Healthcare AI
A framework for a national cancer imaging repository in Nigeria
Cancer is a growing global health concern responsible for approximately 10 million deaths annually, with low- and middle-income countries (LMICs) accounting for over 70% of cancer-related mortality. Nigeria, the most populous country in Africa, bears the highest incidence and mortality burden. Although Artificial Intelligence (AI) holds promise for improving early detection and treatment, its success depends on access to large, high-quality imaging datasets. Nigeria lacks a centralized repository for cancer imaging data to support AI development. This study proposes a framework for establishing a national cancer imaging repository to facilitate data-driven cancer research and AI model development in Nigeria, using twelve (12) tertiary healthcare institutions across the six geo-political zones as pilot implementation centres.
Executive Impact: Why This Matters
Understanding the scale of the challenge and the potential for AI-driven solutions in a low-resource setting like Nigeria.
Annual Cancer Deaths Globally
Cancer Mortality in LMICs
Pilot Healthcare Institutions
AI Model Accuracy Potential
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This section explores the pivotal role of Artificial Intelligence in revolutionizing healthcare, particularly in early cancer detection and treatment strategies. Discover how AI models, when trained on high-quality datasets, can significantly improve diagnostic accuracy and personalize patient care.
Dive into the complexities of building and maintaining robust data infrastructure for medical imaging. This segment highlights the architectural considerations, data governance, and interoperability standards crucial for scalable and secure repositories.
Understand the strategic implications of establishing national-level health initiatives. This part focuses on the policy frameworks, stakeholder engagements, and long-term sustainability plans required to integrate advanced medical technologies into public health systems.
Enterprise Process Flow: Data Harmonization
Normalization of acquisition parameters
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Metadata mapping to standardized schema
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Semantic encoding of imaging data
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Vendor-specific tag normalization
| Feature/Criteria | The Cancer Imaging Archive (TCIA) | Proposed Nigerian Imaging Repository |
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| Primary Storage Platform |
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| Imaging Modalities Supported |
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| Anonymization Protocols |
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70%
Projected Increase in Cancer Cases in Africa by 2030
Addressing Nigeria's Data Infrastructure Gap
Nigeria lacks a large-scale image repository that could be used to train advanced AI models and develop innovative solutions tailored to the specific needs of Nigerian patients. Most Nigerian researchers interested in data-driven medical research, therefore, depend on online repositories consisting of data from non-Nigerians, and this can lead to misdiagnosis when used for Nigerian patients, as existing studies indicate that tumour biology, breast density, and cancer presentation differ across ethnic groups, with African populations often having distinct tumour characteristics compared to Caucasian counterparts. The proposed framework aims to bridge this critical gap.
Implementation Roadmap
A phased strategy for deploying the National Cancer Imaging Repository (NCIR).
Pilot Site Selection
Twelve tertiary healthcare institutions were selected based on oncology service capacity, geographic coverage, operational readiness, and participation in national cancer initiatives. Formal data-sharing agreements will be established through Memoranda of Understanding.
Infrastructure Deployment
Each site will deploy PACS integration tools, anonymization and annotation software, and secure data transfer services. Centralized cloud infrastructure will provide scalable storage, processing capacity, and GPU resources to support data curation and future AI workflows.
Training and Capacity Building
Structured training programs will equip radiologists, data managers, and IT personnel with skills in PACS integration, anonymization, annotation, and ethical compliance. Continuous professional development will support long-term sustainability.
National Scale-up
Following pilot evaluation, additional institutions will be onboarded using standardized deployment protocols. Ongoing stakeholder engagement and feedback-driven optimization will guide expansion toward a sustainable national imaging repository.
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