Dataset Information

Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

ABSTRACT: Background: Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aimed to develop a model for absolute breast cancer risk prediction for Nigerian women.Methods: A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998-2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model.Results: The NBCS model included age, age at menarche, parity, duration of breastfeeding, family history of breast cancer, height, body mass index, benign breast diseases, and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model [area under ROC curve (AUC) = 0.703, 95% confidence interval (CI), 0.687-0.719] was better than the Black Women's Health Study (BWHS) model (AUC = 0.605; 95% CI, 0.586-0.624), Gail model for white population (AUC = 0.551; 95% CI, 0.531-0.571), and Gail model for black population (AUC = 0.545; 95% CI, 0.525-0.565). Compared with the BWHS and two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45%, and 14.19%, respectively.Conclusions: We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in Sub-Saharan Africa populations.Impact: Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high risk for breast cancer screening. Cancer Epidemiol Biomarkers Prev; 27(6); 636-43. ©2018 AACR.

SUBMITTER: Wang S

PROVIDER: S-EPMC6086588 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

Wang Shengfeng S Ogundiran Temidayo T Ademola Adeyinka A Olayiwola Oluwasola A OA Adeoye Adewunmi A Sofoluwe Adenike A Morhason-Bello Imran I Odedina Stella S Agwai Imaria I Adebamowo Clement C Obajimi Millicent M Ojengbede Oladosu O Olopade Olufunmilayo I OI Huo Dezheng D

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20180420 6

<b>Background:</b> Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aimed to develop a model for absolute breast cancer risk prediction for Nigerian women.<b>Methods:</b> A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998-2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used ...[more]

PMID: 29678902

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Project description:BackgroundsIn contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet.MethodsA large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004-2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women's Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy.ResultsDuring a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94-1.09) and 0.94 (95% CI, 0.89-0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608-0.661) and 0.585 (95% CI, 0.564-0.605) in the CKB and the SWHS, respectively.ConclusionsBased only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals' awareness and aid risk-stratified screening and prevention strategies.

Dataset Information

Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

Publications

Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

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