A recent cover story in the New York Times Magazine made a convincing case against the mammogram. Its main criticism was that mammograms result in many false-positives, which other research has confirmed. Women get treated for cancers they don’t have or for cancers that are noninvasive but which doctors at the moment can’t distinguish from the malignant ones. All of this leads to a lot of wasted money, stress and distrust of the system as a whole.
While this is almost certainly true, many in the scientific community prefer to look at mammograms in a different way. Mammograms are a life-saving screening method, but they are not being utilized properly.
But there is a way to fix that. A group of researchers at the University of Wisconsin-Madison, Georgia Institute of Technology and Harvard Medical School have started to use risk-factor models that can help eliminate some of the harms associated with mammography and use it to its full potential.
With mammograms, it’s becoming increasingly apparent that a one-size-fits-all recommendation is not the ideal approach. Prof. Oguzhan Alagoz from the University of Wisconsin, his former doctoral student Turgay Ayer from the Georgia Institute of Technology and Natasha Stout from Harvard Medical School are working on a model that will determine the optimal time for a woman to get her next mammogram.
Currently, screening recommendations are based almost exclusively on age. The American Cancer Society and many other groups recommend annual mammograms starting at 40, while the U.S. Preventive Services Task Force recommends biennial tests starting at 50. Individual physicians will then discuss earlier screening based on other risk factors.
But age is not the only relevant risk factor for breast cancer. Family history, alcohol use, number of lifetime menstrual cycles and breast density are just a few of the myriad other factors. Ayer, Alagoz and Stout’s model accounts for many of these risk factors and personal history information and using this information, calculates the best time for a woman’s next mammogram.
The model works by calculating the risk of getting either invasive or noninvasive breast cancer. While noninvasive cancers pose little threat to the woman, they can sometimes progress to invasive cancer. So the model includes the likelihood of the woman getting noninvasive cancer simply because that is a risk factor for invasive cancer. The model then makes a recommendation based on the risk of invasive or noninvasive cancer. If the woman’s overall invasive cancer risk exceeds the desired threshold, a mammogram is recommended.
A recent article in Operations Research shows how the model would be used.
Take a 40-year-old white woman who has no history of breast cancer in her family, who started menstruating at 14 and had her first child at 23. Because her chances of getting in situ cancer or invasive cancer are low (0.1 percent and 0.2 percent), the model recommends waiting to get a mammogram until she turns 42.
They then look at a 50-year-old woman who has the same risk factors as the first woman but didn’t have any mammograms during that time. This time, the model recommends getting a mammogram because her risk, at this later age, is high enough to justify the screening.
If the woman has another negative mammogram, the intervals continue to increase. Conversely, an unusual mammogram result, such as a benign cyst in the breast, can prompt a woman to shorten the time between her next mammogram.
In the end, the model would create a single statistic that would account for the individual’s breast-cancer risk factors and her previous screening decisions and results. Ideally, this statistic would be a starting point for discussion among the radiologist, physician and patient. It would help with mammography decisions of course, but it would also be useful in discussing other breast-cancer prevention treatments, such as the drugs tamoxifen or raloxifene.
In addition, the model could help reduce the $100 million that we overspend on mammography nationwide. These savings come from a simultaneous drop in the number of mammograms and the number of false-positive mammograms. Using the screening guidelines set in this model, researchers estimate that an average woman would need 14 fewer mammograms and that they would at least halve the number of false-positive mammograms.
Of course, implementation of such a model might be controversial. When the Preventive Services Task Force changed its mammography recommendations, there was a major backlash. Many claimed that the task force didn’t truly have women’s interests in mind and that it was in the pockets of the insurance companies.