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
Quantitative assessment of miR34a as an independent prognostic marker in breast cancer
Agarwal S, Hanna J, Sherman ME, Figueroa J, Rimm DL. Quantitative assessment of miR34a as an independent prognostic marker in breast cancer. British Journal Of Cancer 2014, 112: 61-68. PMID: 25474246, PMCID: PMC4453614, DOI: 10.1038/bjc.2014.573.Peer-Reviewed Original ResearchConceptsDisease-specific survivalBreast cancer cohortPoor disease-specific survivalDisease-specific deathIndependent breast cancer cohortsBreast cancerCancer cohortPoor outcomeCohort 1Multivariate Cox proportional hazards analysisCox proportional hazards analysisNode-positive populationX-tile softwareNode-negative patientsProportional hazards analysisTumor suppressorBreast cancer patientsIndependent prognostic markerExpression of miR34aReceptor statusNode statusPreclinical observationsTumor sizeCancer patientsCohort 2