Development and validation of circulating CA125 prediction models in postmenopausal women

Naoko Sasamoto, Ana Babic, Bernard A Rosner, Renée T Fortner, Allison F Vitonis, Hidemi Yamamoto, Raina N Fichorova, Linda J Titus, Anne Tjønneland, Louise Hansen, Marina Kvaskoff, Agnès Fournier, Francesca Romana Mancini, Heiner Boeing, Antonia Trichopoulou, Eleni Peppa, Anna Karakatsani, Domenico Palli, Sara Grioni, Amalia MattielloRosario Tumino, Valentina Fiano, N Charlotte Onland-Moret, Elisabete Weiderpass, Inger T Gram, J Ramón Quirós, Leila Lujan-Barroso, Maria-Jose Sánchez, Sandra Colorado-Yohar, Aurelio Barricarte, Pilar Amiano, Annika Idahl, Eva Lundin, Hanna Sartor, Kay-Tee Khaw, Timothy J Key, David Muller, Elio Riboli, Marc Gunter, Laure Dossus, Britton Trabert, Nicolas Wentzensen, Rudolf Kaaks, Daniel W Cramer, Shelley S Tworoger, Kathryn L Terry

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Article number116
Number of pages12
JournalJournal of Ovarian Research
Issue number1
Publication statusPublished - 26 Nov 2019


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