Dataset Information

Risk Models for Breast Cancer and Their Validation.

ABSTRACT: Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer-Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132,139 women attending mammography screening in the State of Washington, USA.

SUBMITTER: Brentnall AR

PROVIDER: S-EPMC7100774 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Risk Models for Breast Cancer and Their Validation.

Brentnall Adam R AR Cuzick Jack J

Statistical science : a review journal of the Institute of Mathematical Statistics 20200301 1

Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint ...[more]

PMID: 32226220

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Project description:PurposeThe Gail model is one of the most widely used tools to assess the risk of breast cancer. However, it is known to overestimate breast cancer risk for Asian women. Here, we validate the Gail model and the Korean model using Korean data, and subsequently update and revalidate the Korean model using recent data.MethodsWe validated the modified Gail model (model 2), Asian American Gail model, and a previous Korean model using screening patient data collected between January 1999 and July 2004. The occurrence of breast cancer was confirmed by matching the resident registration number with data from the Korean Breast Cancer Registration Program. The expected-to-observed (E/O) ratio was used to validate the reliability of the program, and receiver operating characteristics curve analysis was used to evaluate the program's discriminatory power. There has been a rapid increase in the incidence of breast cancer in Korea, and we updated and revalidated the Korean model using incidence and mortality rate data from recent years.ResultsAmong 40,229 patients who were included in the validation, 161 patients were confirmed to have developed breast cancer within 5 years of screening. The E/O ratios and 95% confidence intervals (CI) were 2.46 (2.10-2.87) for the modified Gail model and 1.29 (1.11-1.51) for the Asian American Gail model. The E/O ratio and 95% CI for the Korean model was 0.50 (0.43-0.59). For the updated Korean model, the E/O ratio and 95% CI were 0.85 (0.73-1.00). In the discriminatory power, the area under curve and 95% CI of the modified Gail model, Asian American Gail model, Korean model and updated Korean model were 0.547 (0.500-0.594), 0.543 (0.495-0.590), 0.509 (0.463-0.556), and 0.558 (0.511-0.605), respectively.ConclusionThe updated Korean model shows a better performance than the other three models. It is hoped that this study can provide the basis for a clinical risk assessment program and a future prospective study of breast cancer prevention.

Project description:IntroductionNumerous prediction models have been developed to support treatment-related decisions for breast cancer patients. External validation, a prerequisite for implementation in clinical practice, has been performed for only a few models. This study aims to externally validate published clinical prediction models using population-based Dutch data.MethodsPatient-, tumor- and treatment-related data were derived from the Netherlands Cancer Registry (NCR). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), scaled Brier score, and model calibration. Net benefit across applicable risk thresholds was evaluated with decision curve analysis.ResultsAfter assessing 922 models, 87 (9%) were included for validation. Models were excluded due to an incomplete model description (n = 262 (28%)), lack of required data (n = 521 (57%)), previously validated or developed with NCR data (n = 45 (5%)), or the associated NCR sample size was insufficient (n = 7 (1%)). The included models predicted survival (33 (38%) overall, 27 (31%) breast cancer-specific, and 3 (3%) other cause-specific), locoregional recurrence (n = 7 (8%)), disease free survival (n = 7 (8%)), metastases (n = 5 (6%)), lymph node involvement (n = 3 (3%)), pathologic complete response (n = 1 (1%)), and surgical margins (n = 1 (1%)). Seven models (8%) showed poor (AUC<0.6), 39 (45%) moderate (AUC:0.6-0.7), 38 (46%) good (AUC:0.7-0.9), and 3 (3%) excellent (AUC≥0.9) discrimination. Using the scaled Brier score, worse performance than an uninformative model was found in 34 (39%) models.ConclusionComprehensive registry data supports broad validation of published prediction models. Model performance varies considerably in new patient populations, affirming the importance of external validation studies before applying models in clinical practice. Well performing models could be clinically useful in a Dutch setting after careful impact evaluation.

Dataset Information

Risk Models for Breast Cancer and Their Validation.

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Risk Models for Breast Cancer and Their Validation.

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