Ovarian Cancer Risk Prediction Models
Absolute Risk Prediction Models
Absolute cancer risk is the probability that an individual with given risk factors and a given age will develop cancer over a defined period of time. Examples of these risk factors include race, age, sex, genetics, body mass index, family history of cancer, history of tobacco use, use of aspirin and nonsteroidal anti-inflammatory drugs (NSAIDS), physical activity, use of hormone replacement therapy, reproductive factors, history of cancer screening, and dietary factors.
Developing statistical models that estimate the probability of developing cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk. These types of models also will be useful for designing future chemoprevention and screening intervention trials in individuals at high risk of specific cancers in the general population.
The following risk prediction models are intended primarily for research use and have been peer-reviewed, meaning the methodology and results of these models have been evaluated by qualified scientists and clinicians and published in scientific and medical journals.
- Andersen MR, Goff BA, Lowe KA, et al. Use of a Symptom Index, CA125, and HE4 to predict ovarian cancer
. Gynecologic Oncology. Mar 2010;116(3):378-383.
- Skates SJ, Menon U, MacDonald N, Rosenthal AN, Oram DH, Knapp RC, Jacobs IJ. Calculation of the risk of ovarian cancer from serial CA-125 values for preclinical detection in postmenopausal women. J Clin Oncol. 2003 May 15;21(10 Suppl):206s-210s.
- Hartge P, Whittemore AS, Itnyre J, McGowan L, Cramer D. Rates and risks of ovarian cancer in subgroups of white women in the United States. The Collaborative Ovarian Cancer Group. Obstet Gynecol. 1994 Nov;84(5):760-4.
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