2024
An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers
Xirou V, Moutafi M, Bai Y, Nwe Aung T, Burela S, Liu M, Kimple R, Shabbir Ahmed F, Schultz B, Flieder D, Connolly D, Psyrri A, Burtness B, Rimm D. An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers. Oral Oncology 2024, 152: 106750. PMID: 38547779, PMCID: PMC11060915, DOI: 10.1016/j.oraloncology.2024.106750.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesHead and neck cancerTILs evaluationHPV-positiveNeck cancerPrognostic valueHead and neck squamous cell cancer casesTIL variablesAssociated with favorable prognosisHPV-negative headHPV-negative populationHematoxylin-eosin-stained sectionsCox regression analysisPotential clinical implicationsInter-observer variabilityInfiltrating lymphocytesClinicopathological factorsFavorable prognosisValidation cohortTumor cellsCancer casesProspective settingQuPath softwareRetrospective collectionPredictive significance
2023
An Algorithm as a Biomarker for Response to Immune Checkpoint Inhibitor Therapy
Rimm D. An Algorithm as a Biomarker for Response to Immune Checkpoint Inhibitor Therapy. JAMA Oncology 2023, 9: 60-61. PMID: 36394848, DOI: 10.1001/jamaoncol.2022.4772.Peer-Reviewed Original Research
2021
Artificial intelligence applied to breast pathology
Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L. Artificial intelligence applied to breast pathology. Virchows Archiv 2021, 480: 191-209. PMID: 34791536, DOI: 10.1007/s00428-021-03213-3.Peer-Reviewed Original ResearchConceptsArtificial intelligenceApplication of AIComplex artificial intelligenceDevelopment of algorithmsComputer visionDeep learningMachine learningMitosis detectionDigital pathologyNeural networkDigital dataHistology imagesTissue segmentationField of pathologyImage analysisIntelligencePromising resultsTaskLearningImagesSegmentationBreast pathologyComputerAlgorithmNetworkAn Open Source, Automated Tumor Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
Bai Y, Cole K, Martinez-Morilla S, Ahmed FS, Zugazagoitia J, Staaf J, Bosch A, Ehinger A, Nimeus E, Hartman J, Acs B, Rimm DL. An Open Source, Automated Tumor Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer. Clinical Cancer Research 2021, 27: clincanres.0325.2021. PMID: 34088723, PMCID: PMC8530841, DOI: 10.1158/1078-0432.ccr-21-0325.Peer-Reviewed Original Research
2019
An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma
Acs B, Ahmed FS, Gupta S, Wong P, Gartrell RD, Sarin Pradhan J, Rizk EM, Gould Rothberg BE, Saenger YM, Rimm DL. An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma. Nature Communications 2019, 10: 5440. PMID: 31784511, PMCID: PMC6884485, DOI: 10.1038/s41467-019-13043-2.Peer-Reviewed Original ResearchConceptsOpen sourceOpen source softwareSource softwareTIL scoreTraining setDisease-specific overall survivalHigh TIL scorePoor prognosis cohortsSubset of patientsAlgorithmIndependent prognostic markerBroad adoptionAssessment of tumorOverall survivalFavorable prognosisMelanoma patientsMultivariable analysisValidation cohortIndependent associationPrognostic markerSeparate patientsPrognostic variablesIndependent cohortRetrospective collectionMelanomaDeep 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/Not 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
2016
Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial
Bartlett JM, Christiansen J, Gustavson M, Rimm DL, Piper T, van de Velde CJ, Hasenburg A, Kieback DG, Putter H, Markopoulos CJ, Dirix LY, Seynaeve C, Rea DW. Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial. Archives Of Pathology & Laboratory Medicine 2016, 140: 66-74. PMID: 26717057, DOI: 10.5858/arpa.2014-0599-oa.Peer-Reviewed Original ResearchConceptsHazard ratioBreast cancerResidual riskMultivariate Cox proportional hazardsDistant recurrence-free survivalClinical prognostic factorsEarly breast cancerRecurrence-free survivalSignificant prognostic valueCox proportional hazardsHER2/neuIHC4 scoreHormone therapyNodal statusTrial cohortPrognostic factorsPrognostic valueClinical trialsKi-67Proportional hazardsMultivariate analysisTEAM trialBiomarker expressionQuantitative immunofluorescenceResidual risk assessment
2014
Automated Objective Determination of Percentage of Malignant Nuclei for Mutation Testing
Viray H, Coulter M, Li K, Lane K, Madan A, Mitchell K, Schalper K, Hoyt C, Rimm DL. Automated Objective Determination of Percentage of Malignant Nuclei for Mutation Testing. Applied Immunohistochemistry & Molecular Morphology 2014, 22: 363-371. PMID: 24162261, PMCID: PMC3999345, DOI: 10.1097/pai.0b013e318299a1f6.Peer-Reviewed Original ResearchConceptsCriterion standardMalignant cellsMalignant nucleiCompanion diagnostic testsTumor cell percentageMutation testingEosin-stained tissuesCell percentageInForm softwareHistologic specimensTumor tissueColon adenocarcinomaTumor cellsDiagnostic testsPotential future toolDNA mutation testingTissue sectionsContinuous variablesFurther validationPathologist estimationAnalytic sensitivityVariant resultsDNA mutationsBenign nucleiTissue
2012
Spatial spectral imaging as an adjunct to the Bethesda classification of thyroid fine‐needle aspiration specimens
Hahn LD, Hoyt C, Rimm DL, Theoharis C. Spatial spectral imaging as an adjunct to the Bethesda classification of thyroid fine‐needle aspiration specimens. Cancer Cytopathology 2012, 121: 162-167. PMID: 22833451, DOI: 10.1002/cncy.21224.Peer-Reviewed Original Research
2010
Quantitative evaluation of protein expression as a function of tissue microarray core diameter: is a large (1.5 mm) core better than a small (0.6 mm) core?
Anagnostou VK, Lowery FJ, Syrigos KN, Cagle PT, Rimm DL. Quantitative evaluation of protein expression as a function of tissue microarray core diameter: is a large (1.5 mm) core better than a small (0.6 mm) core? Archives Of Pathology & Laboratory Medicine 2010, 134: 613-9. PMID: 20367312, DOI: 10.5858/134.4.613.Peer-Reviewed Original ResearchAntibody validation
Bordeaux J, Welsh A, Agarwal S, Killiam E, Baquero M, Hanna J, Anagnostou V, Rimm D. Antibody validation. BioTechniques 2010, 48: 197-209. PMID: 20359301, PMCID: PMC3891910, DOI: 10.2144/000113382.Peer-Reviewed Original Research
2009
Melanoma Prognostic Model Using Tissue Microarrays and Genetic Algorithms
Rothberg BE, Berger AJ, Molinaro AM, Subtil A, Krauthammer MO, Camp RL, Bradley WR, Ariyan S, Kluger HM, Rimm DL. Melanoma Prognostic Model Using Tissue Microarrays and Genetic Algorithms. Journal Of Clinical Oncology 2009, 27: 5772-5780. PMID: 19884546, PMCID: PMC2792999, DOI: 10.1200/jco.2009.22.8239.Peer-Reviewed Original ResearchConceptsHigh-risk group
2008
Estrogen receptor co-activator (AIB1) protein expression by automated quantitative analysis (AQUA) in a breast cancer tissue microarray and association with patient outcome
Harigopal M, Heymann J, Ghosh S, Anagnostou V, Camp RL, Rimm DL. Estrogen receptor co-activator (AIB1) protein expression by automated quantitative analysis (AQUA) in a breast cancer tissue microarray and association with patient outcome. Breast Cancer Research And Treatment 2008, 115: 77-85. PMID: 18521745, DOI: 10.1007/s10549-008-0063-9.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAutomationBiomarkers, TumorBreast NeoplasmsFemaleGene Expression Regulation, NeoplasticHumansMultivariate AnalysisNuclear Receptor Coactivator 3Oligonucleotide Array Sequence AnalysisPrognosisProportional Hazards ModelsReceptors, EstrogenReceptors, ProgesteroneRegression AnalysisTranscription FactorsTreatment OutcomeConceptsHigh AIB1 expressionTranscription intermediary factor 2Poor patient outcomesAIB1 expressionTissue microarrayPatient outcomesHER2/neu statusBreast cancer tissue microarrayFluorescent immunohistochemical stainingWorse overall survivalUnivariate survival analysisBreast cancer specimensCancer tissue microarrayHER2/neuCoregulatory proteinsCox univariate survival analysesBreast tissue microarraysOverall survivalER statusPR statusPrognostic significanceIndependent associationBreast cancerPrognostic biomarkerImmunohistochemical staining
2007
Dr. David Rimm is interviewed by Feras Akbik.
Rimm D. Dr. David Rimm is interviewed by Feras Akbik. The Yale Journal Of Biology And Medicine 2007, 80: 183-5. PMID: 18449386, PMCID: PMC2347361.Peer-Reviewed Original ResearchHSP90 as a marker of progression in melanoma
McCarthy MM, Pick E, Kluger Y, Gould-Rothberg BE, Lazova R, Camp RL, Rimm DL, Kluger HM. HSP90 as a marker of progression in melanoma. Annals Of Oncology 2007, 19: 590-594. PMID: 18037622, DOI: 10.1093/annonc/mdm545.Peer-Reviewed Original ResearchUtility of multispectral imaging for nuclear classification of routine clinical histopathology imagery
Boucheron LE, Bi Z, Harvey NR, Manjunath B, Rimm DL. Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery. BMC Molecular And Cell Biology 2007, 8: s8. PMID: 17634098, PMCID: PMC1924513, DOI: 10.1186/1471-2121-8-s1-s8.Peer-Reviewed Original Research
2006
Classification of Breast Cancer Using Genetic Algorithms and Tissue Microarrays
Dolled-Filhart M, Rydén L, Cregger M, Jirström K, Harigopal M, Camp RL, Rimm DL. Classification of Breast Cancer Using Genetic Algorithms and Tissue Microarrays. Clinical Cancer Research 2006, 12: 6459-6468. PMID: 17085660, DOI: 10.1158/1078-0432.ccr-06-1383.Peer-Reviewed Original ResearchConceptsBreast cancerPatient outcomesTissue microarraySubset of patientsBreast cancer patientsTissue microarray platformInternal validation setRoutine pathology laboratoriesCancer patientsEstrogen receptorTissue biomarkersIndependent cohortTumor subtypesPredictive valueAcid-base analysisPathology laboratoryRNA expression studiesCancerTissue sectionsPatientsCohortOutcomesFurther validationObjective quantitative analysisBiomarker discoveryQuantitative In situ Analysis of β-Catenin Expression in Breast Cancer Shows Decreased Expression Is Associated with Poor Outcome
Dolled-Filhart M, McCabe A, Giltnane J, Cregger M, Camp RL, Rimm DL. Quantitative In situ Analysis of β-Catenin Expression in Breast Cancer Shows Decreased Expression Is Associated with Poor Outcome. Cancer Research 2006, 66: 5487-5494. PMID: 16707478, DOI: 10.1158/0008-5472.can-06-0100.Peer-Reviewed Original ResearchConceptsProgesterone receptorEstrogen receptorPrognostic valueBreast cancerKi-67X-tile softwareProportional hazards modelBreast cancer prognosisBreast cancer showBreast cancer tumorsΒ-catenin expressionYale Pathology archivesHazard ratioNode statusPoor outcomeTumor sizePrognostic markerWorse outcomesImmunohistochemical studyNuclear gradeCase cohortLow-level expressionPathology archivesTissue microarrayBeta-catenin expression