2021
Immune adverse events (irAEs) with adjuvant ipilimumab in melanoma, use of immunosuppressants and association with outcome: ECOG-ACRIN E1609 study analysis
Tarhini AA, Kang N, Lee SJ, Hodi FS, Cohen GI, Hamid O, Hutchins LF, Sosman JA, Kluger HM, Eroglu Z, Koon HB, Lawrence DP, Kendra KL, Minor DR, Lee CB, Albertini MR, Flaherty LE, Petrella TM, Streicher H, Sondak VK, Kirkwood JM. Immune adverse events (irAEs) with adjuvant ipilimumab in melanoma, use of immunosuppressants and association with outcome: ECOG-ACRIN E1609 study analysis. Journal For ImmunoTherapy Of Cancer 2021, 9: e002535. PMID: 33963015, PMCID: PMC8108687, DOI: 10.1136/jitc-2021-002535.Peer-Reviewed Original ResearchConceptsImmune-related adverse eventsRelapse-free survivalUse of immunosuppressantsAdjuvant ipilimumabGrade 3Grade 1Significant associationAdverse eventsPrognostic factorsSpecific immune-related adverse eventsTerms of RFSEndocrine immune-related adverse eventsBetter relapse-free survivalHigh-dose corticosteroidsImmune adverse eventsHigh-risk melanomaIndependent prognostic factorOverall survival outcomesDose corticosteroidsImmunosuppressant useRFS benefitsImproved OSBetter prognosisAdjuvant useSurvival outcomesAssessment of Age, Period, and Birth Cohort Effects and Trends in Merkel Cell Carcinoma Incidence in the United States
Jacobs D, Huang H, Olino K, Weiss S, Kluger H, Judson BL, Zhang Y. Assessment of Age, Period, and Birth Cohort Effects and Trends in Merkel Cell Carcinoma Incidence in the United States. JAMA Dermatology 2021, 157: 59-65. PMID: 33146688, PMCID: PMC7643047, DOI: 10.1001/jamadermatol.2020.4102.Peer-Reviewed Original ResearchConceptsMerkel cell carcinomaBirth cohort effectsCell carcinomaIncidence rateCalendar periodBirth cohortPatient ageNew casesCohort effectsEnd Results Program databaseCross-sectional retrospective studyLongitudinal cohort studyHigh incidence rateAge-adjusted ratesRisk factor exposureRecent birth cohortsCohort studyCarcinoma incidenceRetrospective studyNeuroendocrine cancerAge effectsProgram databaseCohort analysisMAIN OUTCOMECarcinoma
2020
High WHO/ISUP Grade and Unfavorable Architecture, Rather Than Typing of Papillary Renal Cell Carcinoma, May Be Associated With Worse Prognosis
Yang C, Shuch B, Kluger H, Humphrey PA, Adeniran AJ. High WHO/ISUP Grade and Unfavorable Architecture, Rather Than Typing of Papillary Renal Cell Carcinoma, May Be Associated With Worse Prognosis. The American Journal Of Surgical Pathology 2020, 44: 582-593. PMID: 32101890, DOI: 10.1097/pas.0000000000001455.Peer-Reviewed Original ResearchConceptsPapillary renal cell carcinomaType 2 papillary renal cell carcinomaDisease-free survivalWHO/ISUP gradeHigh WHO/ISUP gradeOverall survivalRenal cell carcinomaISUP gradeWorld Health OrganizationPRCC typeType 1Hazard ratioPathologic stageCell carcinomaMicropapillary architectureHistologic parametersType 2Stepwise multivariate Cox regression analysisWorse disease-free survivalMultivariate Cox regression analysisTumor areaType 1 histologyUrological Pathology (ISUP) gradeCox regression analysisMixed type 1
2019
Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death
Kulkarni PM, Robinson EJ, Pradhan J, Gartrell-Corrado RD, Rohr BR, Trager MH, Geskin LJ, Kluger HM, Wong PF, Acs B, Rizk EM, Yang C, Mondal M, Moore MR, Osman I, Phelps R, Horst BA, Chen ZS, Ferringer T, Rimm DL, Wang J, Saenger YM. Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death. Clinical Cancer Research 2019, 26: 1126-1134. PMID: 31636101, PMCID: PMC8142811, DOI: 10.1158/1078-0432.ccr-19-1495.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAlgorithmsArea Under CurveBiopsyDeep LearningDisease ProgressionFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedMaleMelanomaMiddle AgedNeoplasm Recurrence, LocalNeural Networks, ComputerRetrospective StudiesRisk FactorsStaining and LabelingSurvival RateYoung AdultConceptsDeep neural network architectureNeural network architectureDeep learningNetwork architectureComputational modelImage sequencesDigital imagesVote aggregationDisease-specific survivalDSS predictionPractical advancesComputational methodsIHC-based methodsImagesGeisinger Health SystemNovel methodGHS patientsArchitectureLearningKaplan-Meier analysisPrimary melanoma tumorsEarly-stage melanomaClinical trial designModelAdjuvant immunotherapy
2015
Does immunotherapy increase the rate of radiation necrosis after radiosurgical treatment of brain metastases?
Colaco RJ, Martin P, Kluger HM, Yu JB, Chiang VL. Does immunotherapy increase the rate of radiation necrosis after radiosurgical treatment of brain metastases? Journal Of Neurosurgery 2015, 125: 17-23. PMID: 26544782, DOI: 10.3171/2015.6.jns142763.Peer-Reviewed Original ResearchConceptsTreatment-related imaging changesCytotoxic chemotherapyRadiation necrosisBrain metastasesSystemic therapyStereotactic Gamma Knife radiosurgeryMedian overall survivalGamma knife radiosurgeryHigh-dose radiationChemotherapy eraGK surgeryImmunotherapy increasesMedian followConclusions PatientsOverall survivalImaging changesGK treatmentKnife radiosurgeryInflammatory reactionStereotactic radiosurgeryLower riskRadiosurgical treatmentPatientsImmunotherapyTherapy
2013
Advances in therapy for melanoma brain metastases
Flanigan JC, Jilaveanu LB, Chiang VL, Kluger HM. Advances in therapy for melanoma brain metastases. Clinics In Dermatology 2013, 31: 264-281. PMID: 23608446, DOI: 10.1016/j.clindermatol.2012.08.008.Peer-Reviewed Original Research