2023
Determinants of overall survival in patients with metastatic uveal melanoma
Demkowicz P, Pointdujour‐Lim R, Miguez S, Lee Y, Jones B, Barker C, Bosenberg M, Abramson D, Shoushtari A, Kluger H, Francis J, Sznol M, Bakhoum M. Determinants of overall survival in patients with metastatic uveal melanoma. Cancer 2023, 129: 3275-3286. PMID: 37382208, PMCID: PMC11149607, DOI: 10.1002/cncr.34927.Peer-Reviewed Original ResearchConceptsAnti-PD-1 therapyMetastatic uveal melanomaDeath hazard ratioImmune checkpoint inhibitorsOverall survivalHazard ratioUveal melanomaSurvival outcomesFemale sexCheckpoint inhibitorsECOG scoreValidation cohortEastern Cooperative Oncology Group performance status scaleGood baseline performance statusMetastatic uveal melanoma patientsMetastatic UM patientsImproved overall survivalMedian overall survivalBaseline performance statusBetter survival outcomesImproved survival outcomesPotential of immunotherapyWorse survival outcomesImmune checkpoint therapyKaplan-Meier analysis
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
2020
Primary Treatment Selection for Clinically Node-Negative Merkel Cell Carcinoma of the Head and Neck
Jacobs D, Olino K, Park HS, Clune J, Cheraghlou S, Girardi M, Burtness B, Kluger H, Judson BL. Primary Treatment Selection for Clinically Node-Negative Merkel Cell Carcinoma of the Head and Neck. Otolaryngology 2020, 164: 1214-1221. PMID: 33079010, DOI: 10.1177/0194599820967001.Peer-Reviewed Original ResearchConceptsNode-negative Merkel cell carcinomaLymph node evaluationImproved overall survivalPrimary tumor excisionMerkel cell carcinomaCase volumeOverall survivalSurgical managementCell carcinomaTumor excisionTreatment selectionNode evaluationCox proportional hazards regressionGuideline-recommended carePrimary treatment selectionNational Cancer DatabaseNode-negative diseasePercentage of patientsRetrospective cohort analysisInitial surgical managementKaplan-Meier analysisWide local excisionProportional hazards regressionRates of receiptInitial management
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