Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence.

Fiche publication

Date publication

septembre 2026

Journal

Virchows Archiv : an international journal of pathology

Auteurs

Membres identifiés du Cancéropôle Est :
Pr CHENARD Marie-Pierre

Tous les auteurs :
Tondon C, Erb G, Egele C, Michot JP, Fetique D, Chenard MP, Bellocq JP

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/42730840

Résumé

Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise routinely mitigates potential issues due to quality variability into or across laboratories, ensuring diagnostic accuracy. However, as artificial intelligence (AI) induced by whole slide imaging (WSI) emerges as a future advancement in pathology, a shift toward greater technical standardisation of the preparations or adaptive algorithms deserves consideration. This study examined longitudinal data (2019-2024) from the French national external quality assessment (EQA) programme integrated with a national survey conducted in 2024. We report that up to 25,5% of H&E slides, varying by tissue type and assessment year, exhibited technical preparation imperfection from multiple stages, including microtomic sectioning, excessive thickness, and tissue stretching. Separately, 8,3% to 23,8% of slides showed suboptimal staining, characterised mainly by insufficient nucleo-cytoplasmic contrast or intensity fluctuations. Advocating sustained quality improvement to prepare for future diagnostic pathology, we emphasise that optimising pre-analytical H&E preparation quality before AI deployment is critical: robust AI performance requires consistent, high-quality inputs rather than reliance on hypothetical post-hoc algorithmic compensation. Prioritising quality at the source reduces computational burden, environmental impact, and deployment costs while enhancing ethical transparency. Our findings provide technical areas for improvement to enhance H&E testing while anticipating AI integration at scale.