Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application.
Ibrahim, Asmaa Jahanifar, Mostafa Wahab, Noorul Toss, Michael S Makhlouf, Shorouk Atallah, Nehal Lashen, Ayat G Katayama, Ayaka Graham, Simon Bilal, Mohsin
Ibrahim, Asmaa
Jahanifar, Mostafa
Wahab, Noorul
Toss, Michael S
Makhlouf, Shorouk
Atallah, Nehal
Lashen, Ayat G
Katayama, Ayaka
Graham, Simon
Bilal, Mohsin
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Affiliation
Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust
Other Contributors
Publication date
2023-12-27
Subject
Research Projects
Organizational Units
Journal Issue
Abstract
In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.
Citation
Mod Pathol. 2024 Mar;37(3):100416
Type
Article