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
2009
Melanoma Prognostic Model Using Tissue Microarrays and Genetic Algorithms
Rothberg BE, Berger AJ, Molinaro AM, Subtil A, Krauthammer MO, Camp RL, Bradley WR, Ariyan S, Kluger HM, Rimm DL. Melanoma Prognostic Model Using Tissue Microarrays and Genetic Algorithms. Journal Of Clinical Oncology 2009, 27: 5772-5780. PMID: 19884546, PMCID: PMC2792999, DOI: 10.1200/jco.2009.22.8239.Peer-Reviewed Original ResearchConceptsHigh-risk group
2007
HSP90 as a marker of progression in melanoma
McCarthy MM, Pick E, Kluger Y, Gould-Rothberg BE, Lazova R, Camp RL, Rimm DL, Kluger HM. HSP90 as a marker of progression in melanoma. Annals Of Oncology 2007, 19: 590-594. PMID: 18037622, DOI: 10.1093/annonc/mdm545.Peer-Reviewed Original Research
2006
Characterizing disease states from topological properties of transcriptional regulatory networks
Tuck DP, Kluger HM, Kluger Y. Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinformatics 2006, 7: 236. PMID: 16670008, PMCID: PMC1482723, DOI: 10.1186/1471-2105-7-236.Peer-Reviewed Original ResearchConceptsTranscriptional regulatory networksRegulatory networksTranscription factorsTranscriptional networksRegulated genesGene deregulationExpression profilesDiseased statesGene regulatory networksCentrality of genesGene expression experimentsGene expression profilesGene expression studiesGene centralityRegulatory linkExpression experimentsExpression studiesGene linksGenesCell typesExpression datasetsGene subsetsDifferential activityNormal cellsRemarkable degree