Automated systems for creating tissue microarrays in oncomorphological studies
Tissue microarrays (TMAs) are a promising method for high-throughput analysis of archived tissue samples. Laboratories using conventional manual methods for creating TMAs face the challenge of increasing efficiency and standardization, which is particularly crucial for oncomorphological research and diagnostics. This can be achieved through the automation of the process. This review focuses on the capabilities and advantages of automated systems for TMA creation over manual methods, with an emphasis on their application in the analysis of Ewing sarcoma and other undifferentiated round cell sarcomas. In the analyzed works, automated systems were used to extract and position tissue cores into recipient paraffin blocks, followed by histological and immunohistochemical analyses on the obtained TMA sections. Furthermore, the quality of the tissue microarray sections was evaluated. In these works, automated systems demonstrated high precision in positioning tissue cores, significantly accelerating TMA creation and improving the quality of the resulting sections. Thus, automated systems for TMA creation offer significant advantages over manual methods, ensuring standardization and increasing the productivity of laboratory research. Automated systems allow for the efficient analysis of large sample sets, which is especially important for the validation of diagnostic and prognostic biomarkers. The published works highlight the need for further development and wider implementation of automated systems in oncomorphological research to enhance their efficiency and reproducibility.
Authors
- Inna A Parfenova (ORCID: https://orcid.org/0009-0002-0667-9352)
- Sofia A Eryshova (ORCID: https://orcid.org/0009-0008-8862-2619)
Institutions
- Ministry of Health of the Russian Federation (RU)
- Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology (RU)
Publication Details
- Journal
- Morphology
- Published
- 2025-05-14
- DOI
- https://doi.org/10.17816/morph.634185
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00