2020
A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study
Lu C, Bera K, Wang X, Prasanna P, Xu J, Janowczyk A, Beig N, Yang M, Fu P, Lewis J, Choi H, Schmid RA, Berezowska S, Schalper K, Rimm D, Velcheti V, Madabhushi A. A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study. The Lancet Digital Health 2020, 2: e594-e606. PMID: 33163952, PMCID: PMC7646741, DOI: 10.1016/s2589-7500(20)30225-9.Peer-Reviewed Original ResearchConceptsNon-small cell lung carcinomaEarly-stage non-small cell lung carcinomaOverall survivalRetrospective studyEarly-stage non-small cell lung cancerNon-small cell lung cancerMultivariable Cox regression analysisCell differentiation pathwayCox proportional hazards modelLung squamous cell carcinomaEarly-stage LUADOverall survival informationCox regression analysisPrognosis of patientsCell lung cancerRisk stratification modelSquamous cell carcinomaLung cancer pathogenesisIndependent validation cohortCell lung carcinomaProportional hazards modelComputer-extracted featuresAdjuvant therapyDifferentiation pathwayValidation cohort
2019
Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning.
Robinson E, Kulkarni P, Pradhan J, Gartrell R, Yang C, Rizk E, Acs B, Rohr B, Phelps R, Ferringer T, Horst B, Rimm D, Wang J, Saenger Y. Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning. Journal Of Clinical Oncology 2019, 37: 9577-9577. DOI: 10.1200/jco.2019.37.15_suppl.9577.Peer-Reviewed Original ResearchDeep neural net architectureOpen source softwareRecurrent neural networkNeural net architectureDigital pathology toolsDeep learningSource softwareNet architectureFeature informationNeural networkNetwork parametersTIFF filesAdjuvant immunotherapyMelanoma recurrenceCohort 2Cohort 1Cell classificationStage IMultivariable Cox proportional hazards modelsDNNCox proportional hazards modelColumbia University Medical CenterNuclear segmentationEvidence of diseaseIndependent prognostic factor
2014
Correlation of Somatic Mutations and Clinical Outcome in Melanoma Patients Treated with Carboplatin, Paclitaxel, and Sorafenib
Wilson MA, Zhao F, Letrero R, D'Andrea K, Rimm DL, Kirkwood JM, Kluger HM, Lee SJ, Schuchter LM, Flaherty KT, Nathanson KL. Correlation of Somatic Mutations and Clinical Outcome in Melanoma Patients Treated with Carboplatin, Paclitaxel, and Sorafenib. Clinical Cancer Research 2014, 20: 3328-3337. PMID: 24714776, PMCID: PMC4058354, DOI: 10.1158/1078-0432.ccr-14-0093.Peer-Reviewed Original ResearchMeSH KeywordsAdultAntineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorCarboplatinDouble-Blind MethodFemaleFollow-Up StudiesGenotypeGTP PhosphohydrolasesHumansMaleMelanomaMembrane ProteinsMiddle AgedMutationNeoplasm StagingNiacinamidePaclitaxelPhenylurea CompoundsPrognosisProto-Oncogene Proteins B-rafSkin NeoplasmsSorafenibSurvival RateConceptsProgression-free survivalNRAS-mutant melanomaPlatelet-derived growth factor receptorPerformance statusClinical outcomesNRAS mutationsCox proportional hazards modelVEGF receptorsSomatic mutationsWorse performance statusGood performance statusImproved clinical responseKaplan-Meier methodClinical trial populationsPretreatment tumor samplesSite of diseaseProportional hazards modelEffect of sorafenibBRAF-mutant melanomaFisher's exact testGrowth factor receptorClinical responseOverall survivalClinicopathologic featuresMelanoma patientsPrognostic Biomarkers in Phase II Trial of Cetuximab-Containing Induction and Chemoradiation in Resectable HNSCC: Eastern Cooperative Oncology Group E2303
Psyrri A, Lee JW, Pectasides E, Vassilakopoulou M, Kosmidis EK, Burtness BA, Rimm DL, Wanebo HJ, Forastiere AA. Prognostic Biomarkers in Phase II Trial of Cetuximab-Containing Induction and Chemoradiation in Resectable HNSCC: Eastern Cooperative Oncology Group E2303. Clinical Cancer Research 2014, 20: 3023-3032. PMID: 24700741, PMCID: PMC4049169, DOI: 10.1158/1078-0432.ccr-14-0113.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntibodies, Monoclonal, HumanizedAntineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorCarboplatinCarcinoma, Squamous CellCetuximabChemoradiotherapyDisease-Free SurvivalDrug Resistance, NeoplasmFemaleFluorescent Antibody TechniqueHead and Neck NeoplasmsHumansInduction ChemotherapyKaplan-Meier EstimateMaleMiddle AgedMitogen-Activated Protein Kinase KinasesPaclitaxelPhosphatidylinositol 3-KinasesPrognosisProportional Hazards ModelsProto-Oncogene Proteins c-aktRas ProteinsSignal TransductionSquamous Cell Carcinoma of Head and NeckTissue Array AnalysisConceptsProgression-free survivalEvent-free survivalPhase II trialOverall survivalII trialTissue microarrayStage III/IV headMultivariable Cox proportional hazards modelsMultivariable Cox regression analysisNeck squamous cell cancerRAS/MAPK/ERKCox proportional hazards modelInsulin-like growth factor 1 receptorLarge prospective studiesCox regression analysisInferior overall survivalKaplan-Meier methodSquamous cell cancerLog-rank testGrowth factor 1 receptorProportional hazards modelPI3K/Akt pathwayFactor 1 receptorPI3K/AktEGF receptor
2013
Effect of PDL-1 expression on prognosis in head and neck squamous cell carcinoma.
Vasilakopoulou M, Velcheti V, Rampias T, Sasaki C, Rimm D, Fountzilas G, Psyrri A. Effect of PDL-1 expression on prognosis in head and neck squamous cell carcinoma. Journal Of Clinical Oncology 2013, 31: 6012-6012. DOI: 10.1200/jco.2013.31.15_suppl.6012.Peer-Reviewed Original ResearchPDL-1 expressionPDL-1Prognostic significanceT cellsMultivariate Cox proportional hazards modelCox proportional hazards modelNeck squamous cell carcinomaDeath-1 receptorFavorable clinical outcomeKaplan-Meier methodInduces tumor regressionSquamous cell carcinomaHNSCC tissue microarrayLog-rank testProportional hazards modelProtein expression levelsImproved DFSEfficacy outcomesImmunotherapeutic approachesClinical outcomesMultivariable analysisEntire cohortCell carcinomaCancer patientsFavorable outcome
2012
In situ quantitative measurement of mRNA to predict response to trastuzumab in a cohort of metastatic breast cancer patients.
Vassilakopoulou M, Bordeaux J, Neumeister V, Cheng H, Schalper K, Skarlos D, Pectasides D, Pavlidis N, Koutras A, Linardou H, Razis E, Bobos M, Kotoula V, Rimm D, Fountzilas G, Psyrri A. In situ quantitative measurement of mRNA to predict response to trastuzumab in a cohort of metastatic breast cancer patients. Journal Of Clinical Oncology 2012, 30: 573-573. DOI: 10.1200/jco.2012.30.15_suppl.573.Peer-Reviewed Original ResearchBreast cancer patientsMetastatic breast cancer patientsCancer patientsHER2 mRNA levelsHER2 mRNAHistological gradeTrastuzumab-treated metastatic breast cancer patientsMultivariate analysisCox proportional hazards modelHormone receptor statusMetastatic breast cancerKaplan-Meier analysisOverall survival timeMRNA levelsProportional hazards modelHER2 extracellular domainAssessment of HER2Extracellular domainTrastuzumab initiationChemotherapy regimensPrimary endpointReceptor statusTrastuzumab therapyMetastatic cohortTrastuzumab treatmentPredictors for response to cetuximab in a prospective clinical trial (E2303) of patients with head and neck squamous cell carcinoma (HNSCC).
Psyrri A, Lee J, Pectasides E, Vassilakopoulou M, Burtness B, Rimm D, Wanebo H, Forastiere A. Predictors for response to cetuximab in a prospective clinical trial (E2303) of patients with head and neck squamous cell carcinoma (HNSCC). Journal Of Clinical Oncology 2012, 30: 5576-5576. DOI: 10.1200/jco.2012.30.15_suppl.5576.Peer-Reviewed Original ResearchClinical trialsEastern Cooperative Oncology Group Phase II TrialExact testStage III/IV HNSCCMultivariate Cox proportional hazards modelMultivariate Cox regression analysisERK1/2 levelsNeck squamous cell carcinomaCox proportional hazards modelEpidermal growth factor receptor inhibitorsPhase II clinical trialGrowth factor receptor inhibitorsManagement of HNSCCPhase II trialCox regression analysisKaplan-Meier methodProspective clinical trialsSquamous cell carcinomaLog-rank testAssociation of biomarkersProportional hazards modelFisher's exact testLogistic regression modelsProtein expression levelsWeekly cetuximabValidation 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 analysisStathmin expression and its relationship to microtubule‐associated protein tau and outcome in breast cancer
Baquero MT, Hanna JA, Neumeister V, Cheng H, Molinaro AM, Harris LN, Rimm DL. Stathmin expression and its relationship to microtubule‐associated protein tau and outcome in breast cancer. Cancer 2012, 118: 4660-4669. PMID: 22359235, PMCID: PMC3391341, DOI: 10.1002/cncr.27453.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAnalysis of VarianceBiomarkers, TumorBlotting, WesternBreastBreast NeoplasmsCell Line, TumorCohort StudiesFemaleFluorescent Antibody TechniqueGene Expression Regulation, NeoplasticHumansImmunohistochemistryKaplan-Meier EstimateLymphatic MetastasisMiddle AgedNeoplasm GradingNeoplasm StagingOdds RatioPredictive Value of TestsPrognosisProportional Hazards ModelsRisk AssessmentRisk FactorsRNA, Small InterferingStathminTau ProteinsTissue Array AnalysisTreatment OutcomeConceptsHigh stathmin expressionDisease-free survivalMAP-tauOverall survivalStathmin expressionBreast cancerHuman epidermal growth factor receptor 2 (HER2) expressionEpidermal growth factor receptor 2 expressionMultivariate analysisCox proportional hazards modelWorse overall survivalReceptor 2 expressionTissue microarray formatMicrotubule-associated protein tauProportional hazards modelBreast cancer cohortIndependent predictorsMenopausal statusNodal statusBetter prognosisPrognostic valueTumor sizePathological characteristicsProgesterone receptorNuclear grade
1998
Expression of c‐met is a strong independent prognostic factor in breast carcinoma
Ghoussoub R, Dillon D, D'Aquila T, Rimm E, Fearon E, Rimm D. Expression of c‐met is a strong independent prognostic factor in breast carcinoma. Cancer 1998, 82: 1513-1520. PMID: 9554529, DOI: 10.1002/(sici)1097-0142(19980415)82:8<1513::aid-cncr13>3.0.co;2-7.Peer-Reviewed Original ResearchConceptsBreast carcinomaIndependent predictorsStrong independent prognostic factorCox proportional hazards modelGrowth factorIndependent prognostic factorLymph node statusSubset of patientsInvasive ductal carcinomaUseful prognostic markerProportional hazards modelBreast tumor specimensHepatocyte growth factorNegative patientsPrognostic factorsAggressive diseaseDuctal carcinomaNode statusPrognostic valuePrognostic markerTumor specimensHazards modelPatientsPredictive valueSurvival rate