2020
The Society for Immunotherapy of Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation
Taube JM, Akturk G, Angelo M, Engle EL, Gnjatic S, Greenbaum S, Greenwald NF, Hedvat CV, Hollmann TJ, Juco J, Parra ER, Rebelatto MC, Rimm DL, Rodriguez-Canales J, Schalper KA, Stack EC, Ferreira CS, Korski K, Lako A, Rodig SJ, Schenck E, Steele KE, Surace MJ, Tetzlaff MT, von Loga K, Wistuba II, Bifulco CB, . The Society for Immunotherapy of Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation. Journal For ImmunoTherapy Of Cancer 2020, 8: e000155. PMID: 32414858, PMCID: PMC7239569, DOI: 10.1136/jitc-2019-000155.Peer-Reviewed Original ResearchMeSH KeywordsFluorescent Antibody TechniqueHumansImmunohistochemistryImmunotherapyStaining and LabelingTumor MicroenvironmentConceptsMultiplex immunohistochemistryImmune cell subsetsImmunotherapy of cancerRoutine clinical practiceMultiplex immunofluorescence analysisDigital spatial profilingMIF assayTask ForceTreatment of cancerCell subsetsPractice guidelinesBest practice guidelinesAcademic centersClinical practiceImmune systemTumor microenvironmentImmunohistochemistryTumor cellsBiomarker studiesCancer statementsChromogenic immunohistochemistryImmunotherapyImmunofluorescence analysisConsecutive stainingCancer
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
2011
Optimal tumor sampling for immunostaining of biomarkers in breast carcinoma
Tolles J, Bai Y, Baquero M, Harris LN, Rimm DL, Molinaro AM. Optimal tumor sampling for immunostaining of biomarkers in breast carcinoma. Breast Cancer Research 2011, 13: r51. PMID: 21592345, PMCID: PMC3218938, DOI: 10.1186/bcr2882.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorBreast NeoplasmsData Interpretation, StatisticalFemaleHumansSample SizeStaining and LabelingConceptsWhole tissue sectionsBreast carcinomaEstrogen receptorBiomarker expressionTumor biomarker expressionAmount of tumorTissue sectionsEvidence-based standardsHeterogeneous markersTherapeutic responseHER-2Optimal tumorBreast biopsyBreast tumorsClinical implicationsMAP-tauQuantitative immunofluorescenceClinical useLevel of expressionCarcinomaImmunostaining assaysBiomarkersTumorsTissue samplesBiomarker heterogeneity
2006
What brown cannot do for you
Rimm DL. What brown cannot do for you. Nature Biotechnology 2006, 24: 914-916. PMID: 16900128, DOI: 10.1038/nbt0806-914.Peer-Reviewed Original Research