In 2002, the National Cancer Institutes Cancer Information Service fielded 64,825 calls33% of the years call volumefrom family members and friends of people who had been diagnosed with cancer. Besides asking for information to help their relative or friend, callers, especially family members, may have worried about what the cancer diagnosis meant for their own risk of developing the disease. Although 45% of men and 39% of women will be diagnosed with cancer in their lifetime, it is difficult to predict which individuals will be affected.
Noticeably absent from population-level predictions, however, are the fear and anxiety that can arise when trying to predict a particular persons likelihood of developing cancer. Individuals may or may not want to know their future health, based on the benefits they perceive they will get from knowing. A woman may make a decision about a prophylactic mastectomy based on the results of a test for a BRCA1 or BRCA2 gene mutation. A physician may recommend that a patient who smokes use a risk prediction model for lung cancer to provide added incentive for the patient to quit smoking. Or these patients may choose not to seek this information.
For cancer, a persons two primary risks are developing the disease in the first place and dying of it. Predicting the course of disease between these two points has proven easier than quantifying the risk of developing cancer. "As far as predicting the future individually there is an extremely long history that goes back to Hippocrates of people who have spent a lot of time working on predicting whats going to happen to a person after they develop disease," said Mitchell Gail, M.D., Ph.D., chief of the Biostatistics Branch of NCIs Division of Cancer Epidemiology and Genetics. "The people that are working in this area are usually people who are conducting clinical trials to treat specific diseases," said Gail. "They have a lot of information on the subsequent course of disease and survival in the patients that theyve treated and theyre able to use the factors that they observe at the time of diagnosis to predict how the patients going to do."
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As another example, Peter Bach, M.D., of Memorial Sloan-Kettering Cancer Center, New York, and colleagues recently published a tool for predicting an individual long-term smokers risk of developing lung cancer (see article, Vol. 95, No. 6, p. 470). The benefit of these types of models, which quantify an individuals risk of developing and possibly dying from a particular cancer, is that armed with the risk information, people can choose whether to alter their lifestyle to fight a genetic predisposition.
But these two predictive models only work for specific sets of people. The breast cancer risk assessment tools results have predictive value only for women over the age of 35 who have not previously been diagnosed with breast cancer. The results are most accurate for Caucasian women, although data that better quantifies the risk for African American and Hispanic women were added to the tool in 2001. The lung cancer risk assessment tools results are predictive only for people between the ages of 50 and 75 who smoked 10 to 60 cigarettes a day for 25 to 55 years. The results are most accurate for current smokers and for smokers who quit fewer than 20 years before estimating their risk. These limiting factors necessary for applying the models point to how difficult it is to accurately quantify risk.
Another tool available to assess cancer risk is the Harvard Center for Cancer Preventions Your Cancer Risk tool, which provides estimates of the risk of developing any of 12 different types of cancer. The creators of the tool note that it can be used only to emphasize areas of increased risk, not predict the occurrence of a specific cancer, and that the tool only applies to people older than age 40 who have not previously had cancer. However, anyone can take the online questionnaires to learn about their own risk of developing cancer in the future and actions they can take to reduce that risk.
Beyond modeling, individuals can look at their own lifestyle and determine what factors may put them at risk for developing certain types of cancers. NCI provides specific information about risk factors for 11 types of cancer. "One thing to keep in mind is that not all risk factors are equal," noted Lisa Schwartz, M.D., of the Department of Veterans Affairs Medical Center, in White River Junction, Vt., and Dartmouth Medical School, Hanover, N.H. "The strength of association and evidence for causality varies widely. It is important to distinguish between factors which are based on solid, consistent scientific evidence and have a large effect on risk from those which are speculative or small in magnitude."
Because predicting an individuals risk of developing cancer is so difficult, communicating well what is known about risk is crucial. Schwartz and colleague Steven Woloshin, M.D., also of the Department of Veterans Affairs Medical Center and Dartmouth Medical School, have studied aspects of the ways women understand their risk of developing breast cancer. They stress the importance of giving context to make risk estimates easier to understand. "Weve found that its easier to tell someone Heres your risk with x, and heres your risk without x," said Woloshin. "It might be like, Heres your risk without a family history and heres your risk if you do have a family history. I think that sometimes thats probably the most helpful thing to do. You see how much your family history really leaves you with."
Since Gail updated the model used for NCIs breast cancer risk assessment tool in 2001, the results of the tool follow this basic premise. Individualized risk estimates of a woman developing invasive breast cancer are now given in comparison to a woman of average risk rather than in comparison to a woman with no risk factors at all. Gail is working now to improve the model further. "Were trying to improve predictions by incorporating stronger risk factors," said Gail.
Gail sees other applications for individual predictive models down the road. He would like to see them developed to help physicians make decisions about preventive treatment. "One thing I noticed when trying to use models of this type to help women decide whether to take tamoxifen or not was that there are not models for adverse events, for example, [that increase] the risk of stroke or pulmonary embolism or endometrial cancer," said Gail. "So there are many events for which it would be advantageous to develop individualized models to help in the decision process. Its not only (the risk of developing) breast cancer that determines whether you should take tamoxifen, its your risk of all these other factors too. It would be nice to know what your risk of these other events is both in the presence and in the absence of tamoxifen, before making a decision [about taking tamoxifen]."
And just as valuable are models, such as the Gail model and Bachs lung cancer model, that can take actual risk factors that may concern a patient and quantify them so the patient has a clearer picture of his or her risk of cancer. "Some people have very rich models for cancer and risk which include all sorts of things, things that scientific evidence may not support, things like diet and hormones, in addition to the traditional risk factors," said Woloshin. "But its not so clear how strongly they weigh these things, just that these things exist in their minds."
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