Abstract

Abstract Background There have been no sustained efforts at stemming the rising incidence and mortality from breast cancer in sub-Saharan African countries like Nigeria. Mammography is the gold standard for breast cancer screening and is associated with improved survival particularly among women 50 to 69. Reports from various parts of Nigeria show very low mammography screening uptake. Causes include lack of geographical access, financial issues (less than 5% of the population is covered by the National Health Insurance Scheme), lack of awareness and the increasing expertise gap from the continued brain drain, despite the already low number of radiologists in the country. Given these obstacles, our long-term goal is to devise a rational basis for identifying mammographic risk categories so that this resource can be deployed cost-effectively. We propose to carry out a 5-year prospective mammographic study in Nigerian women and to use machine learning to develop a risk prediction algorithm, so that following a single screening mammogram, the appropriate frequency of subsequent mammograms can be determined. Our immediate goal is to examine the feasibility of performing such a study. To this end, we have initiated a cross-sectional pilot study that will recruit up to 2000 women in multiple urban centers in Nigeria. Objective: To explore feasibility of creating a digital mammography image database representative of the radiological features of the breast of Nigerian women. Objective: To explore feasibility of creating a database that can be used to inform a predictive mammographic risk model for breast cancer and identify the appropriate population that will benefit from breast cancer screening in Nigeria. Design This is a prospective, open-label, cross-sectional pilot study in 2000 eligible women in Nigeria and will include a single screening mammography examination. The mammography images will be read by the study radiologist. The Pilot phase will determine the feasibility and aid in the design of the of the Extension phase in which up to 20,000 participants will undergo yearly mammography examination for 5 years. Incident cases of breast cancer during the study period will permit the development of deep learning algorithms for predicting the risk of breast cancer in this population. Main Eligibility Criteria: Inclusion Criteria All consecutive women aged ≥40 years and < 65 years presenting for screening mammography at the participating institutions will be eligible. Signed study-specific written informed consent. Exclusion Criteria Complaint of a focal dominant lump or a bloody or clear nipple discharge. History of breast implants. Any woman who is pregnant or has reason to believe that she might be pregnant. Statistical Methods Analyses of continuous data will be summarized using descriptive statistics where the following parameters will be reported: Number of observations, Number of missing observations, Mean, Median, Standard deviation (SD), Minimum (Min), Maximum (Max) Categorical data will be presented with absolute and relative frequency (n and %). Present Accrual and Target accrual Two hundred ninety-six women have been enrolled and screening digital mammographic images using GE Digital mammography equipment have been obtained on all participants at 2 study sites in 2 cities in Southern Nigeria. The target accrual is 2000 participants over 12 months. Conclusion Our preliminary experience to date suggests the feasibility of proceeding with a larger prospective study to define mammographic risk groups in Nigerian women which will permit the rational allocation of resources for breast cancer prevention activities in this underserved population. Contact Information: Dr Toyin Shonukan at tshonukan@yemanjacancercare.org Citation Format: Oluwatoyin Shonukan, Hyelakumi Ibrahim, Esther Udoh, Wahab Egbeolu, Kanyinsola Oyeyinka, Jonas Haggstrom. A PROSPECTIVE PILOT STUDY OF MAMMOGRAPHIC PREDICTION OF WOMEN’S RISK (M-POWER) IN NIGERIA [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-20-01.

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