Affiliations of authors: Division of Cancer Control and Population Sciences (ANF, DS, RB-B) and Division of Cancer Epidemiology and Genetics (MHG, PH, RMP), National Cancer Institute, Bethesda, MD; Department of Epidemiology, Harvard School of Public Health, Boston, MA (GAC)
Correspondence to: Andrew N. Freedman, PhD, National Cancer Institute, National Institutes of Health, EPN 4005 MSC 7344, 6130 Executive Blvd., Bethesda, MD 208927344 (e-mail: Andrew_Freedman{at}nih.gov).
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ABSTRACT |
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INTRODUCTION |
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The number of models has grown steadily (Table 1) since the first risk prediction model for a chronic disease was published in 1976 (47). This model, the Framingham Coronary Risk Prediction Model, used several clinical and biologic factors to predict an individual's risk of developing heart disease. Modified versions of this early model are now widely used by physicians to make decisions on prevention and treatment strategies (48). In the late 1980s and early 1990s, investigators began to publish models that predicted the absolute risk of breast cancerthat is, the probability that an individual would develop breast cancer over a defined period of time. These models incorporated known risk factors, such as age, age at menarche, age at first live birth, and family history of breast cancer. After the discovery of the breast cancer susceptibility genes BRCA1 and BRCA2 between 1994 and 1995, a number of genetic susceptibility risk prediction models were developed to predict the likelihood that an individual carried a BRCA1 or BRCA2 gene for breast cancer by use of her family history.
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As the number and sophistication of cancer risk prediction models have grown, so too has interest in ensuring that the models are appropriately applied, correctly developed, and rigorously evaluated. On May 2021, 2004, the NCI sponsored "Cancer Risk Prediction Models: a Workshop on Development, Evaluation, and Application" in Washington, DC. Experts currently developing, evaluating, or using risk prediction models met to identify strengths and limitations of cancer and genetic susceptibility prediction models currently in use and under development; to explore methodologic issues related to their development, evaluation, and validation; and to identify research priorities and resources needed to advance the field.
This report summarizes the presenters' major points by topic area, provides additional highlights from specific presentations, briefly describes several risk prediction models presented at the meeting, and summarizes participants' recommendations for future research and activity.
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ISSUES IN APPLICATIONS OF CANCER RISK PREDICTION MODELS |
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Cancer risk prediction models also have been used to develop benefitrisk indices. For example, although the Breast Cancer Prevention Trial demonstrated that tamoxifen treatment produced a 49% reduction in invasive breast cancer in women at elevated disease risk, adverse events such as stroke, pulmonary embolism, and endometrial cancer also occurred more often in women taking tamoxifen than in women who did not take tamoxifen. Using the Gail model, Gail et al. (51) created a benefitrisk index that weighs the benefits of taking tamoxifen against the reduced breast cancer risk and the risks of adverse events. Rather than recommend a single 5-year level of breast cancer risk, such as the figure of 1.67%, Gail et al. found that the level of risk needed to justify the use of tamoxifen for breast cancer prevention was much higher in older women, who had higher risks of adverse events.
Another application of risk prediction models is estimating the population burden, the cost of cancer, and the impact of a specific intervention. For example, using the Gail model and a benefitrisk index, Freedman et al. (52) were able to estimate the numbers of women who would be eligible for and would benefit from taking tamoxifen for breast cancer chemoprevention in the United States.
Perhaps the best known use of cancer risk prediction models is in clinical decision-making to help physicians and patients determine appropriate screening regimens and/or interventions. Genetic susceptibility risk models also are used by physicians for their patients with a strong family history to estimate their cancer risk and to help them decide whether to pursue genetic testing.
At the meeting, participants stressed the need to consider the appropriate use of risk prediction models in different contexts and the related implications for development, application, and validation. For example, those who use models should determine the extent to which models that were developed and validated at the population level are useful in aiding decision-making for an individual.
Intervention Trials
Dr. Joseph Costantino (Graduate School in Public Health, University of Pittsburgh) stressed the need for good population data on baseline rates for non-cancer events, so that model developers can incorporate competing causes of death into benefitrisk indices. He also noted that most cancer risk prediction models were developed predominately from a Caucasian population. Although the models work well for these populations, race-specific estimates of relative and attributable risks are needed to refine these models for use in non-Caucasian populations. Specifically, there is a need to develop new risk prediction models for both breast cancer and prostate cancer in African Americans, two common cancers with a high mortality in this population.
Population Burden of Disease and Impact of Changing Risk Factors
Dr. Karen Kuntz (Harvard School of Public Health) described how population-based simulation models could aid in evaluating the impact of changes over time in risk factors, screening and chemoprevention patterns and the cost-effectiveness of interventions. She mentioned that this topic was the focus of the Cancer Intervention and Surveillance Modeling Network, a consortium of NCI-sponsored investigators who use modeling to assess the impact of cancer control interventions (e.g., prevention strategies, screening, and treatment) on population trends in incidence and mortality. These models are also used to project future trends and to help determine optimal cancer control strategies (53).
Clinical Decision-Making (Breast Cancer Risk in General)
Dr. Laura Esserman (University of California, San Francisco, Carol Franc Buck Breast Care Center) spoke about the need to integrate all currently available information on risk to ensure that models were useful for clinical decision-making. She stressed that women could be helped through the use of decision aids that provide simple graphical information on their breast cancer risk and the risk and benefits of potential prevention strategies in the context of their overall health and the average breast cancer risk for the population. She also mentioned the critical need for models and decision aids to incorporate biomarker data, including imaging and genetic studies. These technologies can help clinicians and patients decide which intervention to pursue and can help clinicians assess the impact of these interventions on a patient. Decision aids also can facilitate a dialogue between patients and clinicians, motivate behavior change, and increase a woman's willingness to accept interventions or reassure her that her risk is not elevated compared with that of average women.
Clinical Decision-Making (Genetic Susceptibility and Breast Cancer Risk)
Dr. Susan Domchek (Abramson Cancer Center, University of Pennsylvania) began her talk by stressing the limitations of assessing risk from family history alone because factors such as adoption, small family size, and inaccurate family history may lead to erroneous conclusions about risk. She emphasized that the goal of breast cancer genetic susceptibility risk models was to identify candidates for screening for BRCA1 and/or BRCA2 mutations. Furthermore, she stressed that high sensitivity was needed for these models if they were to identify all mutation carriers. High specificity also is necessary, both clinically and economically, to avoid genetic testing of women who are less likely to be mutation carriers. She mentioned the limitations and tremendous variation in different prediction models and conceded that the current medical-legal environment encouraged clinicians in the United States to use the models that give the highest sensitivity but at a cost in decreases of specificity.
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ISSUES IN DEVELOPING CANCER RISK PREDICTION MODELS |
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Design Issues in Developing Risk Prediction Models
Dr. Mitchell Gail (NCI) spoke about different study designs that could be used to develop and evaluate models of absolute risk. Cohort studies allow one to obtain baseline hazard rates of incidence, hazard of mortality from competing risks, and relative risk estimates. However, cohort studies often focus on special populations, lack covariate data, require long follow-up times, and collect only imprecise data on competing causes of death. Sampling from a cohort to estimate relative risks and cumulative hazards with casecohort or nested casecontrol designs can compensate for some of these limitations.
Another strategy for developing risk prediction models is to combine casecontrol data with national registry data. This strategy can provide detailed information on covariates in a relatively short time. Several of these casecontrol studies can be combined to obtain a relative risk model. Drawbacks of this approach are the potential recall bias from the casecontrol study and the lack of national registry data for many non-cancer diseases.
Absolute risk associated with a mutation in a genetic susceptibility gene is commonly calculated by use of pedigrees of families with many affected members. Geneticists often correct for ascertainment by controlling for the family phenotypes or disease history. Dr. Gail commented on reasons why ascertainment correction may be suspect and noted that it was difficult to obtain accurate information on covariates from all members of a pedigree.
Incorporating Conceptual Issues in Risk Prediction Into Models
Dr. Colin Begg (Memorial Sloan-Kettering Cancer Center) used Lorentz curves to demonstrate the extent to which the inherently stochastic aspects of carcinogenesis limit the ability to predict a future breast cancer in healthy individuals and the extent to which unknown risk factors might improve upon the predictive accuracy of the Gail model. In contrast, there is no theoretical limit to the accuracy of identifying an existing pre-neoplastic lesion or early cancer. He explained that the more predictable the risk, the greater the rationale for focusing prevention strategies on high-risk individuals; broad population-based strategies are more appropriate for less predictable risks. He concluded by mentioning, that as new risk factors were identified, investigators were unlikely to be able to rely on single, large data sources to devise improved risk prediction models. Information will need to be assembled from different sources. Validation will be an especially pivotal concern for these models.
Incorporating Risk Factor Changes Over Time Into Models
Dr. Bernard Rosner (Harvard Medical School) stressed that breast cancer was a complex disease with multiple risk factors and that the nature of the risk factors and magnitude of their effect changed over time. For example, as a woman ages, her body mass index may increase, decrease, or remain stable. Breast cancer risks may be different in each of these cases, depending on the age of the woman. However, virtually all risk prediction models for breast cancer assume that it is a homogeneous disease, even though evidence is accumulating that risk profiles for breast cancer may vary according to both estrogen receptor and progesterone receptor status for some, but not all, risk factors. He discussed the need for different risk models for breast cancerspecific subtypes, noting that each of these cancer subtypes required different treatment decisions. Evaluating subtypes also may improve the discriminatory power of risk prediction models.
Incorporating Epidemiologic With Genetic Factors in Risk Model Development
Dr. Timothy Rebbeck (University of Pennsylvania) discussed characteristics of BRCA1 and/or BRCA2 mutation carriers and their families (e.g., age at diagnosis, cancer occurrence, tumor site, and prognosis) that may contribute to the heterogeneity of the disease, and he described what predictors may be required for personalized cancer risk assessment for these women. Factors, such as smoking, reproductive history, other genotypes unlinked to BRCA gene status, and interactions among these factors, may modify cancer risk. However, currently available data are insufficient to be useful in clinical risk prediction. A number of methodologic issues in studies attempting to identify these factors exist, including the choice of an appropriate sampling design.
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ISSUES IN EVALUATING AND VALIDATING CANCER RISK PREDICTION MODELS |
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Discrimination measures a model's ability to distinguish at the individual level between those who will develop disease and those who will not develop disease. Discrimination is commonly quantified by calculating the concordance statistic, which corresponds to the area under a receiver operating characteristic curve. Good discrimination in a model is important for decisions made at the individual level (i.e., clinical decision-making and screening).
Accuracy scoresincluding positive and negative predictive valuescan be used to evaluate how well a model categorizes specific individuals. This type of measure can be especially helpful in evaluating models used for clinical decision-making. However, even with good sensitivity and specificity, the positive predictive value may be low, especially for rare diseases.
Considerations of Evaluation of Risk Models by Application
Dr. Ruth Pfeiffer (NCI) discussed general criteria for assessing the performance of risk prediction models, and she proposed that criteria that were based on a specific loss function be used for screening and intervention applications. Using these particular criteria can help investigators evaluate their beneficial as well as adverse effects. She found that for some applications, such as screening, discriminatory power was much more important than for other applications, such as preventive interventions. She pointed out that the usefulness of general criteria, such as concordance, depended on the application and that the use of specific loss functions could lead to more appropriate model assessments.
Validation and Use of Risk Models
Dr. Dan McGee (Florida State University) emphasized that a model should be validated on the basis of its use. These uses include creating clinical risk groups for stratification, informing patients and their families about the state of the patient's health, and helping patients make treatment and other decisions. Model "usefulness" is determined by how well a model works in practice, so that a model should be validated for its specific use.
Comparing New and Established Risk Models
Dr. Michael Kattan (Memorial Sloan-Kettering Cancer Center) discussed the need to determine whether a new model being developed was "better" at risk prediction than were clinically established models. He explained that a metric was required to compare the predictive accuracy of the existing against the new models and listed several desirable characteristics of such an error measure. The measure should be understandable and interpretable to persuade physicians that the new model is superior to current risk prediction methods. It also should be amenable to improvement, be parameter free, and be unaffected by censoring. As an error measure, he favors the concordance indexthe probability that, given two randomly drawn patients, the patient for whom treatment fails first had a higher probability of treatment failure.
Statistical Measures for Determining Risk Model Error
Dr. Martin Schumacher (Freiburg University, Freiburg, Germany) focused on the Brier score (the mean-squared error of prediction when predictions are made in terms of event or event-free probabilities). The Brier score can be adapted for competing risks and for updating dynamic predictions. He showed how to estimate the Brier score nonparametrically for survival outcomes in the presence of right censoring by a weighted residual sum of squares. These prediction error methods are valuable for detecting over-fitting, and they yield R2 (i.e., explained variation measures) for checking the explanatory power of prediction models. He illustrated the survival outcomes by applying it to data from the German Breast Cancer Study Group (54). In addition, he used published aggregate data to show that the prediction error of current breast cancer prediction models was only marginally smaller than that of a constant prediction that ignores the information on risk factors.
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IMPLICATIONS FOR POLICY AND PREVENTION |
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The importance of this issue was highlighted by many participants, who commented during the course of the workshop that, despite current knowledge of risk and protective factors for chronic disease as well as for certain cancers (e.g., smoking cessation, diet, exercise, and screening), a large proportion of the population did not adhere to current cancer control recommendations. Some participants noted that more intensive approaches to cancer prevention may be appropriate in certain circumstances, such as for individuals who are at high risk of cancer. Moreover, certain interventions (e.g., tamoxifen for breast cancer prevention) do not lend themselves to population-level strategies and should be reserved for selected groups. Many workshop participants thought that both high-risk individual and population-level approaches were needed to achieve cancer prevention goals.
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HIGHLIGHTS OF SELECTED ABSOLUTE AND GENETIC SUSCEPTIBILITY RISK ASSESSMENT MODELS |
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The Experience of Coronary Heart Disease Risk Prediction Models
Dr. Lisa Sullivan (Boston University Statistics and Consulting Unit, Framingham Heart Study) outlined the experience of cardiovascular risk prediction, a field that is further developed than cancer risk prediction, and discussed the wide clinical use of these risk prediction models. She explained that the Framingham Study used readily available risk factors, including blood pressure, serum lipids, age, sex, smoking status, and diabetes status, and had developed several models by use of different risk populations and outcomes. Although the Framingham Study primarily follows Caucasian middle-class participants, study investigators recalibrated its coronary heart disease risk prediction model, and this recalibrated model is now valid in other ethnic cohorts and has reasonably good discriminatory power (55).
The Framingham models can be used by physicians and their patients to make decisions about lifestyle changes or pharmacologic interventions that may reduce coronary heart disease risk and to assess changes in risk over time (48). This risk assessment system has been translated into a simple scoring system that assigns integer points to each risk factor category. This scoring system allows physicians and patients to easily compute risk estimates and to then match the nature and intensity of treatment (e.g., lowering cholesterol) to the absolute risk of coronary heart disease. The Framingham models do not generally discriminate between coronary heart disease and coronary death and do not correct for deaths from competing diseases. Framingham investigators are currently adding confidence intervals and risk factors (such as nutrition, exercise, and family history) to the model.
Breast Cancer Risk Assessment Model Incorporating Genetic and Epidemiologic Factors
Dr. Jack Cuzick (Imperial Cancer Research Fund) explained that, because no single data set combined hormonal, reproductive, and genetic risk characteristics, his research team had used various published data sets to develop one comprehensive breast cancer risk prediction model (22). The model first calculates the likelihood of a woman carrying mutations in the highly penetrant genetic susceptibility genes BRCA1 and BRCA2, as well as hypothetical low-penetrance genes that are so far unidentified. Logistic regression analysis is then performed to modify risk for environmental, reproductive, and hormonal factors, such as weight, height, ages at menopause and menarche, parity, and hormone use. These factors are incorporated into the model to estimate a woman's individual's absolute risk of developing breast cancer over a 10-year period, as well as over her lifetime. Work is under way to incorporate mammographic density. Estrogen receptor status, which has implications for use of chemopreventive agents such as tamoxifen, is another important future component of the model, although the factors that specifically affect estrogen receptor-positive breast cancer risk are still unclear.
Breast and Ovarian Cancer Genetic Susceptibility Risk Model
Dr. Antonis Antoniou (Cambridge University) discussed the latest developments of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation (BOADICEA) susceptibility model (29,56). This model was initially developed by use of segregation analysis of a population-based series of 1484 breast cancer patients and 156 multiple-case families from the United Kingdom. The model has now been updated with additional data from two UK populationbased studies of breast cancer (57,58) and family data from the BRCA1 and/or BRCA2 carriers identified in 22 population-based studies of breast and ovarian cancer (59). The combined data set included 2785 families, among whom 301 segregated BRCA1 mutations and 236 segregated BRCA2 mutations. According to the model, susceptibility to breast cancer is explained by mutations in BRCA1 and BRCA2 plus a polygenic effect. The latest version includes a birth cohort effect on cancer risks and smoothed cancer incidence rates.
Dr. Antoniou noted that this model was intended to serve as the basis of a risk assessment package that could predict gene carrier status and absolute breast cancer risk. Future work will focus on incorporating new genetic and environmental factors, and validating the model in external data sets.
Hereditary Nonpolyposis Colorectal Cancer (HNPCC) Genetic Susceptibility Risk Model
Dr. Chris Amos (M.D. Anderson Cancer Center) described the genetic basis and epidemiology of HNPCC and the difficulty of studying this syndrome because it encompassed a broad spectrum of different cancers in addition to colorectal cancer. He discussed the substantial variation in the age of onset of colorectal cancer in HNPCC families and noted that this variation could not be explained by the genotype alone, suggesting that additional unknown genetic and environmental factors must be involved in determining the age of onset. He stressed the importance and need for the development of genetic susceptibility risk models for HNPCC.
Dr. Giovanni Parmigiani (Johns Hopkins University) described how family history could be used to develop cancer genetic susceptibility risk models for high-risk individuals. These models can provide valuable information about the presence of a mutation in a breast cancer susceptibility gene and can be used in counseling patients and estimating the likelihood of developing cancer. He also discussed empirical and Mendelian modeling. The former evaluates the probability of a positive genetic test and determines associations between genetic testing results and features of family history. The latter evaluates the probability of a deleterious mutation at a cancer susceptibility gene and derives carrier probabilities from genetic parameters. He also described the open-source BayesMendel software used in his group for Mendelian risk prediction.
In an extension of their work on breast cancer susceptibility, Dr. Parmigiani noted that he and his colleagues were currently developing a statistical model with associated software named CRCAPRO. The model uses family history of colorectal and endometrial cancer to assess the probability that an individual carries a mutation of the MLH1 and MSH2 genes (60). Like the breast cancer model, CRCAPRO uses a Mendelian approach that assumes autosomal dominant inheritance. Age-dependent penetrance and prevalences are based on a systematic review of the literature.
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FUTURE RESEARCH |
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Details of these recommendations are summarized in Table 6.
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NOTES |
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REFERENCES |
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(1) Statistical Research and Application Branch, Cancer Control and Population Sciences, National Cancer Institute. Available at: http://srab.cancer.gov/devcan/. [Last accessed: December 1, 2004.]
(2) Memorial Sloan-Kettering Cancer Center. Available at: http://www.mskcc.org/. [Last accessed: December 1, 2004.]
(3) University of Texas Southwestern Medical Center at Dallas. Available at: http://www3.utsouthwestern.edu/cancergene/. [Last accessed: December 1, 2004.]
(4) Breast Cancer Risk Assessment Tool. National Cancer Institute. Available at: http://bcra.nci.nih.gov/. [Last accessed: December 1, 2004.]
(5) Your Disease Risk. The Source on Prevention. The Harvard Center for Cancer Prevention. Available at: http://www.yourdiseaserisk.harvard.edu/hccpquiz.pl?func=show&page=cancer_index/. [Last accessed: December 1, 2004.]
(6) Vogel VG, Bevers T. Handbook of breast cancer risk-assessment: evidence-based guidelines for evaluation, prevention, counseling, and treatment. Boston (MA): Jones & Bartlett Publishers; 2003.
(7) Colditz GA. Handbook of cancer risk assessment and prevention. Boston (MA): Jones & Bartlett Publishers; 2004.
(8) International Society of Cancer Risk Assessment and Management (ISC-RAM). Available at: http://www.isc-ram.org/index.html. [Last accessed: December 1, 2004.]
(9) GenovationsTM predictive genomics for personalized medicine. Available at: http://www.genovations.com. [Last accessed: December 1, 2004.]
(10) IntergeneticsTM Incorporated. Available at: http://www.intergenetics.com/intergenetics/index.html. [Last accessed: December 1, 2004.]
(11) Sciona. Science-based tools for personalized product design. Available at: http:www.sciona.com/coresite/index.asp?p=1. [Last accessed: December 1, 2004.]
(12) National Cancer Institute. The nation's investment in cancer research. A plan and budget proposal for the fiscal year 2006. Available at: http://plan.cancer.gov/. [Last accessed: February 22, 2005.]
(13) Ottman R, Pike MC, King MC, Henderson BE. Practical guide for estimating risk for familial breast cancer. Lancet 1983;ii:5568.[CrossRef]
(14) Anderson DE, Badzioch MD. Risk of familial breast cancer. Cancer 1985;56:3837.[ISI][Medline]
(15) Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989;81:187986.[Abstract]
(16) Taplin SH, Thompson RS, Schnitzer F, Anderman C, Immanuel V. Revisions in the risk-based Breast Cancer Screening Program at Group Health Cooperative. Cancer 1990;66:8128.[ISI][Medline]
(17) Claus EB, Risch N, Thompson WD. The calculation of breast cancer risk for women with a first degree family history of ovarian cancer. Breast Cancer Res Treat 1993;28:11520.[CrossRef][ISI][Medline]
(18) Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer 1994;73:64351.[ISI][Medline]
(19) Rosner B, Colditz GA. Nurses' health study: log-incidence mathematical model of breast cancer incidence. J Natl Cancer Inst 1996;88:35964.
(20) Colditz GA, Rosner B. Cumulative risk of breast cancer to age 70 years according to risk factor status: data from the Nurses' Health Study. Am J Epidemiol 2000;152:95064.
(21) Ueda K, Tsukuma H, Tanaka H, Ajiki W, Oshima A. Estimation of individualized probabilities of developing breast cancer for Japanese women. Breast Cancer 2003;10:5462.[Medline]
(22) Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004;23:111130.[CrossRef][ISI][Medline]
(23) Couch FJ, DeShano ML, Blackwood MA, Calzone K, Stopfer J, Campeau L, et al. BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. N Engl J Med 1997;336:140915.
(24) Shattuck-Eidens D, Oliphant A, McClure M, McBride C, Gupte J, Rubano T, et al. BRCA1 sequence analysis in women at high risk for susceptibility mutations. Risk factor analysis and implications for genetic testing. JAMA 1997;278:124250.[Abstract]
(25) Parmigiani G, Berry D, Aguilar O. Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. Am J Hum Genet 1998;62:14558.[CrossRef][ISI][Medline]
(26) Berry DA, Iversen ES Jr, Gudbjartsson DF, Hiller EH, Garber JE, Peshkin BN, et al. BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. J Clin Oncol 2002;20:270112.
(27) Frank TS, Manley SA, Olopade OI, Cummings S, Garber JE, Bernhardt B, et al. Sequence analysis of BRCA1 and BRCA2: correlation of mutations with family history and ovarian cancer risk. J Clin Oncol 1998;16:241725.
(28) Frank TS, Deffenbaugh AM, Reid JE, Hulick M, Ward BE, Lingenfelter B, et al. Clinical characteristics of individuals with germline mutations in BRCA1 and BRCA2: analysis of 10 000 individuals. J Clin Oncol 2002;20:148090.
(29) Antoniou AC, Pharoah PD, McMullan G, Day NE, Stratton MR, Peto J, et al. A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. Br J Cancer 2002;86:7683.[CrossRef][ISI][Medline]
(30) de la Hoya M, Osorio A, Godino J, Sulleiro S, Tosar A, Perez-Segura P, et al. Association between BRCA1 and BRCA2 mutations and cancer phenotype in Spanish breast/ovarian cancer families: implications for genetic testing. Int J Cancer 2002;97:46671.[CrossRef][ISI][Medline]
(31) Vahteristo P, Eerola H, Tamminen A, Blomqvist C, Nevanlinna H. A probability model for predicting BRCA1 and BRCA2 mutations in breast and breast-ovarian cancer families. Br J Cancer 2001;84:7048.[CrossRef][ISI][Medline]
(32) Hartge P, Struewing JP, Wacholder S, Brody LC, Tucker MA. The prevalence of common BRCA1 and BRCA2 mutations among Ashkenazi Jews. Am J Hum Genet 1999;64:96370.[CrossRef][ISI][Medline]
(33) Apicella C, Andrews L, Hodgson SV, Fisher SA, Lewis CM, Solomon E, et al. Log odds of carrying an ancestral mutation in BRCA1 or BRCA2 for a defined personal and family history in an Ashkenazi Jewish woman (LAMBDA). Breast Cancer Res 2003;5:R20616.[CrossRef][ISI][Medline]
(34) Jonker MA, Jacobi CE, Hoogendoorn WE, Nagelkerke NJ, de Bock GH, van Houwelingen JC. Modeling familial clustered breast cancer using published data. Cancer Epidemiol Biomarkers Prev 2003;12:147985.
(35) Gilpin CA, Carson N, Hunter AG. A preliminary validation of a family history assessment form to select women at risk for breast or ovarian cancer for referral to a genetics center. Clin Genet 2000;58:299308.[CrossRef][ISI][Medline]
(36) Fisher TJ, Kirk J, Hopper JL, Godding R, Burgemeister FC. A simple tool for identifying unaffected women at a moderately increased or potentially high risk of breast cancer based on their family history. Breast 2003;12:1207.[CrossRef][ISI][Medline]
(37) Selvachandran SN, Hodder RJ, Ballal MS, Jones P, Cade D. Prediction of colorectal cancer by a patient consultation questionnaire and scoring system: a prospective study. Lancet 2002;360:27883.[CrossRef][ISI][Medline]
(38) Imperiale TF, Wagner DR, Lin CY, Larkin GN, Rogge JD, Ransohoff DF. Using risk for advanced proximal colonic neoplasia to tailor endoscopic screening for colorectal cancer. Ann Intern Med 2003;139:95965.
(39) Wijnen JT, Vasen HF, Khan PM, Zwinderman AH, van der KH, Mulder A, et al. Clinical findings with implications for genetic testing in families with clustering of colorectal cancer. N Engl J Med 1998;339:5118.
(40) Ohori M, Swindle P. Nomograms and instruments for the initial prostate evaluation: the ability to estimate the likelihood of identifying prostate cancer. Semin Urol Oncol 2002;20:11622.[CrossRef][Medline]
(41) Bruner DW, Baffoe-Bonnie A, Miller S, Diefenbach M, Tricoli JV, Daly M, et al. Prostate cancer risk assessment program. A model for the early detection of prostate cancer. Oncology (Huntingt) 1999;13:32534.[Medline]
(42) Eastham JA, May R, Robertson JL, Sartor O, Kattan MW. Development of a nomogram that predicts the probability of a positive prostate biopsy in men with an abnormal digital rectal examination and a prostate-specific antigen between 0 and 4 ng/mL. Urology 1999;54:70913.[CrossRef][ISI][Medline]
(43) Optenberg SA, Clark JY, Brawer MK, Thompson IM, Stein CR, Friedrichs P. Development of a decision-making tool to predict risk of prostate cancer: the Cancer of the Prostate Risk Index (CAPRI) test. Urology 1997;50:66572.[CrossRef][ISI][Medline]
(44) Bach PB, Kattan MW, Thornquist MD, Kris MG, Tate RC, Barnett MJ, et al. Variations in lung cancer risk among smokers. J Natl Cancer Inst 2003;95:4708.
(45) Hartge P, Whittemore AS, Itnyre J, McGowan L, Cramer D. Rates and risks of ovarian cancer in subgroups of white women in the United States. The Collaborative Ovarian Cancer Group. Obstet Gynecol 1994;84:760764.[Abstract]
(46) Colditz GA, Atwood KA, Emmons K, Monson RR, Willett WC, Trichopoulos D, Hunter DJ. Harvard report on cancer prevention volume 4: Harvard cancer risk index. Risk Working Group, Harvard Center for Cancer Prevention. Cancer Causes and Control, 2000 Jul;11(6):47788.[CrossRef][ISI][Medline]
(47) Kannel WB, McGee D, Gordon T. A general cardiovascular risk profile: the Framingham Study. Am J Cardiol 1976;38:4651.[CrossRef][ISI][Medline]
(48) Grundy SM, Balady GJ, Criqui MH, Fletcher G, Greenland P, Hiratzka LF, et al. Primary prevention of coronary heart disease: guidance from Framingham: a statement for healthcare professionals from the AHA Task Force on Risk Reduction. American Heart Association. Circulation 1998;97:187687.
(49) Costantino JP, Gail MH, Pee D, Anderson S, Redmond CK, Benichou J, et al. Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst 1999;91:15418.
(50) Fisher B, Costantino JP, Wickerham DL, Redmond CK, Kavanah M, Cronin WM, et al. Tamoxifen for prevention of breast cancer: report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study. J Natl Cancer Inst 1998;90:137188.
(51) Gail MH, Costantino JP, Bryant J, Croyle R, Freedman L, Helzlsouer K, et al. Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst 1999;91:182946.
(52) Freedman AN, Graubard BI, Rao SR, McCaskill-Stevens W, Ballard-Barbash R, Gail MH. Estimates of the number of US women who could benefit from tamoxifen for breast cancer chemoprevention. J Natl Cancer Inst 2003;95:52632.
(53) Cancer Intervention and Surveillance Modeling Network. National Cancer Institute. Available at: http://cisnet.cancer.gov/about/. [Last accessed: December 1, 2004.]
(54) Graf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Stat Med 1999;18:252945.[CrossRef][ISI][Medline]
(55) D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 2001;286:1807.
(56) Antoniou AC, Pharoah PP, Smith P, Easton DF. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer 2004;91:158090.[ISI][Medline]
(57) Peto J, Collins N, Barfoot R, Seal S, Warren W, Rahman N, et al. Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J Natl Cancer Inst 1999;91:9439.
(58) Lalloo F, Varley J, Ellis D, Moran A, O'Dair L, Pharoah P, et al. Prediction of pathogenic mutations in patients with early-onset breast cancer by family history. Lancet 2003;361:11012.[CrossRef][ISI][Medline]
(59) Antoniou A, Pharoah PD, Narod S, Risch HA, Eyfjord JE, Hopper JL, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet 2003;72:111730.[CrossRef][ISI][Medline]
(60) Bayes Mendel Laboratory. Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University. Available at: http://astor.som.jhmi.edu/BayesMendel/crcapro.html/. [Last accessed: December 1, 2004.]
Manuscript received December 9, 2004; revised March 3, 2005; accepted March 7, 2005.
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