In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 76, No. 14_Supplement ( 2016-07-15), p. 2590-2590
Abstract:
Background: Breast cancer (BC) risk prediction models, such as the Gail model, have been developed and widely used to identify women at higher risk of having breast cancer in developed countries. However, no model exists for Black women of sub-Saharan Africa (SSA). Because African women have different risk profiles, it is of public health importance to develop a Black women-specific model. Methods: A total of 1,880 hospital-based cases and 2,166 population-based controls from the Nigerian Breast Cancer Study (NBCS, 1998∼2015) were included in the analysis. Subjects were randomly divided into the training (2/3 of the data) and validation sets (1/3 of the data), and multivariate logistic regressions were used to derive the model. Risk factors were selected based on previous NBCS findings and literature review. Calibration and discrimination performances were assessed using the observed/expected ratio (O/E) and concordance statistic (C-index), respectively. Results: The final model included age, age at menarche, parity, duration of breast feeding, family history of BC in first degree relatives, height, body mass index, benign breast diseases, oral contraceptive use, and alcohol drinking. The model performed well in the validation set with an O/E of 1.01 (95% CI: 0.93∼1.09) and C-index of 0.694 (95% CI: 0.666∼0.722). The odds ratios for developing BC by percentiles of the predicted chance of cases, relative to women in the middle quintile, showed a monotonic increasing trend. Conclusion: In Nigeria, the most populous country in SSA with 175 million people, this study developed and validated a risk prediction model for BC that is specific to Black women. It can be used to identify individuals at high risk of BC for cancer prevention. Using the population incidence rates in Nigeria, an absolute risk prediction model will be further developed. Table 1Validation analyses in both training set and validation setPercentile of predicted chance being casesTraining set (n = 2699) OR (95%CI)Validation set (n = 1347) OR (95%CI) & lt;5%0.18(0.10-0.30)0.24 (0.11-0.55)5%∼20%0.31(0.23-0.41)0.53(0.36-0.78)20%∼40%0.62(0.48-0.79)0.83(0.60-1.16)40%∼60%1.00(referent)1.00(referent)60%∼80%1.50(1.18-1.90)1.74(1.25-2.44)80%∼95%2.76(2.10-3.62)3.68(2.48-5.44)95%∼8.53(4.93-14.77)7.43(3.76-14.69)C-index0.724(0.705-0.742)0.694(0.666-0.722) Citation Format: Shengfeng Wang, Temidayo Ogundiran, Adeyinka Ademola, Oluwasola A. Olayiwola, Adewunmi Adeoye, Adenike Adeniji-Sofoluwe, Imran Morhason-Bello, Stella Odedina, Imaria Agwai, Clement Adebamowo, Millicent Obajimi, Oladosu Ojengbede, Olufunmilayo I. Olopade, Dezheng Huo. Development and validation of a breast cancer risk prediction model for black women: findings from the Nigerian breast cancer study. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2590.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2016-2590
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2016
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
