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 biomarkerUsing Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma
Johannet P, Coudray N, Donnelly DM, Jour G, Illa-Bochaca I, Xia Y, Johnson DB, Wheless L, Patrinely JR, Nomikou S, Rimm DL, Pavlick AC, Weber JS, Zhong J, Tsirigos A, Osman I. Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma. Clinical Cancer Research 2021, 27: 131-140. PMID: 33208341, PMCID: PMC7785656, DOI: 10.1158/1078-0432.ccr-20-2415.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedDisease ProgressionDrug Resistance, NeoplasmFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedImmune Checkpoint InhibitorsMachine LearningMaleMelanomaMiddle AgedNeoplasm StagingPrognosisProgression-Free SurvivalProspective StudiesRisk AssessmentROC CurveSkinSkin NeoplasmsConceptsProgression-free survivalImmune checkpoint inhibitorsLower riskClinicodemographic characteristicsAdvanced melanomaClinical dataWorse progression-free survivalICI treatment outcomesKaplan-Meier curvesBiomarkers of responseStandard of careCheckpoint inhibitorsICI responseImmunotherapy responseValidation cohortTraining cohortDisease progressionProspective validationTreatment outcomesHigh riskClinical practicePatientsROC curveProgressionRisk
2009
High Expression of Mammalian Target of Rapamycin Is Associated with Better Outcome for Patients with Early Stage Lung Adenocarcinoma
Anagnostou VK, Bepler G, Syrigos KN, Tanoue L, Gettinger S, Homer RJ, Boffa D, Detterbeck F, Rimm DL. High Expression of Mammalian Target of Rapamycin Is Associated with Better Outcome for Patients with Early Stage Lung Adenocarcinoma. Clinical Cancer Research 2009, 15: 4157-4164. PMID: 19509151, DOI: 10.1158/1078-0432.ccr-09-0099.Peer-Reviewed Original ResearchConceptsLung cancer patientsMTOR expressionCancer patientsMammalian targetEarly-stage lung adenocarcinomaHigh mTOR expressionIndependent lower riskMedian overall survivalStage IA patientsProtein expressionSubgroup of patientsLung adenocarcinoma patientsStage lung adenocarcinomaMTOR protein expressionRole of mTOROverall survivalPathologic characteristicsPatient survivalValidation cohortAdenocarcinoma groupAdenocarcinoma patientsPrognostic stratificationLung cancerTraining cohortFavorable outcome
2008
Quantitative Assessment of Tissue Biomarkers and Construction of a Model to Predict Outcome in Breast Cancer Using Multiple Imputation
Emerson JW, Dolled-Filhart M, Harris L, Rimm DL, Tuck DP. Quantitative Assessment of Tissue Biomarkers and Construction of a Model to Predict Outcome in Breast Cancer Using Multiple Imputation. Cancer Informatics 2008, 7: cin.s911. PMID: 19352457, PMCID: PMC2664700, DOI: 10.4137/cin.s911.Peer-Reviewed Original ResearchLymph node statusProtein expression levelsNode statusBreast cancerBaseline clinical modelCohort of patientsLack of tumorTissue microarray studyLarge independent cohortsExpression levelsMultiple imputationPatient survivalTraining cohortTissue biomarkersIndependent cohortClinical modelSelect markersCohortSimilar improvementsBiomarker analysisCancerClinical annotationProtein markersBiomarkersFuture studies