2017
Identifying Metastases-related Information from Pathology Reports of Lung Cancer Patients.
Soysal E, Warner J, Denny J, Xu H. Identifying Metastases-related Information from Pathology Reports of Lung Cancer Patients. AMIA Joint Summits On Translational Science Proceedings 2017, 2017: 268-277. PMID: 28815141, PMCID: PMC5543353.Peer-Reviewed Original ResearchPathology reportsSpecimen siteImportant prognostic factorLung cancer patientsMetastatic patternPrognostic factorsClinical courseHistological typeCancer patientsMetastasis sitesMetastatic statusCancer recurrenceCancer metastasisMetastasisTumor metastasisPatientsStatus indicatorsReportStatusRecurrence
2015
Effects of Health Insurance on Tumor Stage, Treatment, and Survival in Large Cohorts of Patients with Breast and Colorectal Cancer
Zhang Y, Franzini L, Chan W, Xu H, Du X. Effects of Health Insurance on Tumor Stage, Treatment, and Survival in Large Cohorts of Patients with Breast and Colorectal Cancer. Journal Of Health Care For The Poor And Underserved 2015, 26: 1336-1358. PMID: 26548682, DOI: 10.1353/hpu.2015.0119.Peer-Reviewed Original ResearchConceptsRisk of mortalityColorectal cancerTumor stagePrivate health insuranceCancer patientsHealth insuranceCancer-directed surgeryColorectal cancer patientsTexas Cancer RegistryInsurance coverageAdditional private health insuranceBreast cancer patientsHealth insurance statusHealth insurance coverageOverall survivalCancer RegistryInsurance statusBreast cancerLarge cohortHigh riskMedicare beneficiariesPatientsCancerChemotherapySurgeryTrends and variations in breast and colorectal cancer incidence from 1995 to 2011: A comparative study between Texas Cancer Registry and National Cancer Institute’s Surveillance, Epidemiology and End Results data
LIU Z, ZHANG Y, FRANZIN L, CORMIER J, CHAN W, XU H, DU X. Trends and variations in breast and colorectal cancer incidence from 1995 to 2011: A comparative study between Texas Cancer Registry and National Cancer Institute’s Surveillance, Epidemiology and End Results data. International Journal Of Oncology 2015, 46: 1819-1826. PMID: 25672365, PMCID: PMC4356494, DOI: 10.3892/ijo.2015.2881.Peer-Reviewed Original ResearchConceptsColorectal cancer incidenceNational Cancer Institute's SurveillanceTexas Cancer RegistryBreast cancer incidenceCancer incidenceCancer RegistryAge-adjusted breast cancer incidenceColorectal cancer patientsEnd Results (SEER) dataSEER areasColorectal cancerCancer patientsIncidence rateRelative riskIncidenceBreastRegistrySurveillanceEpidemiologySEERResult dataTemporal trendsEnd resultPatientsParallel comparison
2014
Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality
Xu H, Aldrich M, Chen Q, Liu H, Peterson N, Dai Q, Levy M, Shah A, Han X, Ruan X, Jiang M, Li Y, St Julien J, Warner J, Friedman C, Roden D, Denny J. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. Journal Of The American Medical Informatics Association 2014, 22: 179-191. PMID: 25053577, PMCID: PMC4433365, DOI: 10.1136/amiajnl-2014-002649.Peer-Reviewed Original ResearchConceptsType 2 diabetes patientsElectronic health recordsCancer patientsCancer mortalityDiabetes patientsEHR dataNon-diabetic cancer patientsCox proportional hazards modelDrug exposure informationOral hypoglycemic medicationsCharlson Comorbidity IndexNon-diabetic patientsUse of metforminCancer diagnosisHealth recordsSite-specific cancersBody mass indexProportional hazards modelVanderbilt University Medical CenterUniversity Medical CenterLarge electronic health recordHypoglycemic medicationsCause mortalityComorbidity indexInsulin use
2013
Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy
Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy A, Abramson V, Bhave S, Levy M, Xu H, Yankeelov T. Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. Journal Of The American Medical Informatics Association 2013, 20: 688-695. PMID: 23616206, PMCID: PMC3721158, DOI: 10.1136/amiajnl-2012-001332.Peer-Reviewed Original ResearchConceptsNeoadjuvant chemotherapyFeature selectionCycles of NACPredictive model buildingTime most patientsBreast cancer patientsImportant clinical problemCourse of therapyMachine learningDynamic contrast-enhanced MRIContrast-enhanced MRIQuantitative dynamic contrast-enhanced MRIMost patientsTreatment regimenCancer patientsClinical variablesTherapeutic responseBreast cancerPredictive modeling approachClinical problemData show promiseLogistic regressionPatientsMachineDiffusion-weighted MRI data