Virtual Evaluation of Hematoxylin & Eosin via Digital Pathology Survey (VEED) Project: Results from a Non-Inferiority Study of a Tabs-Based Staining Method
Background/Objectives: Despite hematoxylin and eosin (H&E) staining remaining 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 histochemical 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 40× 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.
Authors
- Silvia Uccella (ORCID: https://orcid.org/0000-0002-5714-7295)
- Alessandro Gambella (ORCID: https://orcid.org/0000-0001-7826-002X)
- Roberto Virgili
- Mariavittoria Vescovo (ORCID: https://orcid.org/0000-0001-6959-3252)
- Bruna Cerbelli (ORCID: https://orcid.org/0000-0001-5419-2309)
- Giuseppe Nicolò Fanelli (ORCID: https://orcid.org/0000-0001-7069-7980)
- Vincenzo L’Imperio (ORCID: https://orcid.org/0000-0002-9284-2998)
- Luisella Righi (ORCID: https://orcid.org/0000-0002-8927-204X)
- Cristiana Bellan (ORCID: https://orcid.org/0000-0001-6119-9007)
- Giorgio Cazzaniga (ORCID: https://orcid.org/0000-0001-5373-0611)
- Veronica Vicomandi (ORCID: https://orcid.org/0000-0001-5207-8764)
- Giuseppe Perrone (ORCID: https://orcid.org/0000-0002-9538-5729)
- Lorenzo Nibid (ORCID: https://orcid.org/0000-0001-8583-0596)
- Luca Mastracci (ORCID: https://orcid.org/0000-0003-0193-5281)
- Francesco Merolla (ORCID: https://orcid.org/0000-0003-0711-6738)
- Albino Eccher (ORCID: https://orcid.org/0000-0002-9992-5550)
- Stefano Marletta (ORCID: https://orcid.org/0000-0001-7881-8767)
- Pasquale Pisapia (ORCID: https://orcid.org/0000-0002-6429-0620)
- Giuseppe Ingravallo (ORCID: https://orcid.org/0000-0002-4792-3545)
- Alessandro Caputo (ORCID: https://orcid.org/0000-0001-5139-3869)
- Erica Iannaccone
- Martina D’Angelo
- Elisabetta Maffei
Institutions
- University of Siena (IT)
- University of Pisa (IT)
- University of Modena and Reggio Emilia (IT)
- University of Molise (IT)
- University of Salerno (IT)
- Humanitas University (IT)
- Università Campus Bio-Medico (IT)
- Cornell University (US)
- Campus Bio Medico University Hospital (IT)
- Ospedale Policlinico San Martino (IT)
- IRCCS Humanitas Research Hospital (IT)
- Azienda Ospedaliera Universitaria Pisana (IT)
- University of Turin (IT)
- University of Bari Aldo Moro (IT)
- University of Milano-Bicocca (IT)
- University of Naples Federico II (IT)
- University of Genoa (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Diagnostics
- Published
- 2026-06-16
- DOI
- https://doi.org/10.3390/diagnostics16121868
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00