AI-assisted mitotic counting improves reproducibility and efficiency across multiple tumour types

2026-10-01

AI Summary

This study addresses the time-consuming nature and significant inter-observer variability of pathological mitotic counting by proposing MitPro, a human–machine collaborative framework. Leveraging deep learning-based hotspot detection and multi-center whole-slide image analysis, MitPro precisely localizes high-activity regions to assist pathologists in interpretation. By preserving physician authority over final decisions, the framework standardizes scoring criteria and reduces missed detections. Experimental results demonstrate that MitPro improves the intraclass correlation coefficient from 0.589 to 0.949 and reduces average assessment time by 151.8 seconds. Across multiple tumor types, it significantly enhances the efficiency, consistency, and reproducibility of mitotic counting.

Abstract

Mitotic counting is an important component of tumour grading, diagnosis and prognostic assessment across several tumour types, but manual assessment is time-consuming and subject to inter-pathologist variability. To help address these challenges, we developed MitPro, an AI tool designed to improve consistency and efficiency by directing pathologists towards regions with the highest predicted mitotic activity and highlighting mitotic figures for review, while retaining pathologist control over region selection and the final count. We evaluated its effect on the reproducibility and efficiency of mitotic counting in a retrospective, non-interventional, paired reader study comprising 385 whole-slide images from 3 centres in 3 countries and 7 tumour types using 3 different scanners. 13 pathologists participated, with each slide assessed independently by 3 pathologists without AI assistance and again with AI assistance after a minimum 2 week washout period. Across all slides, AI-assisted counting increased the intraclass correlation coefficient from 0.589 to 0.949. Mean pathologist-level median assessment time decreased from 286.4 to 127.8 seconds, corresponding to an average saving of 151.8 seconds per assessment. Improvements in agreement and efficiency were also observed in supporting analyses using HALO AP and Sectra image management systems and in 2 additional tumour types outside the main study population. AI-assisted assessment was associated with a subtle shift towards higher mitotic counts and scores, consistent with identification of more active mitotic hotspots and fewer missed mitotic figures. The frequency of score change between unassisted and AI-assisted assessment was comparable with inter-pathologist variation during routine counting. These findings support the use of MitPro as an assistive tool for more consistent and efficient mitotic assessment in routine practice.

Problem

• mitotic counting
• inter-pathologist variability
• tumour grading
• reproducibility
• efficiency

Innovation

• Mitotic counting
• Artificial intelligence
• Computational pathology
• Reproducibility
• Whole-slide imaging