Whole-slide analysis of mitotic activity in melanoma reveals higher-proliferation hotspots and distinct spatial patterns

Why clinicians should know about it

• Picked for Histology (paper of the day, 5 August 2026).

Abstract

INTRODUCTION: Accurate assessment of mitotic activity is an important component of melanoma prognostication but remains challenging due to interobserver variability and difficulties in identifying representative mitotic hotspots. We developed and evaluated a deep learning-based mitosis detection algorithm for whole-slide images (WSIs) of melanoma and investigated its utility for hotspot identification and spatial analysis of mitotic distribution. METHODS: The model was trained on manually annotated histopathology images and applied to a cohort of 114 melanoma cases comprising 378 WSIs. Performance was assessed against expert annotation using sensitivity, precision, and F1-score. Algorithm-identified hotspots were reviewed by a pathologist and compared with mitotic counts reported in routine clinical practice. Spatial organization of mitotic figures was evaluated using nearest-neighbour distance analyses. RESULTS: The algorithm achieved a sensitivity of 88%, precision of 75%, and an F1 score of 0.81, demonstrating strong agreement with expert assessment. Across the cohort, 30,547 mitotic figures were detected, enabling comprehensive whole-slide analysis of proliferative activity. AI-assisted hotspot identification yielded significantly higher mitotic counts than those reported in routine pathology practice (5.35 ± 7.9 versus 2.96 ± 3.73 mitoses/mm2, P = 0.004), suggesting improved identification of regions with maximal proliferative activity. Whole-slide analysis further enabled characterization of mitotic spatial organization. Distinct patterns ranging from clustered to relatively uniform distributions were observed across tumours. Moreover, melanomas arising in chronically sun-damaged (CSD) sites demonstrated significantly greater nearest-neighbour distances than melanomas from non-CSD sites (423.3 μm versus 285.4 μm, P = 0.023), indicating differences in the spatial organization of proliferating tumour cells. CONCLUSION: Our findings demonstrate that AI-assisted mitosis detection can accurately identify mitotic figures while improving hotspot detection compared with routine assessment. Beyond mitotic quantification, large-scale whole-slide analysis enables novel spatial characterization of mitotic organization, providing additional insights into melanoma biology and highlighting new opportunities for computational pathology-based biomarker discovery.

Abstract as published, via PubMed.