December 5, 2013
Computer Model Suggests Genetic Breast Cancer Screening May Benefit Those At Intermediate Risk
Study findings published in Cancer Prevention Research underscore viability of simulation modeling to stratify patients by disease risk to better focus resources where most beneficial
Archimedes Inc., a healthcare modeling and analytics company, today announced results of a simulated clinical trial which found that the seven single-nucleotide polymorphisms (7SNP) genetic test for breast cancer was most cost effective when used to guide MRI screenings for patients found to have an intermediate lifetime risk of developing the disease. The study, "Cost-effectiveness of a genetic test for breast cancer risk," appeared in the December 5th online issue of the peer-reviewed journal Cancer Prevention Research."This Archimedes Model simulation suggests that genetic screening for breast cancer risk in conjunction with MRI can reduce cancer deaths and identifies a population at an intermediate risk near 20 percent for which it is optimally cost effective," said Tuan Dinh, PhD, vice president of analytics and modeling at Archimedes and one of the authors of study. "This study further illustrates that risk modeling may provide information that will enable physicians to better determine a patient's risk of disease and more appropriately allocate resources that will be beneficial."
In 2007, the American Cancer Society recommended MRI as an adjunct to mammography for the screening of breast cancer in women who have a lifetime risk of breast cancer of approximately 20-25 percent or greater as determined by models based on family history such as the Gail test. In the virtual study, researchers used Archimedes' detailed simulation model of breast cancer risk factors, disease progression, and healthcare processes to estimate the costs and benefits of using genetic testing to refine estimates of risk for purposes of referring women to MRI screening. The simulation included growth, detection, and spread of tumors, as well as screening and treatment.
The model compared two tests to categorize patients by lifetime risk, the Gail risk test and the 7SNP test. The Gail model, which is widely used by the National Cancer Institute, estimates risk using information on age, race, family history, and age of menarche and first live birth. The 7SNP genetic test uses the genotype of the patient to refine the estimate of the Gail test. In the simulated study, average-risk patients received an annual mammogram and high-risk patients received an annual MRI.
The simulated population consisted of 100,000 non-Hispanic white women starting at the age of 40 with no prior history of cancer and a lifetime Gail risk of breast cancer of at least 10 percent. Cancer incidence was based on Surveillance, Epidemiology, and End Results (SEER) data and validated to the Cancer Prevention Study II (CPS-II) Nutrition Cohort dataset. Risk factors were drawn from the National Health and Nutrition Examination Survey (NHANES-4) and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data. Mammogram characteristics were derived from Breast Cancer Surveillance Consortium data.
For patients with a lifetime risk of at least 10 percent, the model showed that the 7SNP test results in a 2.7 percent reduction in cancer deaths relative to the Gail test alone. For patients with a risk of 16-28 percent, the 7SNP test saved 0.005 quality-adjusted life years (QALY) per person at a cost of $163,264 per QALY. The cost effectiveness of using the 7SNP test for patients with intermediate Gail risk is similar to that of other recommended strategies, including annual MRI for patients with a lifetime risk greater than 20 percent or BRCA1/2 mutations, for which the model estimated a cost of $141,415 per QALY, relative to mammogram.
"These findings may help physicians and their patients as they strive to identify optimal breast cancer screening options for individual women based on their current risk profile," added Dr. Henri Folse, lead author of the study. "In addition, investigators can use mathematical modeling and cost-effectiveness analyses, such as those described in this study, to identify an optimal range of risk for which prevention and screening strategies are most cost effective."
This study was a collaborative project between Archimedes and Genetic Technologies Ltd.
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