2021
Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma
Moore MR, Friesner ID, Rizk EM, Fullerton BT, Mondal M, Trager MH, Mendelson K, Chikeka I, Kurc T, Gupta R, Rohr BR, Robinson EJ, Acs B, Chang R, Kluger H, Taback B, Geskin LJ, Horst B, Gardner K, Niedt G, Celebi JT, Gartrell-Corrado RD, Messina J, Ferringer T, Rimm DL, Saltz J, Wang J, Vanguri R, Saenger YM. Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma. Scientific Reports 2021, 11: 2809. PMID: 33531581, PMCID: PMC7854647, DOI: 10.1038/s41598-021-82305-1.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBiopsyChemotherapy, AdjuvantClinical Decision-MakingDeep LearningFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedKaplan-Meier EstimateLymphocytes, Tumor-InfiltratingMaleMelanomaMiddle AgedNeoplasm StagingPatient SelectionPrognosisRetrospective StudiesRisk AssessmentROC CurveSkinSkin NeoplasmsYoung AdultConceptsTumor-infiltrating lymphocytesDisease-specific survivalEarly-stage melanomaOpen-source deep learningCutoff valueMultivariable Cox proportional hazards analysisCox proportional hazards analysisDeep learningLow-risk patientsProportional hazards analysisKaplan-Meier analysisAccurate prognostic biomarkersEosin imagesAccuracy of predictionAdjuvant therapyRisk patientsSpecific survivalPrognostic valueValidation cohortReceiver operating curvesTraining cohortTIL analysisClinical trialsPrimary melanomaPrognostic biomarker
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
2003
Subcellular localization of activating transcription factor 2 in melanoma specimens predicts patient survival.
Berger AJ, Kluger HM, Li N, Kielhorn E, Halaban R, Ronai Z, Rimm DL. Subcellular localization of activating transcription factor 2 in melanoma specimens predicts patient survival. Cancer Research 2003, 63: 8103-7. PMID: 14678960.Peer-Reviewed Original ResearchConceptsATF2 expressionTranscription factor 2Melanoma specimensUseful prognostic markerEarly-stage melanomaWeak cytoplasmic stainingStrong nuclear stainingFactor 2Mean followCutaneous specimensLocalized diseaseOverall survivalIndependent predictorsPreclinical findingsClark levelClinicopathological dataPatient survivalPoor outcomePrognostic valueWorse outcomesPrognostic markerPoor survivalPreclinical modelsClinical significanceImmunohistochemical staining