README.md

---
license: cc-by-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- cancer
- oncology
- synthetic
- healthcare
- sub-saharan-africa
- pathology
- histopathology
- diagnosis
pretty_name: Cancer Pathology Histopathology Africa
size_categories:
- 10K<n<100K
configs:
- config_name: low_burden
  data_files: cancer_pathology_histopathology_low_burden.csv
- config_name: moderate_burden
  data_files: cancer_pathology_histopathology_moderate_burden.csv
  default: true
- config_name: high_burden
  data_files: cancer_pathology_histopathology_high_burden.csv
data_type: synthetic
---

> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

# Cancer Pathology & Histopathology Africa

## Abstract

This synthetic dataset represents cancer pathology and histopathology data across sub-Saharan Africa, capturing diagnostic information, IHC marker availability, and turnaround times. The dataset contains 3,600-5,400 records per scenario with IHC marker availability varying by facility type (35-92%).

## 1. Introduction

### 1.1 Problem Statement
Pathology services are essential for cancer diagnosis and treatment planning, yet access varies dramatically across sub-Saharan Africa. National referral centers may have comprehensive IHC capabilities, while district facilities often lack basic immunohistochemistry. Turnaround times can be weeks in resource-limited settings.

### 1.2 Purpose
This dataset supports:
- Pathology service capacity assessment
- Diagnostic quality improvement initiatives
- Resource planning for pathology services
- Research on diagnostic delays

## 2. Methodology

### 2.1 Target Population
- **Geographic scope**: Sub-Saharan Africa
- **Population represented**: Cancer pathology specimens
- **Time period**: 2018-2025

### 2.2 Key Parameters
- IHC availability: National referral (92%), Regional (72%), District (35%)
- Turnaround time: 3-28 days
- Diagnosis confirmed: 85%
- Grade distribution: Well (12%), Moderate (35%), Poor (38%), Undifferentiated (15%)

### 2.3 Scenario Design

| Scenario | Description | Records |
|----------|-------------|---------|
| low_burden | Higher resource setting | 3,600 |
| moderate_burden | Standard setting | 4,500 |
| high_burden | Lower resource setting | 5,400 |

## 3. Dataset Description

### 3.1 Key Variables
- pathology_id, country, year, facility_type
- cancer_type, icd_o_morphology_code, morphology_description
- grade, lymphovascular_invasion, perineural_invasion
- margin_status, ihc_er, ihc_pr, ihc_her2, ihc_ki67_index
- molecular_subtype (breast), specimen_type
- diagnosis_confirmed, turnaround_time_days

### 3.2 Morphology Codes (ICD-O)
| Code | Description | Frequency |
|------|-------------|-----------|
| 8140/3 | Adenocarcinoma | 35% |
| 8070/3 | Squamous cell carcinoma | 25% |
| 8500/3 | Ductal carcinoma | 15% |
| 8260/3 | Papillary carcinoma | 8% |

## 4. Data Sources

- WHO Classification of Tumours (IARC)
- African pathology registry data
- College of American Pathologists guidelines
- Peer-reviewed literature on pathology in LMICs

## 5. Use Cases

- Pathology service capacity assessment
- IHC availability analysis
- Diagnostic turnaround time research
- Quality improvement initiatives

## 6. Citation

```bibtex
@dataset{cancer_pathology_histopathology_africa_2025,
  title={Cancer Pathology Histopathology Africa},
  author={Electric Sheep Africa},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/electricsheepafrica/cancer-pathology-histopathology-africa}
}
```

## 7. License

Creative Commons Attribution 4.0 (CC-BY-4.0)