Key Points The Cancer Intervention and Surveillance Modeling Network, or CISNET, is a group of researchers supported by NCI who create statistical models to help understand the impact of cancer control interventions, such as screening and treatment, on cancer incidence and mortality at the population level. (Question 1) The CISNET breast cancer collaborators seek to quantify the impact of adjuvant therapies (various combinations of drug treatments) and mammography screening on the recent decline in breast cancer mortality in the U.S. (Question 2) The CISNET breast cancer study found that the decline in breast cancer mortality could not be explained by either adjuvant therapies or screening alone. Instead, it was found that women benefit from both adjuvant therapy and mammography screening. The researchers attributed about half of the mortality decline to adjuvant therapies and half to screening. (Question 6) As highlighted in a 2006 Journal of the National Cancer Institute monograph, CISNET has demonstrated a process for collaborative work and an example of how statistical modeling can play an expanded role in providing input to public health policy and decision making. (Question 8)

1. What is CISNET?
The Cancer Intervention and Surveillance Modeling Network, or CISNET, is a group of researchers supported by the National Cancer Institute (NCI), part of the National Institutes of Health (NIH) (http://cisnet.cancer.gov). The group was formed to create statistical models to better understand the impact of cancer control interventions, such as screening and treatment, on cancer incidence and mortality at the population level. These models are used to explain past trends, to project future trends, and to help identify the best strategies for cancer control. CISNET grantees are focusing on breast, prostate, colorectal, and lung cancer.

2. What is the goal of the CISNET breast cancer collaboration?
CISNET breast cancer collaborators seek to determine the relative contributions of adjuvant therapies (various combinations of drug treatments given after surgery) and mammography screening to the 23.5 percent decline in breast cancer mortality observed in the U.S. population between 1990 and 2000 for women ages 30 to 79. Many scientists have attributed this reduction to an increase in the number of women receiving mammograms, which increases the likelihood of early detection and successful treatment. However, some leading scientists think that most of the mortality decline is due to improvements in adjuvant therapies after diagnosis, rather than to increased participation in cancer screening. This attempt to quantify the importance of mammography screening is particularly important given recent controversies over the value of mammography and the potential for overdiagnosis and false-positive results.

3. Who are the CISNET breast cancer collaborators?
The CISNET breast cancer study is a collaboration of doctors, cancer researchers, population scientists, and statisticians located at NCI (Bethesda, Md.), Dana-Farber Cancer Institute (Boston, Mass.), Erasmus MC, University Medical Center (Rotterdam, Netherlands), Georgetown University Medical Center (Washington, D.C.), University of Texas M. D. Anderson Cancer Center (Houston, Texas), Stanford University (Stanford, Calif.), University of Rochester (Rochester, N.Y.), University of Wisconsin (Madison, Wisc.), and Cornerstone Systems Northwest, Inc. (Lynden, Wash.).

4. How does CISNET modeling work?
In order to quantify the individual contributions mammography and adjuvant therapy have made to the decline in breast cancer mortality rates, seven different CISNET modeling groups used common data inputs and asked common questions about expected outcomes. For consistency in modeling, inputs included the patterns and extent of mammography and adjuvant therapy use, and questions sought information about outcomes such as improvement or decline in health with or without these two factors. This approach used population data to describe the extent and patterns of mammography and adjuvant therapy usage in the U.S. over time. The seven groups worked independently to develop their models but worked together to consider shared problems and to enable comparisons by developing uniform reporting methods.

The usage patterns were then combined with all of the available information on the benefits of these advances that had been compiled by the seven independent modeling groups. The seven models differ in their structure, assumptions, and use of study results. Using this variety of approaches gives a broad view of the relationships between screening, treatment, and mortality while accounting for the uncertainty related to the modeling results. The comparative modeling approach enabled each group to synthesize existing, and sometimes conflicting, knowledge to build models of breast cancer screening and treatment using certain common population-level inputs.

5. How does CISNET modeling determine the contribution of mammography screening to the decline in breast cancer mortality?
Two distinct components were necessary to model the contribution of mammography to the decline in breast cancer mortality. The first, a shared component used by all seven modelers, was the patterns of mammography usage over time. Population-level data from the National Health Interview Survey (NHIS) were used to estimate the age at which a woman receives her first mammogram. Data from the Breast Cancer Surveillance Consortium were used to model how often a woman will return for repeat mammograms. These two data sources were then used to generate individual mammography histories that were representative of the U.S. population from 1975 to 2000. These histories reflect the rapid rise in the use of mammography in the late 1980s and early 1990s. In some cases, women may have received no mammography exams. At the other extreme, some women may continue to receive mammograms regularly after their initial exams. NCI recommends mammograms every one to two years for women over age 40.

The second component, which each of the seven groups was free to develop independently, took into account the benefits of mammography for those who received it. While there were a wide variety of approaches, most groups modeled the development and growth of tumors prior to clinical symptoms, the ability of mammography to detect tumors of a specific size and/or stage, and the ultimate ability of early detection to either help delay the time of death from breast cancer or help cure the patient.

6. How does CISNET modeling determine the contribution of adjuvant therapies to the decline in breast cancer mortality?
As with mammography, two components were necessary to model the contribution of adjuvant therapy to the decline in breast cancer mortality. The first was a shared component, which represented the patterns of usage of adjuvant therapies developed in the 1970s, 80s, and 90s, such as multi-agent chemotherapy, tamoxifen (a drug that has been shown to reduce the risk of breast cancer recurrence) given for two or five years, or a combination of both treatments.

These patterns were developed using data from NCI's Surveillance, Epidemiology, and End Results (SEER) program, and supplemented by SEER-based studies that examined patterns of cancer patient care. As the safety and effectiveness of these therapeutic regimens were validated over time, recommendations that determined which patients received these treatments changed. Thus, the usage varied as a function of calendar year, age of the patient at diagnosis, estrogen receptor status of the tumor, and stage of the disease.

The second component, which each group outlined independently, described the benefits of adjuvant therapy to those who received it. Although there was some variability in how this information was applied, most groups based their models on a synthesis of randomized clinical trials of adjuvant therapies for breast cancer conducted by the Early Breast Cancer Trialists' Collaborative Group (EBCTCG) (1,2).

7. What did the CISNET Breast Cancer Collaborators find?
In results published in the October 27, 2005, issue of the New England Journal of Medicine, the CISNET breast cancer collaborators reported that the proportion of the total reduction in the rate of death from breast cancer attributed 28 percent to 65 percent of the total reduction in breast cancer mortality to screening, while adjuvant treatment was responsible for the remaining reduction in the mortality rate (3).

In October 2006, the Journal of the National Cancer Institute published a monograph detailing the models that the CISNET breast cancer collaborators used to obtain these results (4). As was reported in the New England Journal of Medicine, the CISNET monograph shows that all seven modeling groups found some benefit from both adjuvant therapy and mammography screening and concludes that the decline in breast cancer mortality can be explained by a combination of screening and therapy and not by either one alone. When the results of all of the modeling approaches were combined, about half of the decline in mortality was due to adjuvant therapies and half to screening.

8. How do the CISNET modeling results differ from information gained through randomized controlled trials?
Interventions such as mammography and adjuvant therapy have been studied in randomized controlled trials, which carefully assess the benefits and risks of particular treatments or screening methods for specific groups of people in comparison to control groups that do not receive the interventions. However, the impact of mammography or adjuvant therapy on a whole population may be different from that in a clinical trial. In randomized controlled trials, participants are specifically assigned to receive a certain screening procedure or therapy, and the frequency and extent of use are predetermined. In a population setting, like that used in CISNET modeling, the patterns of mammography or adjuvant therapy usage vary widely among individuals.

To estimate the benefits of adjuvant treatment, the CISNET breast cancer analysis combined results from clinical trials with information on the extent of usage in the U.S. population to estimate the impact on mortality. Although randomized controlled trials of mammography have provided a rich source of information, meta-analyses attempting to estimate the precise benefit of mammography using combined data from these trials have been conflicting; individual trials have been variably excluded from these meta-analyses because of perceived shortcomings in the way they were designed and conducted. The CISNET modeling effort showed that the observed decline in mortality in the U.S. would not have been detected without a sizable contribution from mammography.

9. What is the value of CISNET modeling?
In addition to investigating the reasons for declining breast cancer mortality, a comparison of the seven CISNET breast cancer models gives unique insight into the complicated process of modeling population dynamics. This new monograph demonstrates a process for collaborative work and an example of how statistical modeling can play an important role in informing public health policy and decision making.

10. How can I use the CISNET breast cancer models?
These models are not designed for use by or for cancer patients or clinicians seeking treatment guidance for individual cases. Rather, they are based largely on retrospective surveillance data and have been built to aid researchers' and policy makers' understanding of the impact of cancer control interventions, such as prevention, screening, treatment, and therapies, on population trends in incidence and mortality.

11. Where can I find more information?

• CISNET Homepage: http://cisnet.cancer.gov/
• CISNET breast cancer model simulator: http://cisnet.flexinterface.net/home.fxi?&action=load&sid=breast#glossary_nhissurvey
• NCI's Surveillance, Epidemiology, and End Results (SEER) program: http://seer.cancer.gov/
• SEER-based Patterns of Care Studies: http://healthservices.cancer.gov/surveys/poc/
• National Health Interview Surveys: http://www.cdc.gov/nchs/nhis.htm
• NCI's Breast Cancer Surveillance Consortium: http://breastscreening.cancer.gov/espp.pdf
• NCI Publications:
  • What You Need To Know About™ Breast Cancer
  • Breast Cancer PDQ®

Selected References

1. Early Breast Cancer Trialists' Collaborative Group. Polychemotherapy for early breast cancer: an overview of the randomized trials. Lancet 1998;352:930-42.
2. Early Breast Cancer Trialists' Collaborative Group. Tamoxifen for early breast cancer: an overview of the randomized trials. Lancet 1998;351:1451-67.
3. Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen M, Mandelblatt JS, Yakovlev AY, Habbema JDF, Feuer EJ, for the Cancer Intervention and Surveillance Modeling Network (CISNET) Collaborators. Effect of screening and adjuvant therapy on mortality from breast cancer. New England Journal of Medicine 2005;353:1784-92.
4. Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Collaborators. The Impact of Mammography and Adjuvant Therapy on U.S. Breast Cancer Mortality (1975-2000): Collective Results from the Cancer Intervention and Surveillance Modeling Network. Journal of the National Cancer Institute 2006;36:1-126.

Back to Top