotherapyGrade 2Multicenter phase 2 trialSingle-arm phase II trialWorld Health Organization classificationMedian overall survivalNeuro-Oncology criteriaPhase 2 trialOverall response rateFuture patient stratificationMutant gliomasPARP inhibitor olaparibEvaluable patientsPrimary endpointOverall survivalProgressive diseaseSelect patientsClinical benefit
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 ResearchMulticenter phase 2 trial of the PARP inhibitor (PARPi) olaparib in recurrent IDH1 and IDH2-mutant contrast-enhancing glioma.
Fanucci K, Pilat M, Shah R, Boerner S, Li J, Durecki D, Drappatz J, Collichio F, Puduvalli V, Lieberman F, Gonzalez J, Giglio P, Bao X, Ivy S, Bindra R, Omuro A, LoRusso P. Multicenter phase 2 trial of the PARP inhibitor (PARPi) olaparib in recurrent IDH1 and IDH2-mutant contrast-enhancing glioma. Journal Of Clinical Oncology 2022, 40: 2035-2035. DOI: 10.1200/jco.2022.40.16_suppl.2035.Peer-Reviewed Original ResearchProgression-free survivalMedian progression-free survivalStable diseaseDuration of responseOverall response ratePARP inhibitorsOverall survivalStandard therapyOlaparib monotherapyMulticenter phase 2 trialCDKN2A deletionClinical predictive markersGrade 3 lymphopeniaProlonged stable diseasePhase 2 trialGrade 4 tumorsFuture patient stratificationRecent preclinical studiesHigh-grade gliomasNovel drug combinationsContrast-enhancing gliomasEligible ptsEvaluable ptsRecent histologyPrimary endpointComparing Deep Learning and Classical Machine Learning Methods For Differentiating Primary CNS Lymphomas From Gliomas – A Systematic Review (P14-9.004)
Petersen G, Shatalov J, Verma T, Brim W, Merkaj S, Bahar R, Subramanian H, Cui J, Johnson M, Malhotra A, Omuro A, Aboian M. Comparing Deep Learning and Classical Machine Learning Methods For Differentiating Primary CNS Lymphomas From Gliomas – A Systematic Review (P14-9.004). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.2899.Peer-Reviewed Original ResearchIntegration of Machine Learning Into Clinical Radiology Practice – Development of a Machine Learning Tool for Preoperative Glioma Grade Prediction (P14-9.002)
Merkaj S, Zeevi T, Bousabarah K, Kazarian E, Lin M, Pala A, Petersen G, Jekel L, Bahar R, Tillmanns N, Cui J, Ikuta I, Bronen R, Fadel S, Westerhoff M, Omuro A, Aboian M. Integration of Machine Learning Into Clinical Radiology Practice – Development of a Machine Learning Tool for Preoperative Glioma Grade Prediction (P14-9.002). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3243.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 ResearchSemi-Automated Longitudinal Lesion Tracking In PACS Reveals High Proportion of Metastatic Lesions Showing Mixed Response To Radiosurgery (P14-9.001)
Petersen G, Verma T, Bousabarah K, Jekel L, Merkaj S, Bahar R, Fadel S, Ikuta I, Lin M, Omuro A, Aboian M. Semi-Automated Longitudinal Lesion Tracking In PACS Reveals High Proportion of Metastatic Lesions Showing Mixed Response To Radiosurgery (P14-9.001). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3451.Peer-Reviewed Original Research