Dataset Information

Validation of risk assessment models for predicting the incidence of breast cancer in korean women.

ABSTRACT: PURPOSE:The 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. METHODS:We 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. RESULTS:Among 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. CONCLUSION:The 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.

SUBMITTER: Min JW

PROVIDER: S-EPMC4197352 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Validation of risk assessment models for predicting the incidence of breast cancer in korean women.

Min Jun Won JW Chang Myung-Chul MC Lee Hae Kyung HK Hur Min Hee MH Noh Dong-Young DY Yoon Jung Han JH Jung Yongsik Y Yang Jung-Hyun JH

Journal of breast cancer 20140930 3

<h4>Purpose</h4>The 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.<h4>Methods</h4>We validated the modified Gail model (model 2), Asian American Gail model, and a previous Korean model using screening patient data collected between January 1 ...[more]

PMID: 25320620

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Project description:BACKGROUND:There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. METHODS:We used data from 40,158 women aged 50-69?years (via the lifepool cohort) participating in Australia's BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. RESULTS:Over a median of 4.3?years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O)?=?1.09, 95% confidence interval (CI) 1.00-1.18). There was good agreement across decile groups of Gail scores (?2?=?7.1, p?=?0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O?=?1.65, 95% CI 1.33-2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73-3.02, p?<?0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06-1.70, p?=?0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44-0.79, p?<?0.0001). Similar patterns were observed separately for women aged 50-59 and 60-69?years. The model's overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56-0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. CONCLUSIONS:This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50-69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.

Project description:ObjectivePredictive models for the occurrence of cancer symptoms by using machine learning (ML) algorithms could be used to aid clinical decision-making in order to enhance the quality of cancer care. This study aimed to develop and validate a selection of classification models that used ML algorithms to predict the occurrence of breast cancer-related lymphedema (BCRL) among Chinese women.MethodsThis was a retrospective cohort study of consecutive cases that had been diagnosed with breast cancer, stages I-IV. Forty-eight variables were grouped into five feature sets. Five classification models with ML algorithms were developed, and the models' performance and the variables' relative importance were assessed accordingly.ResultsOf 370 eligible female participants, 91 had BCRL (24.6%). The mean age of this study sample was 49.89 (SD = 7.45). All participants had had breast cancer surgery, and more than half of them had had a modified radical mastectomy (n = 206, 55.5%). The mean follow-up time after breast cancer surgery was 28.73 months (SD = 11.71). Most of the tumors were either stage I (n = 49, 31.2%) or stage II (n = 252, 68.1%). More than half of the sample had had postoperative chemotherapy (n = 227, 61.4%). Overall, the logistic regression model achieved the best performance in terms of accuracy (91.6%), precision (82.1%), and recall (91.4%) for BCRL. Although this study included 48 predicting variables, we found that the five models required only 22 variables to achieve predictive performance. The most important variable was the number of positive lymph nodes, followed in descending order by the BCRL occurring on the same side as the surgery, a history of sentinel lymph node biopsy, a dietary preference for meat and fried food, and an exercise frequency of less than three times per week. These factors were the most influential predictors for enhancing the ML models' performance.ConclusionsThis study found that in the ML training dataset, the multilayer perceptron model and the logistic regression model were the best discrimination models for predicting the outcome of BCRL, and the k-nearest neighbors and support vector machine models demonstrated good calibration performance in the ML validation dataset. Future research will need to use large-sample datasets to establish a more robust ML model for predicting BCRL deeply and reliably.

Dataset Information

Validation of risk assessment models for predicting the incidence of breast cancer in korean women.

Publications

Validation of risk assessment models for predicting the incidence of breast cancer in korean women.

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