2024
Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
Dierksen F, Sommer J, Tran A, Lin H, Haider S, Maier I, Aneja S, Sanelli P, Malhotra A, Qureshi A, Claassen J, Park S, Murthy S, Falcone G, Sheth K, Payabvash S. Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT). Diagnostics 2024, 14: 2827. PMID: 39767188, PMCID: PMC11674633, DOI: 10.3390/diagnostics14242827.Peer-Reviewed Original ResearchIntegrated discrimination indexNet reclassification indexPerihematomal edemaHead computed tomographyIntracerebral hemorrhageComputed tomographyClinical variablesClinical predictors of poor outcomeOutcome predictionAcute supratentorial intracerebral hemorrhageAdmission head computed tomographyRadiomic featuresNon-contrast head computed tomographyPredictors of poor outcomeModified Rankin Scale scoreIntracerebral hemorrhage scoreSupratentorial intracerebral hemorrhageIntracerebral hemorrhage patientsClinical risk factorsRankin Scale scoreReceiver operating characteristic areaOperating characteristics areaSecondary brain injuryHematoma evacuationPatient selectionArtificial Intelligence–Driven Patient Selection for Preoperative Portal Vein Embolization for Patients with Colorectal Cancer Liver Metastases
Kuhn T, Engelhardt W, Kahl V, Alkukhun A, Gross M, Iseke S, Onofrey J, Covey A, Camacho J, Kawaguchi Y, Hasegawa K, Odisio B, Vauthey J, Antoch G, Chapiro J, Madoff D. Artificial Intelligence–Driven Patient Selection for Preoperative Portal Vein Embolization for Patients with Colorectal Cancer Liver Metastases. Journal Of Vascular And Interventional Radiology 2024, 36: 477-488. PMID: 39638087, DOI: 10.1016/j.jvir.2024.11.025.Peer-Reviewed Original ResearchTotal liver volumeMetastatic colorectal cancer patientsPreoperative portal vein embolizationColorectal cancer liver metastasesPortal vein embolizationCancer liver metastasesMulticenter retrospective studyColorectal cancer patientsStudent's t-testBoard-certified radiologistsVein embolizationConsecutive patientsLiver metastasesLiver volumePatient selectionRetrospective studyCancer patientsRadiomic featuresInclusion criteriaPatientsSemi-automatic segmentationLab valuesT-testSDAUCPredicting Radiation-Induced Acute Toxicity in Breast Cancer Patients: A Radiogenomic Approach
Wei S, Zhang Y, Sowmiyanarayanan S, Yehia Z, Jan I, Yue N, Haffty B, Nie K. Predicting Radiation-Induced Acute Toxicity in Breast Cancer Patients: A Radiogenomic Approach. International Journal Of Radiation Oncology • Biology • Physics 2024, 120: e341. DOI: 10.1016/j.ijrobp.2024.07.752.Peer-Reviewed Original ResearchBreast cancer patientsRadiation dermatitisSide effectsCancer patientsBreast cancer patients treated with radiationRadiogenomic approachCancer patients treated with radiationRadiation-induced acute toxicityPatients treated with radiationRadiomic featuresSingle nucleotide polymorphismsAcute radiation dermatitisBreast-conserving surgeryRadiation side effectsPeripheral blood samplesTotal radiation dosePersonalized treatment strategiesGenetic statusRadiation therapyMammographic patternsRadiomic texture featuresTherapy side effectsClinical parametersRetrospective studyClinical featuresRadiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival
Zaman S, Dierksen F, Knapp A, Haider S, Karam G, Qureshi A, Falcone G, Sheth K, Payabvash S. Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival. Diagnostics 2024, 14: 944. PMID: 38732358, PMCID: PMC11083693, DOI: 10.3390/diagnostics14090944.Peer-Reviewed Original ResearchInternational Normalized RatioArea under the curveNational Institutes of Health Stroke ScaleIntracerebral hemorrhageRadiomic featuresAntihypertensive Treatment of Acute Cerebral Hemorrhage IINon-contrast head CT scanBaseline International Normalized RatioAssociated with worse survival outcomesAcute intracerebral hemorrhageSupratentorial intracerebral hemorrhageWorse survival outcomesKaplan-Meier analysisHead CT scanCox proportional hazards modelsPredictors of mortalityAcute cerebral hemorrhageReceiver Operating Characteristic (ROC) analysisFirst-order energyHigher mortality riskProportional hazards modelHealth Stroke ScaleCT radiomicsHematoma expansionPost-ICHAutomated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning
Gross M, Haider S, Ze’evi T, Huber S, Arora S, Kucukkaya A, Iseke S, Gebauer B, Fleckenstein F, Dewey M, Jaffe A, Strazzabosco M, Chapiro J, Onofrey J. Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning. European Radiology 2024, 34: 6940-6952. PMID: 38536464, PMCID: PMC11399284, DOI: 10.1007/s00330-024-10624-8.Peer-Reviewed Original ResearchContrast-enhanced magnetic resonance imagingMagnetic resonance imagingClinical staging systemTime of diagnosisHepatocellular carcinomaClinical dataMortality risk predictionOverall survivalStaging systemRadiomic featuresManagement of hepatocellular carcinomaPersonalized follow-up strategiesAssociated with OSMethodsThis retrospective studyHepatocellular carcinoma patientsBaseline magnetic resonance imagingMRI radiomics featuresIndependent validation cohortHarrell's C-indexRisk predictionFollow-up strategiesHigh-risk groupPredictive risk scoreRadiomics feature extractionMedian timeImpact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning–Generated Biomarkers
Haider S, Zeevi T, Sharaf K, Gross M, Mahajan A, Kann B, Judson B, Prasad M, Burtness B, Aboian M, Canis M, Reichel C, Baumeister P, Payabvash S. Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning–Generated Biomarkers. Journal Of Nuclear Medicine 2024, 65: jnumed.123.266637. PMID: 38514087, PMCID: PMC11927063, DOI: 10.2967/jnumed.123.266637.Peer-Reviewed Original ResearchOropharyngeal squamous cell carcinomaSquamous cell carcinomaHuman papillomavirusRadiomic featuresIntraclass correlation coefficientCell carcinomaLentiform nucleusHuman papillomavirus statusReceiver-operating-characteristic analysisReceiver-operating-characteristic curvePET radiomic featuresUnivariate logistic regressionF-FDGPrimary tumorTraining cohortValidation cohortRadiomic biomarkersUnivariate analysisInterindividual comparabilityPredictive valueDegree of reproducibilityMedian areaRadiomic markersLogistic regressionAUCRadiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke
Avery E, Abou-Karam A, Abi-Fadel S, Behland J, Mak A, Haider S, Zeevi T, Sanelli P, Filippi C, Malhotra A, Matouk C, Falcone G, Petersen N, Sansing L, Sheth K, Payabvash S. Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke. Diagnostics 2024, 14: 485. PMID: 38472957, PMCID: PMC10930945, DOI: 10.3390/diagnostics14050485.Peer-Reviewed Original ResearchCollateral statusCollateral scoreLarge vessel occlusionAcute LVO strokeRadiomics modelTest cohortCT angiography of patientsAdmission computed tomography angiographyAnterior circulation territoryAngiography of patientsLong-term outcomesReceiver operating characteristic areaRadiomics-based predictionCollateral arterial circulationOperating characteristics areaAdmission CTACirculation territoryCT angiographyClinical outcomesRadiomic featuresTreatment triageOcclusion strokeVessel occlusionPatientsArterial circulationAutomated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics
Gross M, Huber S, Arora S, Ze’evi T, Haider S, Kucukkaya A, Iseke S, Kuhn T, Gebauer B, Michallek F, Dewey M, Vilgrain V, Sartoris R, Ronot M, Jaffe A, Strazzabosco M, Chapiro J, Onofrey J. Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics. European Radiology 2024, 34: 5056-5065. PMID: 38217704, PMCID: PMC11245591, DOI: 10.1007/s00330-023-10495-5.Peer-Reviewed Original ResearchMagnetic resonance imagingRadiomics feature extractionLiver volumetryIntraclass correlation coefficientRadiomic featuresLiver segmentationAutomated liver volumetryHepatocellular carcinoma patientsMann-Whitney U testAutomated liver segmentationManual segmentationQuantitative imaging biomarkersCarcinoma patientsRetrospective studyInstitutional databaseAnatomical localizationClinical relevanceManual volumetryMann-WhitneyU testExternal validationInternal test setImaging biomarkersInclusion criteriaResultsIn total
2023
Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers
Haider S, Qureshi A, Jain A, Tharmaseelan H, Berson E, Zeevi T, Werring D, Gross M, Mak A, Malhotra A, Sansing L, Falcone G, Sheth K, Payabvash S. Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers. Frontiers In Neuroscience 2023, 17: 1225342. PMID: 37655013, PMCID: PMC10467422, DOI: 10.3389/fnins.2023.1225342.Peer-Reviewed Original ResearchIndependent validation cohortIntracerebral hemorrhageRadiomic featuresValidation cohortClinical variablesHematoma expansionSpontaneous supratentorial intracerebral hemorrhageNon-contrast head CTSupratentorial intracerebral hemorrhageTomography (CT) of patientsNon-contrast headFuture clinical trialsNon-contrast CTIntracerebral Hemorrhage ExpansionHigh predictive valueBAT scoreHypertensive patientsClinical predictorsPrognostic relevanceFunctional outcomeClinical trialsHead CTHemorrhage expansionClinical trial datasetDiscovery cohortA Multiclass Radiomics Method–Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans
Henao J, Depotter A, Bower D, Bajercius H, Todorova P, Saint-James H, de Mortanges A, Barroso M, He J, Yang J, You C, Staib L, Gange C, Ledda R, Caminiti C, Silva M, Cortopassi I, Dela Cruz C, Hautz W, Bonel H, Sverzellati N, Duncan J, Reyes M, Poellinger A. A Multiclass Radiomics Method–Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans. Investigative Radiology 2023, 58: 882-893. PMID: 37493348, PMCID: PMC10662611, DOI: 10.1097/rli.0000000000001005.Peer-Reviewed Original ResearchConceptsCOVID-19 positive patientsClinical Progression ScaleLung lesionsLesion modelDisease severityGround-glass opacitiesCOVID-19 patientsRadiologist assessmentExpert thoracic radiologistsMulticenter cohortPleural effusionDisease extentRetrospective studyDevelopment cohortPatient assessmentTomography scanCT scanSeverity ScalePatient's diseaseTissue lesionsThoracic radiologistsLesionsPatientsRadiomics modelRadiomic featuresNovel use of alternate (Alt) response (Rp) criteria (Cr) for early prediction of outcomes in pancreatic (P) neuroendocrine tumors (NETs): Utilizing banked imaging data from the ECOG-ACRIN E2211 study.
Vijayvergia N, Handorf E, Kunz P, Alkim E, Burke L, Catalano P, Graham N, Levin L, Li W, Meeker C, Rubin D, Narasimhan Sridharan A, O'Dwyer P, Wong T, Anaokar J. Novel use of alternate (Alt) response (Rp) criteria (Cr) for early prediction of outcomes in pancreatic (P) neuroendocrine tumors (NETs): Utilizing banked imaging data from the ECOG-ACRIN E2211 study. Journal Of Clinical Oncology 2023, 41: 4133-4133. DOI: 10.1200/jco.2023.41.16_suppl.4133.Peer-Reviewed Original ResearchProgression-free survivalImproved progression-free survivalNeuroendocrine tumorsStable diseaseProgressive diseaseRadiomic featuresCT/MRI scansPancreatic neuroendocrine tumorsPortal venous phaseShort-term imagingSmaller threshold changesInter-reader agreementTumor sizeC-statisticFirst disease assessmentPotential adjunctTreatment decisionsVenous phasePD casesTime-varying outcomeMRI scansClinical practicePredictive valueTumor densityInter-reader variability
2022
Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease
Ben Bashat D, Thaler A, Lerman Shacham H, Even-Sapir E, Hutchison M, Evans K, Orr-Urterger A, Cedarbaum J, Droby A, Giladi N, Mirelman A, Artzi M. Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease. Npj Parkinson's Disease 2022, 8: 139. PMID: 36271084, PMCID: PMC9586960, DOI: 10.1038/s41531-022-00405-9.Peer-Reviewed Original ResearchParkinson's diseaseGenotype-related differencesDAT-SPECTRadiomic featuresSymptomatic Parkinson's diseaseSignificant correlationProdromal phaseBrain regionsNeuromelanin MRIIron accumulationDiseaseMRIAssessment of individualsImaging valuesRadiomics analysisSignificant differencesPatientsNeuromelaninRiskAgeScoresT2Ratio scoresGroupLR scoresDataset on acute stroke risk stratification from CT angiographic radiomics
Avery EW, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. Dataset on acute stroke risk stratification from CT angiographic radiomics. Data In Brief 2022, 44: 108542. PMID: 36060820, PMCID: PMC9428796, DOI: 10.1016/j.dib.2022.108542.Peer-Reviewed Original ResearchMachine Learning FrameworkImage processing technologyFeature selection algorithmField of radiomicsRadiomics-based analysisMachine learningMedical imagesSelection algorithmAssistance toolRadiomic featuresRadiomics dataProcessing technologyAnalysis frameworkRelevant informationRadiomics algorithmAlgorithmCT angiography imagesRadiomicsMethodological supportExternal testingFrameworkImagesAngiography imagesMachineFeaturesCT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke
Avery EW, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke. NeuroImage Clinical 2022, 34: 103034. PMID: 35550243, PMCID: PMC9108990, DOI: 10.1016/j.nicl.2022.103034.Peer-Reviewed Original ResearchConceptsLarge vessel occlusion strokeIndependent cohortPrognostication toolsMechanical thrombectomyRisk stratificationOcclusion strokeExternal cohortAnterior circulation large vessel occlusion strokeOutcome predictionAcute stroke triageAnterior circulation territoryRadiomic featuresTime of admissionGeisinger Medical CenterLVO stroke patientsReliable clinical informationSignificant differencesAdmission CTAStroke patientsPrognostic informationFavorable outcomeStroke triageTreatment decisionsMedical CenterRadiomics signatureMR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features
Petukhova-Greenstein A, Zeevi T, Yang J, Chai N, DiDomenico P, Deng Y, Ciarleglio M, Haider SP, Onyiuke I, Malpani R, Lin M, Kucukkaya AS, Gottwald LA, Gebauer B, Revzin M, Onofrey J, Staib L, Gunabushanam G, Taddei T, Chapiro J. MR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features. Journal Of Vascular And Interventional Radiology 2022, 33: 814-824.e3. PMID: 35460887, PMCID: PMC9335926, DOI: 10.1016/j.jvir.2022.04.006.Peer-Reviewed Original ResearchConceptsProgression-free survivalPoor progression-free survivalLiver Imaging ReportingHepatocellular carcinomaMR imaging biomarkersRadiomics signatureRadiofrequency ablationRadiomic featuresImaging biomarkersImaging ReportingFirst follow-up imagingMedian progression-free survivalRF ablationEarly-stage hepatocellular carcinomaPretreatment magnetic resonanceFirst-line treatmentMultifocal hepatocellular carcinomaSelection operator Cox regression modelTherapy-naïve patientsEarly-stage diseaseKaplan-Meier analysisCox regression modelLog-rank testFollow-up imagingPrediction of outcomeMachine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
Petersen G, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. American Journal Of Neuroradiology 2022, 43: 526-533. PMID: 35361577, PMCID: PMC8993193, DOI: 10.3174/ajnr.a7473.Peer-Reviewed Original ResearchConceptsMachine learning-based methodsLearning-based methodsBalanced data setData setsVector machine modelMachine learningClassification algorithmsMachine modelMachineAlgorithmData basesPrediction modelPromising resultsPrimary CNS lymphomaPrediction model study RiskRisk of biasRadiomic featuresClassifierSetCNS lymphomaWebLearningFeaturesQualitySystematic review
2021
Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade.
Pulvirenti A, Yamashita R, Chakraborty J, Horvat N, Seier K, McIntyre C, Lawrence S, Midya A, Koszalka M, Gonen M, Klimstra D, Reidy D, Allen P, Do R, Simpson A. Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade. JCO Clinical Cancer Informatics 2021, 5: 679-694. PMID: 34138636, PMCID: PMC8462651, DOI: 10.1200/cci.20.00121.Peer-Reviewed Original ResearchConceptsTumor gradePanNET gradeCT scanComputed tomographyManagement of pancreatic neuroendocrine tumorsPancreatic neuroendocrine tumor gradesRadiographic descriptorsArterial phase CT scansNeuroendocrine tumor gradingTumor grade assessmentPancreatic neuroendocrine tumorsQuantitative image analysisComputed tomography image analysisResected PanNETsGrade tumorsNeuroendocrine tumorsPrimary tumorInstitutional databaseProspective studyTherapeutic managementUnivariate analysisPanNETsTreatment selectionRadiomic featuresTumorAI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?
Pasquini L, Napolitano A, Lucignani M, Tagliente E, Dellepiane F, Rossi-Espagnet M, Ritrovato M, Vidiri A, Villani V, Ranazzi G, Stoppacciaro A, Romano A, Di Napoli A, Bozzao A. AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well? Frontiers In Oncology 2021, 11: 601425. PMID: 34888226, PMCID: PMC8649764, DOI: 10.3389/fonc.2021.601425.Peer-Reviewed Original ResearchHigh-grade gliomasKi-67 expressionEpidermal growth factor receptor vIIIMagnetic resonance imagingOverall survivalKi-67Pathologic diagnosis of high-grade gliomaRadiomics modelRadiomic featuresDiagnosis of high-grade gliomaO-6-methylguanine-DNA methyltransferaseContrast-enhancing tumorOutcome predictionIsocitrate dehydrogenase mutationAdvanced magnetic resonance imagingConfidence intervalsNon-enhancing tumorTumor histologyMGMT methylationPathological diagnosisAdult patientsClinical dataPromoter methylationResonance imagingTumorGlioblastoma radiomics to predict survival: Diffusion characteristics of surrounding nonenhancing tissue to select patients for extensive resection
Pasquini L, Di Napoli A, Napolitano A, Lucignani M, Dellepiane F, Vidiri A, Villani V, Romano A, Bozzao A. Glioblastoma radiomics to predict survival: Diffusion characteristics of surrounding nonenhancing tissue to select patients for extensive resection. Journal Of Neuroimaging 2021, 31: 1192-1200. PMID: 34231927, DOI: 10.1111/jon.12903.Peer-Reviewed Original ResearchConceptsYear old patientSurvival stratificationOlder patientsRadiomic featuresExtent of resectionImprove patient selectionPrimary CNS neoplasmsCox regression modelsStandard of careLogistic regression analysisT-testNonenhancing tissueTumor resectionExtensive surgeryStandard therapyMRI pre-Patient selectionCNS neoplasmsGlioblastoma patientsMRI featuresRadiomics modelResectionEnhancing portionTreatment protocolsRisk factorsComparison of radiomic feature aggregation methods for patients with multiple tumors
Chang E, Joel MZ, Chang HY, Du J, Khanna O, Omuro A, Chiang V, Aneja S. Comparison of radiomic feature aggregation methods for patients with multiple tumors. Scientific Reports 2021, 11: 9758. PMID: 33963236, PMCID: PMC8105371, DOI: 10.1038/s41598-021-89114-6.Peer-Reviewed Original ResearchConceptsCox proportional hazards modelCox proportional hazardsProportional hazards modelBrain metastasesRadiomic featuresHazards modelProportional hazardsStandard Cox proportional hazards modelMultifocal brain metastasesMultiple brain metastasesNumber of patientsPatient-level outcomesHigher concordance indexRadiomic feature analysisRandom survival forest modelSurvival modelsDifferent tumor volumesMultifocal tumorsCancer outcomesMultiple tumorsMetastatic cancerConcordance indexTumor volumePatientsTumor types
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