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 immunotherapySpatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer
Corredor G, Wang X, Zhou Y, Lu C, Fu P, Syrigos K, Rimm DL, Yang M, Romero E, Schalper KA, Velcheti V, Madabhushi A. Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer. Clinical Cancer Research 2019, 25: 1526-1534. PMID: 30201760, PMCID: PMC6397708, DOI: 10.1158/1078-0432.ccr-18-2013.Peer-Reviewed Original Research
2014
Quantitative assessment of miR34a as an independent prognostic marker in breast cancer
Agarwal S, Hanna J, Sherman ME, Figueroa J, Rimm DL. Quantitative assessment of miR34a as an independent prognostic marker in breast cancer. British Journal Of Cancer 2014, 112: 61-68. PMID: 25474246, PMCID: PMC4453614, DOI: 10.1038/bjc.2014.573.Peer-Reviewed Original ResearchConceptsDisease-specific survivalBreast cancer cohortPoor disease-specific survivalDisease-specific deathIndependent breast cancer cohortsBreast cancerCancer cohortPoor outcomeCohort 1Multivariate Cox proportional hazards analysisCox proportional hazards analysisNode-positive populationX-tile softwareNode-negative patientsProportional hazards analysisTumor suppressorBreast cancer patientsIndependent prognostic markerExpression of miR34aReceptor statusNode statusPreclinical observationsTumor sizeCancer patientsCohort 2
2012
Quantitative assessment of invasive mena isoforms (Menacalc) as an independent prognostic marker in breast cancer
Agarwal S, Gertler FB, Balsamo M, Condeelis JS, Camp RL, Xue X, Lin J, Rohan TE, Rimm DL. Quantitative assessment of invasive mena isoforms (Menacalc) as an independent prognostic marker in breast cancer. Breast Cancer Research 2012, 14: r124. PMID: 22971274, PMCID: PMC3962029, DOI: 10.1186/bcr3318.Peer-Reviewed Original ResearchConceptsBreast cancer cohortBreast cancerPoor outcomeTumor cellsCancer cohortPoor disease-specific survivalDisease-specific deathDisease-specific survivalBreast cancer patientsIndependent prognostic markerIndependent breast cancer cohortsNon-invasive tumor cellsInvasive tumor cellsReceptor statusNode statusTumor sizeCancer patientsPrognostic markerSignificant associationCohortCancerIsoform expressionPatientsMetastasisOutcomes
2008
AQUA analysis of thymidylate synthase reveals localization to be a key prognostic biomarker in 2 large cohorts of colorectal carcinoma.
Gustavson MD, Molinaro AM, Tedeschi G, Camp RL, Rimm DL. AQUA analysis of thymidylate synthase reveals localization to be a key prognostic biomarker in 2 large cohorts of colorectal carcinoma. Archives Of Pathology & Laboratory Medicine 2008, 132: 1746-52. PMID: 18976010, DOI: 10.5858/132.11.1746.Peer-Reviewed Original ResearchConceptsThymidylate synthase expressionSynthase expressionColorectal carcinomaSignificant associationDisease-specific survivalColon cancer outcomesColon cancer survivalKey prognostic biomarkersRetrospective cohortNodal statusPrognostic valueColorectal cancerCancer outcomesCancer survivalPathologic classificationPrognostic biomarkerLarge cohortSecond cohortAQUA scoreCytoplasmic expressionNuclear expressionCohortHigh nuclearCytoplasmic ratioFirst cohort
2007
Antibody validation by quantitative analysis of protein expression using expression of Met in breast cancer as a model
Pozner-Moulis S, Cregger M, Camp RL, Rimm DL. Antibody validation by quantitative analysis of protein expression using expression of Met in breast cancer as a model. Laboratory Investigation 2007, 87: 251-260. PMID: 17260003, DOI: 10.1038/labinvest.3700515.Peer-Reviewed Original ResearchConceptsExpression of METPrognostic valueBreast cancerProtein expressionShorter disease-specific survivalDisease-specific survivalInvasive breast cancerHepatocyte growth factor receptorGrowth factor receptorNeck carcinomaAssessment of reproducibilityIntracellular domainTissue microarrayPotential biomarkersCell line controlAntibody validationNuclear MetCancerFactor receptorAntibodiesMetSMet receptorVariable resultsReceptorsCompartmental analysis
2005
Automated Quantitative Analysis (AQUA) of In Situ Protein Expression, Antibody Concentration, and Prognosis
McCabe A, Dolled-Filhart M, Camp RL, Rimm DL. Automated Quantitative Analysis (AQUA) of In Situ Protein Expression, Antibody Concentration, and Prognosis. Journal Of The National Cancer Institute 2005, 97: 1808-1815. PMID: 16368942, DOI: 10.1093/jnci/dji427.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAntibodies, NeoplasmBiomarkers, TumorCell Line, TumorConfidence IntervalsEnzyme-Linked Immunosorbent AssayFemaleFluorescent Antibody TechniqueGene Expression ProfilingGene Expression Regulation, NeoplasticHumansImmunohistochemistryMaleMiddle AgedNeoplasmsOdds RatioPredictive Value of TestsPrognosisProtein Array AnalysisReceptor, ErbB-2Receptors, EstrogenSurvival AnalysisTreatment OutcomeTumor Suppressor Protein p53ConceptsDisease-specific mortalityHigh HER2 expressionHER2 expressionAntibody concentrationsHigh expressionPoor survivalRelative riskTissue microarrayCumulative disease-specific survivalBiomarker expressionLong-term survival dataLow expressionHER2 antibodyX-tile programDisease-specific survivalLow HER2 expressionKaplan-Meier methodBreast cancer patientsExpression of HER2Higher antibody concentrationsLow antibody concentrationsConcentration of antibodyCancer patientsPatient outcomesSitu protein expression