Virtual Evaluation of Hematoxylin & Eosin via Digital Pathology Survey (VEED) Project: Results from a Non-Inferiority Study of a Tabs-Based Staining Method
Year: 2026
Type: article
Abstract: Background/Objectives: Despite Hematoxylin and eosin (H&E) staining remains the cornerstone of histopathological diagnosis, substantial intra- and inter-laboratory variability persists. This issue is increasingly relevant in Digital Pathology, where staining inconsistency may affect whole-slide image interpretation and the performance of image analysis algorithms. In the present work, we evaluated the diagnostic adequacy and non-inferiority of a novel tabs-based H&E staining method compared with conventional liquid reagents. Methods: Fifty formalin-fixed paraffin-embedded tissue samples from routine practice were sectioned in duplicate and stained either conventionally or using H&E Stain Tabs. After slide review, 14 representative tissue samples were selected, scanned at 40x magnification, and used to generate 24 matched image pairs at different magnifications. A blind online survey was completed by 13 expert pathologists using high-quality monitors. Participants assessed overall staining preference and rated stromal, epithelial, cytoplasmic, and nuclear staining quality. Non-inferiority was tested using a predefined margin of −0.10, and paired rating differences were analyzed using the Wilcoxon signed-rank test. Results: Across 312 paired evaluations, the tabs-based method was preferred in 120 cases (38.5%), conventional staining in 118 cases (37.8%), and no preference was expressed in 74 cases (23.7%). The tabs-based method met the criterion for non-inferiority compared with standard staining (z = 2.7). Rating-scale analysis showed significantly better stromal evaluation with the tablet-based method (z = 2.638; p = 0.008), whereas no significant differences were observed for epithelial, cytoplasmic, or nuclear staining. All evaluated images were considered diagnostically adequate. Conclusions: The tabs-based H&E stain was non-inferior to the conventional method and showed particularly favorable performance in the assessment of stromal components. These findings support its potential role in improving staining reproducibility and standardization, particularly in Digital Pathology workflows where pre-analytical and analytical consistency is critical.
Source: Diagnostics
Authors: Lorenzo Nibid, Erica Iannaccone, Elisabetta Maffei, Veronica Vicomandi, Martina D’Angelo, Cristiana Bellan, Bruna Cerbelli, Giorgio Cazzaniga, Vincenzo L’imperio, Albino Eccher, Giuseppe Nicolò Fanelli, Alessandro Gambella, Luca Mastracci, Giuseppe Ingravallo, Stefano Marletta, Francesco Merolla, Pasquale Pisapia, Luisella Righi, Silvia Uccella, Mariavittoria Vescovo, Roberto Virgili, Alessandro Caputo, Giuseppe Perrone
Institutions: Università Campus Bio-Medico, Campus Bio Medico University Hospital, University of Salerno, University of Siena, Sapienza University of Rome, University of Milano-Bicocca, University of Modena and Reggio Emilia, University of Pisa, Azienda Ospedaliera Universitaria Pisana, Weill Cornell Medicine, Ospedale Policlinico San Martino, IRCCS Azienda Ospedaliera Metropolitana, University of Genoa, University of Bari Aldo Moro, Humanitas University, University of Molise, University of Naples Federico II, University of Turin, IRCCS Humanitas Research Hospital
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7164922293