A statistical model based on risk factors says it can predict the probability (absolute risk) of a woman developing breast, ovarian, and endometrial (womb) cancer using easily obtainable information on known risk factors for these cancers.

The authors developed these models by using information from two large US studies that included white, non-Hispanic women aged over 50 years and by including commonly known risk factors, such as parity (the number of children a women delivered), body mass index (an indicator of the amount of body fat), use of oral contraceptives, and menopausal status and use of menopausal hormone therapy.

The resulting models were able to predict individual women's risk of each cancer: for example, individual women's risk for endometrial calculated using this model ranged from 1.22% to 17.8% over the next 20 years depending on their exposure to various risk factors.

The authors say: "These models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors and may assist in clinical decision-making."

Since the date were white, non-Hispanic women aged 50 years or older, they may not accurately predict absolute risk for these cancers for women of other races or ethnicities:  "Limitations are the modest discriminatory ability of the breast and ovarian models and that these models may not generalize to women of other races."

In an accompanying Perspective, Lars Holmberg from Uppsala University Hospital in Sweden and Andrew Vickers from the Memorial Sloan-Kettering Cancer Center in New York (uninvolved in the study) support the focus of the model on helping with clinical-decision making and say: "Ruth Pfeiffer and colleagues present models for absolute risks and thereby avoid the common mistake of proclaiming a substantial relative risk as clinically relevant without considering the background risk."

Citation: Pfeiffer RM, Park Y, Kreimer AR, Lacey JV Jr, Pee D, et al. (2013) Risk Prediction for Breast, Endometrial, and Ovarian Cancer in White Women Aged 50 y or Older: Derivation and Validation from Population-Based Cohort Studies. PLoS Med 10(7): e1001492. doi:10.1371/journal.pmed.1001492