2021
Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association
Lara H, Li Z, Abels E, Aeffner F, Bui MM, ElGabry EA, Kozlowski C, Montalto MC, Parwani AV, Zarella MD, Bowman D, Rimm D, Pantanowitz L. Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association. Applied Immunohistochemistry & Molecular Morphology 2021, 29: 479-493. PMID: 33734106, PMCID: PMC8354563, DOI: 10.1097/pai.0000000000000930.Peer-Reviewed Original ResearchAutomated 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
2020
Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images
Noorbakhsh J, Farahmand S, Foroughi pour A, Namburi S, Caruana D, Rimm D, Soltanieh-ha M, Zarringhalam K, Chuang JH. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nature Communications 2020, 11: 6367. PMID: 33311458, PMCID: PMC7733499, DOI: 10.1038/s41467-020-20030-5.Peer-Reviewed Original ResearchMeSH KeywordsArea Under CurveBreast NeoplasmsColonic NeoplasmsComputational BiologyDeep LearningFemaleGenes, p53GenotypeHumansImage Processing, Computer-AssistedMutationNeoplasmsSpatial BehaviorConceptsConvolutional neural networkWhole slide imagesPower of CNNsNormal convolutional neural networkImage data miningColon cancer imagesData miningCNN accuracyCancer imagesNeural networkHistopathological imagesManual inspectionSlide imagesData typesClassifier comparisonSignificant accuracyHistological imagesImage analysisSpatial similarityImagesClassifier pairsClassificationMutation classificationAccuracyMiningAdvances in quantitative immunohistochemistry and their contribution to breast cancer
Yaghoobi V, Martinez-Morilla S, Liu Y, Charette L, Rimm DL, Harigopal M. Advances in quantitative immunohistochemistry and their contribution to breast cancer. Expert Review Of Molecular Diagnostics 2020, 20: 509-522. PMID: 32178550, DOI: 10.1080/14737159.2020.1743178.Peer-Reviewed Original Research
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
2018
Utility of CD8 score by automated quantitative image analysis in head and neck squamous cell carcinoma
Hartman DJ, Ahmad F, Ferris R, Rimm D, Pantanowitz L. Utility of CD8 score by automated quantitative image analysis in head and neck squamous cell carcinoma. Oral Oncology 2018, 86: 278-287. PMID: 30409313, PMCID: PMC6260977, DOI: 10.1016/j.oraloncology.2018.10.005.Peer-Reviewed Original ResearchConceptsCD8 T cellsImmune cell densityOropharyngeal HNSCCT cellsNeck squamous cell carcinomaCD8 cell densityImmune cell infiltratesSquamous cell carcinomaWhole tissue sectionsEntire tumor sectionHPV infectionMedian survivalCell infiltrateHNSCC patientsCell carcinomaHNSCC casesClinicopathologic parametersOnly predictorTumor sectionsBetter outcomesClinical practiceTumor microenvironmentCell densityClinical validationCells/Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study
Acs B, Pelekanou V, Bai Y, Martinez-Morilla S, Toki M, Leung SCY, Nielsen TO, Rimm DL. Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study. Laboratory Investigation 2018, 99: 107-117. PMID: 30181553, DOI: 10.1038/s41374-018-0123-7.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBreast NeoplasmsCohort StudiesHumansImage Processing, Computer-AssistedKi-67 AntigenMiddle AgedObserver VariationReproducibility of ResultsConceptsDIA platformsAn international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer
Rimm DL, Leung SCY, McShane LM, Bai Y, Bane AL, Bartlett JMS, Bayani J, Chang MC, Dean M, Denkert C, Enwere EK, Galderisi C, Gholap A, Hugh JC, Jadhav A, Kornaga EN, Laurinavicius A, Levenson R, Lima J, Miller K, Pantanowitz L, Piper T, Ruan J, Srinivasan M, Virk S, Wu Y, Yang H, Hayes DF, Nielsen TO, Dowsett M. An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer. Modern Pathology 2018, 32: 59-69. PMID: 30143750, DOI: 10.1038/s41379-018-0109-4.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorBreast NeoplasmsFemaleHumansImage Processing, Computer-AssistedImmunohistochemistryKi-67 AntigenReproducibility of ResultsConceptsIntraclass correlation coefficientBreast cancerBreast Cancer Working GroupAssessment of Ki67Pre-specified analysisCancer Working GroupInternational multicenter studyMulticenter studySubsequent clinical validationInternational Ki67Biopsy sectionsClinical valueBiomarker Ki67Breast tumorsKi67 immunohistochemistryEvaluation of reproducibilityKi67Clinical validationTumor cellsObserved intraclass correlation coefficientScoring methodCorrelation coefficientKi67 scoringMaximum scoreCancerNot Just Digital Pathology, Intelligent Digital Pathology
Acs B, Rimm DL. Not Just Digital Pathology, Intelligent Digital Pathology. JAMA Oncology 2018, 4: 403-404. PMID: 29392271, DOI: 10.1001/jamaoncol.2017.5449.Peer-Reviewed Original Research
2015
PLEKHA5 as a Biomarker and Potential Mediator of Melanoma Brain Metastasis
Jilaveanu LB, Parisi F, Barr ML, Zito CR, Cruz-Munoz W, Kerbel RS, Rimm DL, Bosenberg MW, Halaban R, Kluger Y, Kluger HM. PLEKHA5 as a Biomarker and Potential Mediator of Melanoma Brain Metastasis. Clinical Cancer Research 2015, 21: 2138-2147. PMID: 25316811, PMCID: PMC4397107, DOI: 10.1158/1078-0432.ccr-14-0861.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBiomarkers, TumorBrain NeoplasmsCell Line, TumorFemaleFluorescent Antibody TechniqueGene Expression ProfilingHumansImage Processing, Computer-AssistedIntracellular Signaling Peptides and ProteinsMaleMelanomaMiddle AgedNeoplasm InvasivenessTissue Array AnalysisTranscriptomeYoung AdultConceptsCell line modelsBlood-brain barrierBrain metastasesGene expression profilesGene expression profilingExpression profilingExpression profilesPLEKHA5Brain metastasis-free survivalA375P cellsQuantitative immunofluorescenceEarly brain metastasisMelanoma brain metastasesMetastasis-free survivalProfile of patientsPotential mediatorsProtein levelsMetastatic melanoma casesEarly developmentMelanoma cellsKnockdownDecrease proliferationBBB transmigrationExtracerebral sitesMetastatic sites
2014
Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error
Shipitsin M, Small C, Choudhury S, Giladi E, Friedlander S, Nardone J, Hussain S, Hurley AD, Ernst C, Huang YE, Chang H, Nifong TP, Rimm DL, Dunyak J, Loda M, Berman DM, Blume-Jensen P. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error. British Journal Of Cancer 2014, 111: 1201-1212. PMID: 25032733, PMCID: PMC4453845, DOI: 10.1038/bjc.2014.396.Peer-Reviewed Original ResearchMeSH KeywordsActininAgedAlkyl and Aryl TransferasesArea Under CurveBiomarkers, TumorBiopsy, Fine-NeedleCullin ProteinsDNA-Binding ProteinsFollow-Up StudiesHSP70 Heat-Shock ProteinsHumansImage Processing, Computer-AssistedMaleMembrane ProteinsMiddle AgedMitochondrial ProteinsNeoplasm GradingNeoplasm StagingPhosphorylationProstateProstatic NeoplasmsProteomicsRibosomal Protein S6RNA-Binding Protein FUSROC CurveSelection BiasSmad2 ProteinSmad4 ProteinTissue Array AnalysisVoltage-Dependent Anion Channel 1Y-Box-Binding Protein 1ConceptsProstate cancer aggressivenessCancer aggressivenessLarge patient cohortLow Gleason gradePatient cohortTumor microarrayLethal outcomeProstatectomy samplesGleason gradeSignificant overtreatmentBiopsy interpretationProstatectomy tissuePatient samplesBiopsy testsProteomic biomarkersCancer biomarker discoveryExpert pathologistsMarker signaturesTumor heterogeneityBiomarkersAggressivenessProtein biomarkersBiomarker discoveryQuantitative proteomics approachMarkers of Epithelial to Mesenchymal Transition in Association with Survival in Head and Neck Squamous Cell Carcinoma (HNSCC)
Pectasides E, Rampias T, Sasaki C, Perisanidis C, Kouloulias V, Burtness B, Zaramboukas T, Rimm D, Fountzilas G, Psyrri A. Markers of Epithelial to Mesenchymal Transition in Association with Survival in Head and Neck Squamous Cell Carcinoma (HNSCC). PLOS ONE 2014, 9: e94273. PMID: 24722213, PMCID: PMC3983114, DOI: 10.1371/journal.pone.0094273.Peer-Reviewed Original ResearchMeSH KeywordsAutomationBiomarkers, TumorCarcinoma, Squamous CellCohort StudiesEpithelial-Mesenchymal TransitionFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHead and Neck NeoplasmsHumansImage Processing, Computer-AssistedImmunohistochemistryKaplan-Meier EstimateMaleMultivariate AnalysisNeoplasm MetastasisPhenotypePrognosisProportional Hazards ModelsSquamous Cell Carcinoma of Head and NeckTreatment OutcomeConceptsProgression-free survivalSquamous cell carcinomaOverall survivalCell carcinomaE-cadherinPrimary squamous cell carcinomaNeck squamous cell carcinomaHigh-risk HNSCCKaplan-Meier analysisNovel therapeutic approachesMesenchymal transition phenotypeHigh metastatic potentialLow E-cadherinImproved OSInferior OSIndependent predictorsPoor prognosisCarcinoma prognosisClinicopathological parametersInclusion criteriaTherapeutic approachesTransition phenotypeMetastatic potentialMesenchymal transitionProtein expression analysis
2010
Automated Analysis of Tissue Microarrays
Dolled-Filhart M, Gustavson M, Camp RL, Rimm DL, Tonkinson JL, Christiansen J. Automated Analysis of Tissue Microarrays. Methods In Molecular Biology 2010, 664: 151-162. PMID: 20690061, DOI: 10.1007/978-1-60761-806-5_15.Peer-Reviewed Original ResearchAutomationCell LineCell NucleusCytoplasmEpithelial CellsFluorescent Antibody TechniqueImage Processing, Computer-AssistedReceptors, EstrogenTissue Array AnalysisNuclear Localization of Signal Transducer and Activator of Transcription 3 in Head and Neck Squamous Cell Carcinoma Is Associated with a Better Prognosis
Pectasides E, Egloff AM, Sasaki C, Kountourakis P, Burtness B, Fountzilas G, Dafni U, Zaramboukas T, Rampias T, Rimm D, Grandis J, Psyrri A. Nuclear Localization of Signal Transducer and Activator of Transcription 3 in Head and Neck Squamous Cell Carcinoma Is Associated with a Better Prognosis. Clinical Cancer Research 2010, 16: 2427-2434. PMID: 20371693, PMCID: PMC3030188, DOI: 10.1158/1078-0432.ccr-09-2658.Peer-Reviewed Original ResearchConceptsLonger progression-free survivalNeck squamous cell cancerNeck squamous cell carcinomaProgression-free survivalSquamous cell cancerSquamous cell carcinomaPittsburgh Medical CenterTranscription 3Early Detection Research NetworkCurative intentPrognostic roleSurgical resectionBetter prognosisSignal transducerCell cancerCell carcinomaFavorable outcomeSurvival prognosisClinicopathologic parametersMedical CenterIndependent cohortLower riskTest cohortHNSCCSurvival analysisAnalytic Variability in Immunohistochemistry Biomarker Studies
Anagnostou VK, Welsh AW, Giltnane JM, Siddiqui S, Liceaga C, Gustavson M, Syrigos KN, Reiter JL, Rimm DL. Analytic Variability in Immunohistochemistry Biomarker Studies. Cancer Epidemiology Biomarkers & Prevention 2010, 19: 982-991. PMID: 20332259, PMCID: PMC3891912, DOI: 10.1158/1055-9965.epi-10-0097.Peer-Reviewed Original ResearchConceptsHuman epidermal growth factor receptor 3Cancer patientsEstrogen receptorWestern blottingBiomarker studiesEpidermal growth factor receptor 1Breast cancer patientsLung cancer patientsEpidermal growth factor receptor 3Growth factor receptor 1Factor receptor 1Growth factor receptor 3Clone 1D5Worse prognosisHigher eGFRPrognostic classificationER antibodyCancer-related biomarkersCutoff pointBT474 cellsSurvival analysisEGFR antibodyReceptor 1Receptor 3Quantitative immunofluorescence
2009
Quantitative expression of VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 in melanoma tissue microarrays
Mehnert JM, McCarthy MM, Jilaveanu L, Flaherty KT, Aziz S, Camp RL, Rimm DL, Kluger HM. Quantitative expression of VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 in melanoma tissue microarrays. Human Pathology 2009, 41: 375-384. PMID: 20004943, PMCID: PMC2824079, DOI: 10.1016/j.humpath.2009.08.016.Peer-Reviewed Original ResearchBlotting, WesternCell LineDisease ProgressionHumansImage Processing, Computer-AssistedImmunohistochemistryMelanomaNevusProportional Hazards ModelsRegression AnalysisSeverity of Illness IndexSkin NeoplasmsStatistics, NonparametricTissue Array AnalysisVascular Endothelial Growth Factor AVascular Endothelial Growth Factor Receptor-1Vascular Endothelial Growth Factor Receptor-2Vascular Endothelial Growth Factor Receptor-3Growth factor receptor-bound protein-7 (Grb7) as a prognostic marker and therapeutic target in breast cancer
Nadler Y, González AM, Camp RL, Rimm DL, Kluger HM, Kluger Y. Growth factor receptor-bound protein-7 (Grb7) as a prognostic marker and therapeutic target in breast cancer. Annals Of Oncology 2009, 21: 466-473. PMID: 19717535, PMCID: PMC2826097, DOI: 10.1093/annonc/mdp346.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBiomarkers, TumorBlotting, WesternBreast NeoplasmsCarcinoma, Ductal, BreastCarcinoma, LobularFemaleFluorescent Antibody TechniqueFollow-Up StudiesGRB7 Adaptor ProteinHumansImage Processing, Computer-AssistedMiddle AgedPrognosisReceptor, ErbB-2Survival RateTissue Array AnalysisTumor Cells, CulturedYoung AdultConceptsHER2/neuBreast cancerPrognostic markerHER2/neu-positive breast cancerGRB7 expressionHigh HER2/neuNeu-positive breast cancerHER2/neu overexpressionPrimary breast cancerBreast cancer patientsIndependent prognostic markerNode-positive subsetValuable prognostic markerProtein 7Cy5-conjugated antibodiesMultivariable analysisWorse prognosisEntire cohortCancer patientsNeu overexpressionTissue microarrayTherapeutic targetCancerNeuPatients
2008
Microvessel area using automated image analysis is reproducible and is associated with prognosis in breast cancer
Sullivan CA, Ghosh S, Ocal IT, Camp RL, Rimm DL, Chung GG. Microvessel area using automated image analysis is reproducible and is associated with prognosis in breast cancer. Human Pathology 2008, 40: 156-165. PMID: 18799189, DOI: 10.1016/j.humpath.2008.07.005.Peer-Reviewed Original ResearchConceptsVIII-related antigenMicrovessel densityMicrovessel areaBreast cancerFactor VIII-related antigenPrimary breast cancerEstrogen receptor negativityReceptor negativityNode positivityClinical outcomesEvaluable casesPrognostic parametersAngiogenic biomarkersLarge tumorsYear survivalQuantitative image analysis systemTissue microarrayTumorsCD31Multivariate levelVessel compartmentPoor associationCancerAntigenCD34High levels of vascular endothelial growth factor and its receptors (VEGFR-1, VEGFR-2, neuropilin-1) are associated with worse outcome in breast cancer
Ghosh S, Sullivan CA, Zerkowski MP, Molinaro AM, Rimm DL, Camp RL, Chung GG. High levels of vascular endothelial growth factor and its receptors (VEGFR-1, VEGFR-2, neuropilin-1) are associated with worse outcome in breast cancer. Human Pathology 2008, 39: 1835-1843. PMID: 18715621, PMCID: PMC2632946, DOI: 10.1016/j.humpath.2008.06.004.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBiomarkers, TumorBreast NeoplasmsCarcinoma, Ductal, BreastCarcinoma, LobularConnecticutFemaleFluorescent Antibody Technique, IndirectHumansImage Processing, Computer-AssistedImmunoenzyme TechniquesKaplan-Meier EstimateMiddle AgedNeuropilin-1Receptors, Vascular Endothelial Growth FactorSurvival RateTissue Array AnalysisVascular Endothelial Growth Factor AVascular Endothelial Growth Factor Receptor-1Vascular Endothelial Growth Factor Receptor-2Young AdultConceptsVascular endothelial growth factorEndothelial growth factorBreast cancerVEGFR-1Growth factorNeuropilin-1VEGFR-2Kaplan-Meier survival analysisBreast cancer tissue microarrayVascular endothelial growth factor receptorPrimary breast cancerStandard prognostic factorsEndothelial growth factor receptorPrimary breast adenocarcinomaCancer tissue microarrayTumor-specific expressionGrowth factor receptorPrognostic factorsPrognostic significancePrognostic valueWorse outcomesLarge cohortTissue microarraySurvival analysisSignificant association