Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study
Received 5 October 2018
Accepted for publication 1 January 2019
Published 4 February 2019 Volume 2019:11 Pages 1125—1132
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Dr Kenan Onel
Anwar E Ahmed,1,2 Donna K McClish,3 Thamer Alghamdi,4 Abdulmajeed Alshehri,4 Yasser Aljahdali,4 Khalid Aburayah,4 Abdulrahman Almaymoni,4 Monirah Albaijan,1 Hamdan Al-Jahdali,1,4–6 Abdul Rahman Jazieh4–6
1King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia; 2College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 3Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA; 4College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 5King Abdulaziz Medical City, Riyadh, Saudi Arabia; 6Ministry of the National Guard - Health Affairs, Riyadh, Saudi Arabia
Background: Despite the continuing increase in the breast cancer incidence rate among Saudi Arabian women, no breast cancer risk-prediction model is available in this population. The aim of this research was to develop a risk-assessment tool to distinguish between high risk and low risk of breast cancer in a sample of Saudi women who were screened for breast cancer.
Methods: A retrospective chart review was conducted on symptomatic women who underwent breast mass biopsies between September 8, 2015 and November 8, 2017 at King Abdulaziz Medical City, Riyadh, Saudi Arabia.
Results: A total of 404 (63.8%) malignant breast biopsies and 229 (36.2%) benign breast biopsies were analyzed. Women ≥40 years old (aOR: 6.202, CI 3.497–11.001, P=0.001), hormone-replacement therapy (aOR 24.365, 95% CI 8.606–68.987, P=0.001), postmenopausal (aOR 3.058, 95% CI 1.861–5.024, P=0.001), and with a family history of breast cancer (aOR 2.307, 95% CI 1.142–4.658, P=0.020) were independently associated with an increased risk of breast cancer. This model showed an acceptable fit and had area under the receiver-operating characteristic curve of 0.877 (95% CI 0.851–0.903), with optimism-corrected area under the curve of 0.865.
Conclusion: The prediction model developed in this study has a high ability in predicting increased breast cancer risk in our facility. Combining information on age, use of hormone therapy, postmenopausal status, and family history of breast cancer improved the degree of discriminatory accuracy of breast cancer prediction. Our risk model may assist in initiating population-screening programs and prompt clinical decision making to manage cases and prevent unfavorable outcomes.
Keywords: breast cancer management, risk assessment, modeling, patient stratification, predictive tool
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