RT Journal Article
T1 Development and validation of circulating CA125 prediction models in postmenopausal women
A1 Sasamoto, Naoko
A1 Babic, Ana
A1 Rosner, Bernard A.
A1 Fortner, Renée T.
A1 Vitonis, Allison F.
A1 Yamamoto, Hidemi
A1 Fichorova, Raina N.
A1 Titus, Linda J.
A1 Tjønneland, Anne
A1 Hansen, Louise
A1 Kvaskoff, Marina
A1 Fournier, Agnès
A1 Mancini, Francesca Romana
A1 Boeing, Heiner
A1 Trichopoulou, Antonia
A1 Peppa, Eleni
A1 Karakatsani, Anna
A1 Palli, Domenico
A1 Grioni, Sara
A1 Mattiello, Amalia
A1 Tumino, Rosario
A1 Fiano, Valentina
A1 Onland-Moret, N. Charlotte
A1 Weiderpass, Elisabete
A1 Gram, Inger T.
A1 Quirós, J. Ramón
A1 Lujan-Barroso, Leila
A1 Sanchez-Perez, Maria-Jose
A1 Colorado-Yohar, Sandra
A1 Barricarte, Aurelio
A1 Amiano, Pilar
A1 Idahl, Annika
A1 Lundin, Eva
A1 Sartor, Hanna
A1 Khaw, Kay-Tee
A1 Key, Timothy J.
A1 Muller, David
A1 Riboli, Elio
A1 Gunter, Marc
A1 Dossus, Laure
A1 Trabert, Britton
A1 Wentzensen, Nicolas
A1 Kaaks, Rudolf
A1 Cramer, Daniel W.
A1 Tworoger, Shelley S.
A1 Terry, Kathryn L.
K1 Ovarian cancer
K1 Early detection
K1 CA125
K1 Prediction model
K1 Postmenopausal
K1 Ovarian neoplasms
K1 Neoplasias ováricas
K1 Early detection of cancer
K1 Detección precoz del cáncer
K1 Diagnóstico precoz
K1 Early diagnosis
K1 CA-125 Antigen
K1 Antígeno Ca-125
K1 Postmenopause
K1 Posmenopausia
AB Background: 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.
PB BioMed Central Ltd.
YR 2019
FD 2019-11-26
LK http://hdl.handle.net/10668/3159
UL http://hdl.handle.net/10668/3159
LA en
NO Sasamoto 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.
DS RISalud
RD Apr 11, 2025