TIA Centre Seminar Series: Dr. Asmaa Ibrahim

Full Title: Practical Application of Artificial Intelligence Models in Mitosis Scoring

Abstract: The implementation of AI in clinical practice needs to be critically evaluated against the existing methods. We aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). Utilizing whole slide images from a large Nottingham BC cohort (discovery n=1715, validation n=859) and TCGA-BRCA external test set (n=757), automated mitosis detection was applied using three methods: mitotic count per tumour area (MCT), mitotic index (MI), and mitotic activity index (MAI). The study compares these AI-based metrics with the Nottingham grading system (NGS), Ki67 score, clinicopathological parameters, and patient outcomes. While all three AI methods (MCT, MI, and MAI) significantly correlated with clinicopathological characteristics and patient survival, MAI and MCT show positive correlations with the gold standard visual scoring in NGS (r=0.8 and r=0.7, respectively) and Ki67 score (r=0.69 and r=0.55, respectively). MAI emerges as the sole independent predictor of survival in multivariate Cox regression analysis. The findings emphasize the need to consider the optimal AI-based scoring method for clinical applications, highlighting MAI as a reliable and reproducible approach for accurate mitotic figure quantification in BC.