2025
Towards Automating Risk Stratification of Intraductal Papillary Mucinous Neoplasms: Artificial Intelligence Advances Beyond Human Expertise with Confocal Laser Endomicroscopy
Krishna S, Abdelbaki A, Li Z, Culp S, Xiong X, Napoleon B, Mok S, Bertani H, Feng Y, Kongkam P, Luthra A, Machicado J, El-Dika S, Leblanc S, Tan D, Burlen J, Keane M, Keihanian T, Ladd A, Muniraj T, Visrodia K, Chen W, Esnakula A, Hart P, Chao W. Towards Automating Risk Stratification of Intraductal Papillary Mucinous Neoplasms: Artificial Intelligence Advances Beyond Human Expertise with Confocal Laser Endomicroscopy. Pancreatology 2025 PMID: 40447463, DOI: 10.1016/j.pan.2025.05.011.Peer-Reviewed Original ResearchNeedle-based confocal laser endomicroscopyIntraductal papillary mucinous neoplasmBD-IPMNInterobserver agreementArtificial intelligenceConfocal laser endomicroscopyLaser endomicroscopyBranch ductPapillary mucinous neoplasmPerformance of expertsHuman expertiseAI modelsPost hoc analysisMucinous neoplasmsDysplasia gradeEndoscopic ultrasoundClinical criteriaInterobserver variabilityCyst epitheliumSuperior accuracyDysplasiaAUCPost-hocArtificialDiagnostic parameters
2024
Artificial 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-testSDAUCExternal Validation of an Electronic Health Record–Based Diagnostic Model for Histological Acute Tubulointerstitial Nephritis
Moledina D, Shelton K, Menez S, Aklilu A, Yamamoto Y, Kadhim B, Shaw M, Kent C, Makhijani A, Hu D, Simonov M, O’Connor K, Bitzel J, Thiessen-Philbrook H, Wilson F, Parikh C. External Validation of an Electronic Health Record–Based Diagnostic Model for Histological Acute Tubulointerstitial Nephritis. Journal Of The American Society Of Nephrology 2024, 36: 859-868. PMID: 39500309, PMCID: PMC12059105, DOI: 10.1681/asn.0000000556.Peer-Reviewed Original ResearchJohns Hopkins HospitalAcute tubulointerstitial nephritisValidation cohortKidney biopsyTubulointerstitial nephritisDiagnosis of acute tubulointerstitial nephritisProportion of biopsiesElectronic health recordsAnalyzed patientsDevelopment cohortBaseline prevalenceAccurate diagnosisBiopsyCohortHealth recordsClinician's abilityDiagnostic modelPotential predictorsNephritisAssess discriminationKidneyAUCDevelopment and Internal-External Validation of a Post-Operative Mortality Risk Calculator for Pediatric Surgical Patients in Low- and Middle- Income Countries Using Machine Learning
Eyler Dang L, Klazura G, Yap A, Ozgediz D, Bryce E, Cheung M, Fedatto M, Ameh E. Development and Internal-External Validation of a Post-Operative Mortality Risk Calculator for Pediatric Surgical Patients in Low- and Middle- Income Countries Using Machine Learning. Journal Of Pediatric Surgery 2024, 59: 161883. PMID: 39317568, DOI: 10.1016/j.jpedsurg.2024.161883.Peer-Reviewed Original ResearchMiddle-income countriesPost-operative mortality ratePost-operative mortalityPediatric surgical patientsRisk algorithmPediatric surgery patientsMortality risk calculatorInternal-external validationSurgery patientsEligible patientsSurgical patientsCalibration slopeHospital dischargePatientsType of studyMortality rateClinical practiceCross-validated AUCIncome countriesRisk calculatorAUCMortalityDischarge statusValidation AUCImpact 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 regressionAUC
2023
Cytologic evaluation of upper urinary tract specimens: An institutional retrospective study using The Paris System for Reporting Urine Cytology second edition with histopathologic follow‐up
Khajir G, Sun T, Wang H, Sprenkle P, Adeniran A, Cai G, Levi A. Cytologic evaluation of upper urinary tract specimens: An institutional retrospective study using The Paris System for Reporting Urine Cytology second edition with histopathologic follow‐up. Cytopathology 2023, 35: 235-241. PMID: 37916579, DOI: 10.1111/cyt.13328.Peer-Reviewed Original ResearchHigh-grade urothelial carcinomaLow-grade urothelial carcinomaUpper urinary tractUrothelial carcinomaUrinary tractCytologic evaluationCytology specimensInstitutional retrospective studyParis SystemHigh-grade malignancyAtypical urothelial cellsCytologic categoriesRetrospective studyInstitutional databaseCytological diagnosisUrinary cytopathologyHistopathologicMalignant cellsBenign casesUrothelial cellsTPS categoryCarcinomaReporting systemTractAUC
2021
Performance of Platelet Mass Index as a Marker of Severity for Sepsis and Septic Shock in Children
Chegondi M, Vijayakumar N, Billa R, Badheka A, Karam O. Performance of Platelet Mass Index as a Marker of Severity for Sepsis and Septic Shock in Children. Journal Of Pediatric Intensive Care 2021, 12: 228-234. PMID: 37565022, PMCID: PMC10411082, DOI: 10.1055/s-0041-1731434.Peer-Reviewed Original ResearchPlatelet mass indexPediatric intensive care unitSeptic shockPICU admissionMass indexPrognostic indicatorFl/Day 1Marker of severityRetrospective observational studyIntensive care unitCare unitPediatric sepsisObservational studyClinical valueDay 3SepsisCharacteristic curveFurther studiesAdmissionPMI valuesChildrenAUCSpecificityNonsurvivorsPlacental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome
Radhakrishna U, Vishweswaraiah S, Uppala LV, Szymanska M, Macknis J, Kumar S, Saleem-Rasheed F, Aydas B, Forray A, Muvvala SB, Mishra NK, Guda C, Carey DJ, Metpally RP, Crist RC, Berrettini WH, Bahado-Singh RO. Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome. Genomics 2021, 113: 1127-1135. PMID: 33711455, DOI: 10.1016/j.ygeno.2021.03.006.Peer-Reviewed Original ResearchConceptsNeonatal opioid withdrawal syndromeOpioid withdrawal syndromeIngenuity Pathway AnalysisWithdrawal syndromeOpioid-exposed pregnanciesPlacental tissue samplesOpioid abusePlacental tissueTissue samplesPregnancyMethylation dysregulationGenome-wide methylation analysisSyndromeInfantsPathway analysisAUCDNA methylation profilesMethylation profilesGroupPathophysiologyTherapyStrong evidencePredictive Analytics for Glaucoma Using Data From the All of Us Research Program
Baxter S, Saseendrakumar B, Paul P, Kim J, Bonomi L, Kuo T, Loperena R, Ratsimbazafy F, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Mayo K, Mockrin S, Schully S, Ramirez A, Ohno-Machado L, Investigators A. Predictive Analytics for Glaucoma Using Data From the All of Us Research Program. American Journal Of Ophthalmology 2021, 227: 74-86. PMID: 33497675, PMCID: PMC8184631, DOI: 10.1016/j.ajo.2021.01.008.Peer-Reviewed Original ResearchConceptsGlaucoma surgeryPrimary open-angle glaucomaOphthalmic researchSingle-center cohortElectronic health record dataMultivariable logistic regressionSingle-center dataOpen-angle glaucomaHealth record dataMean ageClaims dataUs Research ProgramLogistic regressionSurgeryRecord dataOphthalmic imagingCharacteristic curveExternal validationGlaucomaCohortAUCSingle-center model
2020
Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET
Naganawa M, Gallezot JD, Shah V, Mulnix T, Young C, Dias M, Chen MK, Smith AM, Carson RE. Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET. EJNMMI Physics 2020, 7: 67. PMID: 33226522, PMCID: PMC7683759, DOI: 10.1186/s40658-020-00330-x.Peer-Reviewed Original ResearchPopulation-based input functionImage-derived input functionInitial distribution volumeArterial input functionInjected doseBlood samplingWhole bodyStandard arterial input functionInitial plasma concentrationsArterial blood samplingOncological patientsPlasma concentrationsGold standard methodDistribution volumePET studiesPET imagingSubject heightInput functionAUCAUC valuesTest-retest dataClinical environmentLater time windowKi valuesImaging
2019
Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding
Shung DL, Au B, Taylor RA, Tay JK, Laursen SB, Stanley AJ, Dalton HR, Ngu J, Schultz M, Laine L. Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding. Gastroenterology 2019, 158: 160-167. PMID: 31562847, PMCID: PMC7004228, DOI: 10.1053/j.gastro.2019.09.009.Peer-Reviewed Original ResearchConceptsUpper gastrointestinal bleedingHospital-based interventionsComposite endpointScoring systemRockall scoreGastrointestinal bleedingClinical riskConsecutive unselected patientsLow-risk patientsClinical scoring systemRisk-scoring systemExternal validation cohortCharacteristic curve analysisInternal validation setOutpatient managementUnselected patientsValidation cohortEmergency departmentMedical CenterGreater AUCPatientsAbstractTextCurve analysisEndpointAUCA Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs
Keenan T, Dharssi S, Peng Y, Chen Q, Agrón E, Wong W, Lu Z, Chew E. A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs. Ophthalmology 2019, 126: 1533-1540. PMID: 31358385, PMCID: PMC6810830, DOI: 10.1016/j.ophtha.2019.06.005.Peer-Reviewed Original ResearchPredicting Efavirenz Concentrations in the Brain Tissue of HIV‐Infected Individuals and Exploring their Relationship to Neurocognitive Impairment
Srinivas N, Joseph SB, Robertson K, Kincer LP, Menezes P, Adamson L, Schauer AP, Blake KH, White N, Sykes C, Luciw P, Eron JJ, Forrest A, Price R, Spudich S, Swanstrom R, Kashuba A. Predicting Efavirenz Concentrations in the Brain Tissue of HIV‐Infected Individuals and Exploring their Relationship to Neurocognitive Impairment. Clinical And Translational Science 2019, 12: 302-311. PMID: 30675981, PMCID: PMC6510381, DOI: 10.1111/cts.12620.Peer-Reviewed Original ResearchConceptsBrain tissue concentrationsCerebrospinal fluidBrain tissueRhesus macaquesNeurocognitive scoresPK/pharmacodynamic analysesPenetration of antiretroviralsTissue concentrationsExposure-response analysisConcentration-time curveAdult rhesus macaquesCSF AUCEfavirenz concentrationsPlasma AUCPharmacodynamic analysisClinical studiesTissue AUCNeurocognitive impairmentCSF samplesPharmacokinetic dataPK modelingHIVTime pointsAUCNecropsy
2018
A multi-parameterized artificial neural network for lung cancer risk prediction
Hart GR, Roffman DA, Decker R, Deng J. A multi-parameterized artificial neural network for lung cancer risk prediction. PLOS ONE 2018, 13: e0205264. PMID: 30356283, PMCID: PMC6200229, DOI: 10.1371/journal.pone.0205264.Peer-Reviewed Original ResearchConceptsLung cancer risk predictionHistory of strokeNon-invasive clinical toolLung cancer riskHealth informationPersonal health informationNon-cancer casesCancer risk predictionRisk stratificationSmoking statusHeart diseaseExercise habitsHispanic ethnicityLung cancer detectionCancer riskClinical toolAdult dataRisk predictionModest sensitivityCancer detectionAUCHigh specificitySpecificityHypertensionAsthma
2016
Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci
Clyde MA, Weber R, Iversen ES, Poole EM, Doherty JA, Goodman MT, Ness RB, Risch HA, Rossing MA, Terry KL, Wentzensen N, Whittemore AS, Anton-Culver H, Bandera EV, Berchuck A, Carney ME, Cramer DW, Cunningham JM, Cushing-Haugen KL, Edwards RP, Fridley BL, Goode EL, Lurie G, McGuire V, Modugno F, Moysich KB, Olson SH, Pearce CL, Pike MC, Rothstein JH, Sellers TA, Sieh W, Stram D, Thompson PJ, Vierkant RA, Wicklund KG, Wu AH, Ziogas A, Tworoger SS, Schildkraut JM. Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. American Journal Of Epidemiology 2016, 184: 579-589. PMID: 27698005, PMCID: PMC5065620, DOI: 10.1093/aje/kww091.Peer-Reviewed Original ResearchConceptsEpidemiologic risk factorsEpithelial ovarian cancerYears of ageRisk factorsAbsolute riskOvarian cancerInvasive epithelial ovarian cancerCase-control studyOvarian Cancer Association ConsortiumHierarchical logistic regression modelsRisk prediction modelLogistic regression modelsProspective data setSignificant single nucleotide polymorphismsCase-control statusControl studyRisk predictionSingle nucleotide polymorphismsAgeCancerLow discriminatory powerWomenAUCRegression modelsNucleotide polymorphisms
2011
Tipranavir/Ritonavir Induction of Buprenorphine Glucuronide Metabolism in HIV-Negative Subjects Chronically Receiving Buprenorphine/Naloxone
Bruce RD, Moody DE, Fang WB, Chodkowski D, Andrews L, Friedland GH. Tipranavir/Ritonavir Induction of Buprenorphine Glucuronide Metabolism in HIV-Negative Subjects Chronically Receiving Buprenorphine/Naloxone. The American Journal Of Drug And Alcohol Abuse 2011, 37: 224-228. PMID: 21438849, DOI: 10.3109/00952990.2011.568081.Peer-Reviewed Original ResearchConceptsTPV/rSteady-state pharmacokinetic evaluationGlucuronide metabolitesBUP/naloxoneHIV-negative subjectsHIV-seronegative subjectsBuprenorphine/naloxoneSteady-state pharmacokineticsCytochrome P450 3A4Ritonavir effectsPharmacodynamic consequencesPharmacokinetic evaluationBuprenorphineNorBUPCombined inhibitionNaloxoneCurve AUCP450 3A4AUCSignificant increasePharmacokineticsTipranavirPrevious reportsSubjectsMetabolites
2006
Dynamic and Quantitative Analysis of Choroidal Neovascularization by Fluorescein Angiography
Shah SM, Tatlipinar S, Quinlan E, Sung JU, Tabandeh H, Nguyen QD, Fahmy AS, Zimmer-Galler I, Symons RC, Cedarbaum JM, Campochiaro PA. Dynamic and Quantitative Analysis of Choroidal Neovascularization by Fluorescein Angiography. Investigative Ophthalmology & Visual Science 2006, 47: 5460-5468. PMID: 17122137, DOI: 10.1167/iovs.06-0012.Peer-Reviewed Original ResearchConceptsFluorescein angiographyFluorescein angiogramsClinical trialsVEGF TrapVascular endothelial growth factor trapPlacebo-treated patientsPhotodynamic therapyChoroidal neovascularization lesion sizeAreas of hyperfluorescenceMacular volumeChoroidal neovascularizationRetrospective gradingOutcome measuresGroup 2Group 1Masked observersPatientsFluorescence areaLesion sizeAngiogramsClinical settingDye injectionInitial assessmentAUCTrials
1998
Methadone Effects on Zidovudine Disposition (AIDS Clinical Trials Group 262)
McCanceKatz E, Rainey P, Jatlow P, Friedland G. Methadone Effects on Zidovudine Disposition (AIDS Clinical Trials Group 262). JAIDS Journal Of Acquired Immune Deficiency Syndromes 1998, 18: 435-443. PMID: 9715839, DOI: 10.1097/00042560-199808150-00004.Peer-Reviewed Original ResearchConceptsInjection drug usersMethadone treatmentChronic methadone treatmentMethadone-maintained patientsHeroin-addicted patientsMethadone effectsZDV doseZDV exposureZDV pharmacokineticsZDV treatmentZidovudine dispositionZidovudine therapyHIV diseaseMethadone levelsPharmacokinetic interactionsTherapeutic rangeRenal clearanceToxicity surveillanceSide effectsDrug usersDrug efficacyZDVClearanceAUCPatients
1996
Metabolic phenotypes of retinoic acid and the risk of lung cancer.
Rigas J, Miller V, Zhang Z, Klimstra D, Tong W, Kris M, Warrell R. Metabolic phenotypes of retinoic acid and the risk of lung cancer. Cancer Research 1996, 56: 2692-6. PMID: 8665495.Peer-Reviewed Original ResearchConceptsRisk of lung cancerAll-trans retinoic acidLung cancerCell carcinomaPlasma concentration x time curveConcentration x time curveIncreased riskMetabolic phenotypeRetinoic acidPopulation of lung cancer patientsLung cancer patientsCatabolism of all-trans retinoic acidCell cancerOral doseDoses of all-trans retinoic acidControl subjectsCancer patientsCytochrome P-450 enzymesActivity of cytochrome P-450 enzymesP-450 enzymesCancerPatientsCarcinomaAUCLung
1988
Clinical significance of the relationship between O-methyldopa levels and levodopa intake.
Cedarbaum J, Kutt H, McDowell F. Clinical significance of the relationship between O-methyldopa levels and levodopa intake. Neurology 1988, 38: 533-6. PMID: 3352906, DOI: 10.1212/wnl.38.4.533.Peer-Reviewed Original ResearchConceptsDaily intakeRecent clinical trialsMean daily intakeTotal daily intakeLevodopa intakeStandard SinemetClinical responseLevodopa preparationsBrain uptakeClinical trialsPlasma concentrationsClinical significanceBlood samplingLevodopaTherapeutic efficacyMethyldopa levelsControlled-release formulationPatientsIntakeTime curveAUCSinemetLevelsTrials
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