ArticleCancer management and research2019
Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study.
Article in Cancer management and research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- A systematic review of prediction models for risk of breast cancer.BMC cancer · 2025Pooled it
- Risk Assessment of Repeated Suicide Attempts Among Youth in Saudi Arabia.Risk management and healthcare policy · 2020Article
- Modelling risk assessment for cervical cancer in symptomatic Saudi women.Saudi medical journal · 2019Article
- Accuracy of Abbreviated Breast MRI in Diagnosing Breast Cancer in Women with Dense Breasts Compared with Standard Imaging Modalities.Saudi journal of medicine & medical sciencesArticle
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite 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.
methodsA 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.
resultsA 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,
conclusionThe 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.
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