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 selectionRadiomics-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 circulationUncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
Tran A, Zeevi T, Haider S, Abou Karam G, Berson E, Tharmaseelan H, Qureshi A, Sanelli P, Werring D, Malhotra A, Petersen N, de Havenon A, Falcone G, Sheth K, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. Npj Digital Medicine 2024, 7: 26. PMID: 38321131, PMCID: PMC10847454, DOI: 10.1038/s41746-024-01007-w.Peer-Reviewed Original ResearchDeep learning modelsHematoma expansionIntracerebral hemorrhageICH expansionComputed tomographyNon-contrast head CTNon-contrast head computed tomographyHigh risk of HEHead computed tomographyHigh-confidence predictionsRisk of HENon-contrast headReceiver operating characteristic areaModifiable risk factorsMonte Carlo dropoutOperating characteristics areaPotential treatment targetHead CTVisual markersIdentified patientsAutomated deep learning modelDataset of patientsRisk factorsHigh riskPatients
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