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
2012
Validation of IHC4 algorithms for prediction of risk of recurrence in early breast cancer using both conventional and quantitative IHC approaches.
Christiansen J, Bartlett J, Gustavson M, Rimm D, Robson T, Van De Velde C, Hasenburg A, Kieback D, Putter H, Markopoulos C, Dirix L, Seynaeve C, Rea D. Validation of IHC4 algorithms for prediction of risk of recurrence in early breast cancer using both conventional and quantitative IHC approaches. Journal Of Clinical Oncology 2012, 30: 517-517. DOI: 10.1200/jco.2012.30.15_suppl.517.Peer-Reviewed Original ResearchEarly breast cancerBreast cancerDAB IHCHazard ratioDisease recurrenceCox proportional hazard modelingKaplan-Meier survival analysisCox proportional hazards modelC-index calculationClinical prognostic factorsProportional hazard modelingProportional hazards modelResidual riskHormone therapyIndependent predictorsPrognostic factorsPrediction of riskRisk markersClinical utilityHazards modelRecurrence riskRecurrencePathology studiesSurvival analysisMultivariate analysis