Observational studyEuropean journal of medical research2017
Developing a risk prediction model for breast cancer: a Statistical Utility to Determine Affinity of Neoplasm (SUDAN-CA Breast).
Observational study in European journal of medical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Article
- Serum vitamin D level, sun-exposed area, dietary factors, and physical activity as predictors of invasive breast cancer risk among Sudanese women: A case-control study.Journal of family medicine and primary care · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer risk prediction models are widely used in clinical settings. Although most of the well-known models were designed based on data collected from western population, yet they have been utilized for surveillance purposes in many limited-resource countries. Given the genetic variations in risk factors that exist between different races, we therefore aimed to develop and validate a tool for breast cancer risk assessment among Sudanese women.
methodsUsing cross-sectional design, 153 subjects were eligible to participate in our study. Data were collected from the only couple of tertiary centers in Sudan. They underwent multiple logistic regression using purposeful selection method to build the model. Various adjustments were made to determine significant predictors. Overall performance, calibration and discrimination were assessed by R
resultsSUDAN predictors of breast cancer were: age, menarche, family history, vegetables and fruits weekly servings, and type of cereals that traditional cuisine is made of. Both Nagelkerke R
conclusionsOur findings suggest that SUDAN provides a simple, efficient and well-calibrated tool to predict and classify women's lifetime risks of developing breast cancer. Input from our model could be deployed to guide utilization of the more advanced screening modalities in resource-limited settings to maximize cost effectiveness. Consequently, this might improve the stage at which the diagnosis is usually made.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.