2023
Risk prediction models for endometrial cancer: development and validation in an international consortium
Shi J, Kraft P, Rosner B, Benavente Y, Black A, Brinton L, Chen C, Clarke M, Cook L, Costas L, Dal Maso L, Freudenheim J, Frias-Gomez J, Friedenreich C, Garcia-Closas M, Goodman M, Johnson L, La Vecchia C, Levi F, Lissowska J, Lu L, McCann S, Moysich K, Negri E, O'Connell K, Parazzini F, Petruzella S, Polesel J, Ponte J, Rebbeck T, Reynolds P, Ricceri F, Risch H, Sacerdote C, Setiawan V, Shu X, Spurdle A, Trabert B, Webb P, Wentzensen N, Wilkens L, Xu W, Yang H, Yu H, Du M, De Vivo I. Risk prediction models for endometrial cancer: development and validation in an international consortium. Journal Of The National Cancer Institute 2023, 115: 552-559. PMID: 36688725, PMCID: PMC10165481, DOI: 10.1093/jnci/djad014.Peer-Reviewed Original ResearchConceptsNurses' Health StudyRisk prediction modelNHS IIEndometrial cancerHealth StudyGenetic factorsEndometrial cancer incidence ratesOvarian Cancer Screening TrialCancer risk prediction modelsEndometrial Cancer ConsortiumPostmenopausal white womenCancer Screening TrialCancer risk stratificationCase-control studyRisk factor distributionCancer incidence ratesRelative risk estimatesEpidemiologic modelHeterogeneous study populationsPublic health practiceProphylactic hysterectomyRisk stratificationEpidemiologic factorsIncidence rateSelect cohort
2019
Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data
Baecker A, Kim S, Risch HA, Nuckols TK, Wu BU, Hendifar AE, Pandol SJ, Pisegna JR, Jeon CY. Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data. PLOS ONE 2019, 14: e0218580. PMID: 31237889, PMCID: PMC6592596, DOI: 10.1371/journal.pone.0218580.Peer-Reviewed Original ResearchConceptsPancreatic ductal adenocarcinomaPancreatic cancerRisk factorsPDAC diagnosisNew-onset diabetes patientsEnd Results (SEER) tumorMonths of claimsUndiagnosed pancreatic cancerClinical risk factorsMultivariable logistic regressionDiagnosis of PDACSex-matched controlsCase-control studyRisk prediction modelHealthcare claims dataSurveillance EpidemiologyResults TumorsDiabetes patientsDuctal adenocarcinomaClaims dataHealthcare claimsEarly detection methodsLogistic regressionCancerDiagnosis
2017
Determining Risk of Barrett’s Esophagus and Esophageal Adenocarcinoma Based on Epidemiologic Factors and Genetic Variants
Dong J, Buas MF, Gharahkhani P, Kendall BJ, Onstad L, Zhao S, Anderson LA, Wu AH, Ye W, Bird NC, Bernstein L, Chow WH, Gammon MD, Liu G, Caldas C, Pharoah PD, Risch HA, Iyer PG, Reid BJ, Hardie LJ, Lagergren J, Shaheen NJ, Corley DA, Fitzgerald RC, consortium S, Whiteman DC, Vaughan TL, Thrift AP. Determining Risk of Barrett’s Esophagus and Esophageal Adenocarcinoma Based on Epidemiologic Factors and Genetic Variants. Gastroenterology 2017, 154: 1273-1281.e3. PMID: 29247777, PMCID: PMC5880715, DOI: 10.1053/j.gastro.2017.12.003.Peer-Reviewed Original ResearchMeSH KeywordsAdenocarcinomaArea Under CurveAustraliaBarrett EsophagusCase-Control StudiesDatabases, FactualDecision Support TechniquesEsophageal NeoplasmsEuropeFemaleGene-Environment InteractionGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLife StyleLogistic ModelsMaleMiddle AgedModels, GeneticMolecular EpidemiologyMultifactorial InheritanceNorth AmericaOdds RatioPhenotypePolymorphism, Single NucleotidePredictive Value of TestsRisk AssessmentRisk FactorsROC CurveConceptsGastroesophageal reflux diseaseBarrett's esophagusEsophageal adenocarcinomaLifestyle factorsPolygenic risk scoresGERD symptomsNon-genetic factorsDemographic/lifestyle factorsNet reclassification improvementCharacteristic curve analysisAUC valuesRisk prediction modelEsophageal cancer studyInternational Barrett'sReflux diseaseHighest quartileNet reclassificationEpidemiologic factorsReclassification improvementLowest quartileHigh riskRisk scorePatientsEsophagusAbstractText
2016
Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci
Clyde MA, Weber R, Iversen ES, Poole EM, Doherty JA, Goodman MT, Ness RB, Risch HA, Rossing MA, Terry KL, Wentzensen N, Whittemore AS, Anton-Culver H, Bandera EV, Berchuck A, Carney ME, Cramer DW, Cunningham JM, Cushing-Haugen KL, Edwards RP, Fridley BL, Goode EL, Lurie G, McGuire V, Modugno F, Moysich KB, Olson SH, Pearce CL, Pike MC, Rothstein JH, Sellers TA, Sieh W, Stram D, Thompson PJ, Vierkant RA, Wicklund KG, Wu AH, Ziogas A, Tworoger SS, Schildkraut JM. Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. American Journal Of Epidemiology 2016, 184: 579-589. PMID: 27698005, PMCID: PMC5065620, DOI: 10.1093/aje/kww091.Peer-Reviewed Original ResearchConceptsEpidemiologic risk factorsEpithelial ovarian cancerYears of ageRisk factorsAbsolute riskOvarian cancerInvasive epithelial ovarian cancerCase-control studyOvarian Cancer Association ConsortiumHierarchical logistic regression modelsRisk prediction modelLogistic regression modelsProspective data setSignificant single nucleotide polymorphismsCase-control statusControl studyRisk predictionSingle nucleotide polymorphismsAgeCancerLow discriminatory powerWomenAUCRegression modelsNucleotide polymorphisms