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New model to calculate risk of cancers

Researchers in the United States have produced a new tool to calculate a woman’s risk of developing breast, ovarian and endometrial cancer over a 10 and 20 year period.

The statistical model uses easy-to-obtain risk factors like weight, alcohol use, the number of times a woman has given birth, and use of contraceptive pills or hormone therapy at menopause.

It was produced by scientists at the National Cancer Institute (NCI) in Bethesda and the study was published in the journal PLOS Medicine.

Dr Ruth Pfeiffer, senior investigator at the NCI, and her team developed the tool after compiling data from two large population-based studies.

They found that risk factors for women getting breast cancer included drinking alcohol, being overweight, having children later in life, having used menopausal hormonal therapy, and having a family history of breast cancer.

The risk factors used in the endometrial and ovarian cancer models include age at menopause, weight, menopause status, smoking history, use of oral contraceptives and menopausal hormonal therapy. Family history is also included as a risk factor for ovarian cancer.

Depending on exposure to these factors, the risk of getting breast cancer over a 10 year period ranged from 1.5 per cent to 22 per cent. The risk for endometrial cancer was 0.4 per cent to 10.5 per cent, and the risk for ovarian cancer was 0.3 per cent to 0.96 per cent.

Dr Pfeiffer said the models could help doctors when assessing patients’ cancer risk and identify patients who could benefit from early treatment and intervention.

“These findings show that breast, ovarian and endometrial cancer can all be predicted using information on known risk factors for these cancers that is easily obtainable,” she said.

“Absolute risk prediction models are useful in the design of cancer prevention trials and can also help women make informed decisions about cancer prevention and treatment options.

“For example, a woman at high risk of breast cancer might decide to take Tamoxifen for breast cancer prevention, but ideally she needs to know her absolute endometrial cancer risk before doing so, because Tamoxifen increases the risk of this cancer.

“Similarly, knowledge of her ovarian cancer risk might influence a woman’s decision regarding prophylactic removal of her ovaries to reduce her breast cancer risk.”

“Importantly … these well-calibrated models should provide realistic information about an individual’s risk of developing breast, ovarian or endometrial cancer that can be used in clinical decision-making and that may assist in the identification of potential participants for research studies.”

Dr Pfeiffer said the while there were already some breast cancer risk models available, up until now there were few such models for ovarian cancer and none for endometrial cancer, despite the three cancers sharing several key characteristics.

“In 2008, just three types of cancer accounted for 10 per cent of global cancer-related deaths,” she said in the PLOS report.

“That year, about 460,000 women died from breast cancer (the most frequently diagnosed cancer among women and the fifth most common cause of cancer-related death). Another 140,000 women died from ovarian cancer, and 74,000 died from endometrial (womb) cancer, the 14th and 20th most common causes of cancer-related death respectively.

“Although these three cancers originate in different tissues, they nevertheless share many risk factors. For example, current age, age at menarche (first period) and parity (the number of children a woman has had) are all strongly associated with breast, ovarian and endometrial cancer risk.

“Because these cancers share many hormonal and epidemiological risk factors, a woman with a high breast cancer risk is also likely to have an above-average risk of developing ovarian or endometrial cancer.”

The data used to build the model was drawn from white, non-Hispanic women and Dr Pfeiffer and her team warned that it may not accurately predict cancer risk for women of other ethnicities.

Debra Vermeer

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