ArticleJournal of clinical laboratory analysis2024
Comparison of MIB-1-Specific Membrane Staining in Hyalinising Trabecular Tumor Using Mainstream Automated Immunohistochemical Staining Platforms.
Article in Journal of clinical laboratory analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Encapsulated neoplasms of the thyroid gland.Virchows Archiv : an international journal of pathology · 2026Review
- Hyalinizing trabecular tumor of the thyroid: A case report.Oncology letters · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMIB-1, a monoclonal antibody against Ki-67, exhibits specific membrane staining in the immunohistochemistry of hyalinising trabecular tumor (HTT). This specific staining pattern is crucial in diagnosing HTT. Although manual immunohistochemical staining remains the established method for MIB-1 staining, this process is complicated, inconsistent, and prone to false negatives.
methodsThis study aimed to explore whether the classical reaction pattern can be replicated by utilizing the current mainstream automated immunohistochemical staining platforms. Furthermore, we examined the effect of different conditions on staining efficiency and their value in clinical diagnosis assistance.
resultsSpecimens obtained from eight and six cases of HTT and non-HTT, respectively, from a single center were stained using the manual staining method and the Dako Autostainer Link 48 (AS48), Dako Omnis, Ventana BenchMark ULTRA, and Leica BOND-III automated immunohistochemical staining platforms. The Autostainer Link 48 was found to be the most stable staining platform, while the BenchMark ULTRA with primary antibody incubation at room temperature (RT) and the Omnis platform with antigen retrieval at pH 9.0 were able to reproduce membrane-positive staining for MIB-1 in the HTT specimens.
conclusionsOur results offer crucial reference value for clinical diagnostic assistance.
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