TY - JOUR
T1 - Modeling risk assessment for breast cancer in symptomatic women
T2 - A Saudi Arabian study
AU - Ahmed, Anwar E.
AU - McClish, Donna K.
AU - Alghamdi, Thamer
AU - Alshehri, Abdulmajeed
AU - Aljahdali, Yasser
AU - Aburayah, Khalid
AU - Almaymoni, Abdulrahman
AU - Albaijan, Monirah
AU - Al-Jahdali, Hamdan
AU - Jazieh, Abdul Rahman
N1 - Publisher Copyright:
© 2019 Ahmed et al.
PY - 2019
Y1 - 2019
N2 - 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), hormonereplacement 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.
AB - 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), hormonereplacement 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.
KW - Breast cancer management
KW - Modeling
KW - Patient stratification
KW - Predictive tool
KW - Risk assessment
UR - http://www.scopus.com/inward/record.url?scp=85063488047&partnerID=8YFLogxK
U2 - 10.2147/CMAR.S189883
DO - 10.2147/CMAR.S189883
M3 - Article
AN - SCOPUS:85063488047
SN - 1179-1322
VL - 11
SP - 1125
EP - 1132
JO - Cancer Management and Research
JF - Cancer Management and Research
ER -