2021
An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy
Perincheri S, Levi AW, Celli R, Gershkovich P, Rimm D, Morrow JS, Rothrock B, Raciti P, Klimstra D, Sinard J. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Modern Pathology 2021, 34: 1588-1595. PMID: 33782551, PMCID: PMC8295034, DOI: 10.1038/s41379-021-00794-x.Peer-Reviewed Original ResearchMeSH KeywordsAdenocarcinomaAgedAged, 80 and overArtificial IntelligenceBiopsy, Large-Core NeedleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedPathology, SurgicalProstatic NeoplasmsSensitivity and SpecificityConceptsMemorial Sloan-Kettering Cancer CenterCore biopsyPredictive valueDiagnostic accuracyProstate core needle biopsiesCore needle biopsySurgical pathology practiceNegative predictive valueProstate core biopsiesPositive predictive valueProstate cancer detectionStrong diagnostic accuracyPoor quality scansCancer detectionCancer CenterProstate biopsyLeading causeNeedle biopsyTransrectal approachProstate cancerProstatic adenocarcinomaProstate carcinomaBiopsyPathology practiceProstate
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 ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overAlgorithmsFemaleHumansImage Interpretation, Computer-AssistedLymphocytes, Tumor-InfiltratingMaleMelanomaMiddle AgedNeoplasm StagingPrognosisRetrospective StudiesSkin NeoplasmsSurvival RateYoung AdultConceptsOpen 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 collectionMelanoma
2016
Standardized evaluation of tumor-infiltrating lymphocytes in breast cancer: results of the ring studies of the international immuno-oncology biomarker working group
Denkert C, Wienert S, Poterie A, Loibl S, Budczies J, Badve S, Bago-Horvath Z, Bane A, Bedri S, Brock J, Chmielik E, Christgen M, Colpaert C, Demaria S, Van den Eynden G, Floris G, Fox SB, Gao D, Ingold Heppner B, Kim SR, Kos Z, Kreipe HH, Lakhani SR, Penault-Llorca F, Pruneri G, Radosevic-Robin N, Rimm DL, Schnitt SJ, Sinn BV, Sinn P, Sirtaine N, O'Toole SA, Viale G, Van de Vijver K, de Wind R, von Minckwitz G, Klauschen F, Untch M, Fasching PA, Reimer T, Willard-Gallo K, Michiels S, Loi S, Salgado R. Standardized evaluation of tumor-infiltrating lymphocytes in breast cancer: results of the ring studies of the international immuno-oncology biomarker working group. Modern Pathology 2016, 29: 1155-1164. PMID: 27363491, DOI: 10.1038/modpathol.2016.109.Peer-Reviewed Original ResearchMeSH KeywordsBreast NeoplasmsFemaleHumansImage Interpretation, Computer-AssistedLymphocytes, Tumor-InfiltratingPathology, ClinicalConceptsTumor-infiltrating lymphocytesEvaluation of TILsIntraclass correlation coefficientBreast cancerInternational Immuno-Oncology Biomarker Working GroupImmune checkpoint inhibitor therapyBiomarker Working GroupImmuno-oncology biomarkersDiagnostic practiceKappa valuesInhibitor therapyInternational Working GroupImmunological parametersClinical trialsClinical studiesFleiss kappa valuePrespecified endpointsStudy aimWorking GroupMean concordanceStandardized reportingCancerRing studiesStandardized evaluationDifferent pathologistsValidation 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
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 ResearchMeSH KeywordsAlgorithmsBiopsy, Fine-NeedleCarcinoma, PapillaryFeasibility StudiesGoiterHumansImage Interpretation, Computer-AssistedPilot ProjectsROC CurveThyroid NeoplasmsThyroid NoduleValidation Studies as TopicConceptsImage classification tests
2007
Utility 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