2024
Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology
Zeevi T, Leapman M, Sprenkle P, Venkataraman R, Staib L, Onofrey J. Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology. IEEE Transactions On Biomedical Engineering 2024, 71: 1084-1091. PMID: 37874731, PMCID: PMC10901528, DOI: 10.1109/tbme.2023.3326799.Peer-Reviewed Original ResearchMagnetic resonance imagingIndividual patientsBiopsy locationProstate biopsy dataBiopsy histopathologyHistopathology scoresPathology scoresBiopsy dataMRI biomarkersTreatment planPatientsResonance imagingProstate regionBiomarkersTherapy treatment plansPathologyRepresentative sampleScoresImaging analysisPrevious studiesHistopathologyProstateCancerCliniciansRadiomics-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
2022
PET/CT radiomics potentially improves progression-free survival (PFS) and overall survival (OS) prognostication beyond UICC TNM staging in oropharyngeal squamous cell carcinoma (OPSCC) patients
Haider S, Sharaf K, Zeevi T, Mahajan A, Forghani R, Judson B, Kann B, Burtness B, Reichel C, Baumeister P, Payabvash S. PET/CT radiomics potentially improves progression-free survival (PFS) and overall survival (OS) prognostication beyond UICC TNM staging in oropharyngeal squamous cell carcinoma (OPSCC) patients. Laryngo-Rhino-Otologie 2022, 101: s184-s184. DOI: 10.1055/s-0042-1746471.Peer-Reviewed Original Research