Sasamoto, NaokoBabic, AnaRosner, Bernard A.Fortner, Renée T.Vitonis, Allison F.Yamamoto, HidemiFichorova, Raina N.Titus, Linda J.Tjønneland, AnneHansen, LouiseKvaskoff, MarinaFournier, AgnèsMancini, Francesca RomanaBoeing, HeinerTrichopoulou, AntoniaPeppa, EleniKarakatsani, AnnaPalli, DomenicoGrioni, SaraMattiello, AmaliaTumino, RosarioFiano, ValentinaOnland-Moret, N. CharlotteWeiderpass, ElisabeteGram, Inger T.Quirós, J. RamónLujan-Barroso, LeilaSanchez-Perez, Maria-JoseColorado-Yohar, SandraBarricarte, AurelioAmiano, PilarIdahl, AnnikaLundin, EvaSartor, HannaKhaw, Kay-TeeKey, Timothy J.Muller, DavidRiboli, ElioGunter, MarcDossus, LaureTrabert, BrittonWentzensen, NicolasKaaks, RudolfCramer, Daniel W.Tworoger, Shelley S.Terry, Kathryn L.2020-08-192020-08-192019-11-26Sasamoto N, Babic A, Rosner BA, Fortner RT, Vitonis AF, Yamamoto H, et al. Development and validation of circulating CA125 prediction models in postmenopausal women. J Ovarian Res. 2019 Nov 26;12(1):116.http://hdl.handle.net/10668/3159Background: Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker. Methods: We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses' Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC. Result: The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset. Conclusions: The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.enOvarian cancerEarly detectionCA125Prediction modelPostmenopausalOvarian neoplasmsNeoplasias ováricasEarly detection of cancerDetección precoz del cáncerDiagnóstico precozEarly diagnosisCA-125 AntigenAntígeno Ca-125PostmenopausePosmenopausiaMedical Subject Headings::Persons::Persons::Age Groups::Adult::AgedMedical Subject Headings::Chemicals and Drugs::Biological Factors::Antigens::Antigens, Neoplasm::Antigens, Tumor-Associated, Carbohydrate::CA-125 AntigenMedical Subject Headings::Check Tags::FemaleMedical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::HumansMedical Subject Headings::Persons::Persons::Age Groups::Adult::Middle AgedMedical Subject Headings::Diseases::NeoplasmsMedical Subject Headings::Phenomena and Processes::Reproductive and Urinary Physiological Phenomena::Reproductive Physiological Phenomena::Reproductive Physiological Processes::Sexual Development::Climacteric::Menopause::PostmenopauseMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Early Diagnosis::Early Detection of CancerMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Models, Theoreticalresearch article31771659Acceso abierto10.1186/s13048-019-0591-41757-2215PMC6878636