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Title: Development and validation of circulating CA125 prediction models in postmenopausal women
Author: Sasamoto, Naoko
Babic, Ana
Rosner, Bernard A.
Fortner, Renée T.
Vitonis, Allison F.
Yamamoto, Hidemi
Fichorova, Raina N.
Titus, Linda J.
Tjønneland, Anne
Hansen, Louise
Kvaskoff, Marina
Fournier, Agnès
Mancini, Francesca Romana
Boeing, Heiner
Trichopoulou, Antonia
Peppa, Eleni
Karakatsani, Anna
Palli, Domenico
Grioni, Sara
Mattiello, Amalia
Tumino, Rosario
Fiano, Valentina
Onland-Moret, N. Charlotte
Weiderpass, Elisabete
Gram, Inger T.
Quirós, J. Ramón
Luján Barroso, Leila
Sánchez, Maria Jose
Colorado Yohar, Sandra
Barricarte, Aurelio
Amiano, Pilar
Idahl, Annika
Lundin, Eva
Sartor, Hanna
Khaw, Kay-Tee
Key, Timothy J.
Muller, David C.
Riboli, Elio
Gunter, Marc
Dossus, Laure
Trabert, Britton
Wentzensen, Nicolas
Kaaks, Rudolf
Cramer, Daniel W.
Tworoger, Shelley S.
Terry, Kathryn L.
Keywords: Càncer d'ovari
Marcadors bioquímics
Ovarian cancer
Biochemical markers
Issue Date: 26-Nov-2019
Publisher: BioMed Central
Abstract: 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.
Note: Reproducció del document publicat a:
It is part of: Journal Of Ovarian Research, 2019-11-26, vol. 12, num. 116
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Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
Publicacions de projectes de recerca finançats per la UE

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