Related Experiment Video
Updated: Oct 14, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Determinants of late detection and advanced-stage diagnosis of breast cancer in Nigeria
Olayide Agodirin1,2, Samuel Olatoke1, Ganiyu Rahman1,3
1Department of Surgery, University of Ilorin and University of Ilorin Teaching Hospital, Ilorin, Nigeria.
Objective:
To describe the risk factors for late detection and advanced-stage diagnosis among patients who detected their BC early.
Method:
Using secondary data, we analyzed the impact of socio-demographic factors, premorbid experience, BC knowledge, and health-seeking pattern on the risk of late detection and advanced-stage diagnosis after early BC detection. Test of statistical significance in SPSS and EasyR was set at 5% using Sign-test, chi-square tests (of independence and goodness of fit), odds ratio, or risk ratio as appropriate.
Result:
Most socio-demographic factors did not affect detection size or risk of disease progression in the 405 records analyzed. High BC knowledge, p-value = 0.001, and practicing breast self-examination (BSE) increased early detection, p-value = 0.04, with a higher probability (OR 1.6 (95% CI 1.1-2.5) of detecting <2cm lesions. Visiting alternative care (RR 1.5(95% CI 1.2-1.9), low BC knowledge (RR 1.3(95% CI 1.1-1.9), and registering concerns for hospital care increased the risk of advanced-stage diagnosis after early detection (64% (95% CI 55-72)). Adhering to the monthly BSE schedule reduced the risk of advanced-stage diagnosis by -25% (95% CI -49, -1.1) in the presence of socioeconomic barriers.
Conclusion:
Strategies to increase BC knowledge and BSE may help BC downstaging, especially among women with common barriers to early diagnosis.
More Related Videos
13:44Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Related Concept Videos
Cancer Survival Analysis
Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...