Artificial Intelligence for histopathological diagnosis and grading of breast cancer in Ethiopia.

BACKGROUND: Recent advances in computational pathology enables AI-assisted diagnosis and risk stratification of breast cancer. This advance in technology will reduce the inconsistent reporting of breast cancer grading using Nottingham Histologic grading system. This study evaluated the implementation of the DeepGrade model for breast cancer grading using H&E-stained slides from breast cancer patients in Ethiopia. OBJECTIVE: To assess the accuracy, specificity, and sensitivity of the DeepGrade model in distinguishing between grade 1 and grade 3 tumours. Additionally, the study aimed to explore the model's ability to further classify Nottingham Histologic Grade 2 tumours into two distinct risk categories. METHODS: A retrospective analysis was conducted using data from 200 tumour samples from the Department of Pathology, Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia. The performance of the DeepGrade model was compared with three pathologists' diagnosis using metrics such as specificity, sensitivity, area under the curve and agreement level. RESULTS: The DeepGrade model reached a 100% specificity for low-grade tumours and an 82.05% sensitivity for high-grade tumours, with an AUC of 0.914 and an agreement level of 86.79%. Our findings illustrated the model's strong agreement with the pathologist, and a Kappa coefficient of 0.71 (95% CI: 0.51-0.90). CONCLUSION: The study showed the potential significance of DeepGrade model utilization in enhancing breast cancer grading practices in resource-limited settings. By adopting this model, a consistent and standardized grading system for breast cancer can be established, significantly enhancing the effectiveness of breast cancer management.