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APPLICATION OF AUTOMATED SYSTEMS FOR ANALYZING PEDIATRIC
BRAIN TUMORS: MORPHOLOGY, IHC, AND GENETICS
Tashmatov Suxrob Abdurashidovich
1
Magrupov Baxodir Asadullayevich
2
"National Children's Medical Center" Children's Neurosurgery Department, Parkent
Street, 294, Tashkent, Uzbekistan, 1001711
1
Center for the Development of Professional Qualification of Medical Workers,
Department of Pathological Anatomy and Forensic Medicine
2
https://doi.org/10.5281/zenodo.15471884
Abstract
This study presents the first experience in Uzbekistan using automated platforms to
analyze pediatric brain tumors through a combination of histological, immunohistochemical
(IHC), and genetic methods. Tissue samples from 98 children (aged 1–17 years) were
examined using automated systems (Leica Bond-Max, Ventana Benchmark Ultra). Key
markers such as Ki-67, GFAP, OLIG2, Synaptophysin, IDH1, and BRAF V600E were assessed.
The integration of digital microscopy, IHC, and PCR-based molecular diagnostics improved
diagnostic accuracy and allowed for the identification of biological behavior and prognosis.
The study demonstrates the efficiency and reproducibility of automated analysis in pediatric
neuro-oncology practice.
Keywords:
pediatric brain tumors, automated systems, IHC, molecular markers, Ki-67,
IDH1, BRAF, digital pathology, diagnosis.
Relevance
Brain tumors are the most common solid tumors in children and account for a
significant portion of pediatric cancer mortality. Accurate diagnosis and classification are
crucial for treatment planning and prognosis. Traditional histology often falls short in
characterizing tumors with complex or ambiguous features. Recent advances in digital
pathology and automated analysis platforms have enabled more standardized and objective
evaluation. In Uzbekistan, there is limited experience using integrated diagnostic systems for
pediatric neuro-oncology. The introduction of automated systems such as Leica Bond-Max
and Ventana Benchmark Ultra offers high-throughput, reproducible assessment of
immunohistochemical markers and molecular alterations. This approach minimizes human
error, reduces variability, and accelerates workflow. Furthermore, molecular profiling of
IDH1, BRAF V600E, and Ki-67 provides critical prognostic and therapeutic information.
Implementing such systems into clinical practice in Uzbekistan can modernize the diagnostic
process, improve accuracy, and facilitate targeted therapy decisions for children with central
nervous system tumors.
Objective:
To evaluate the effectiveness of automated systems in analyzing histological,
immunohistochemical, and genetic features of pediatric brain tumors for improving
diagnostic precision and treatment planning.
Materials and Methods
The study included tumor samples from 98 pediatric patients aged 1 to 17 years, treated
at the National Children's Medical Center between 2021 and 2024. Tissue sections were fixed
in formalin, embedded in paraffin, and stained with H&E. Immunohistochemistry was
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performed using Leica Bond-Max and Ventana Benchmark Ultra platforms to assess Ki-67,
GFAP, OLIG2, Synaptophysin. Genetic analysis of IDH1 and BRAF V600E mutations was
conducted using PCR and Sanger sequencing. Digital imaging and automated quantification
tools were used for interpretation. Statistical analysis was performed using SPSS and
GraphPad Prism to evaluate correlations between histological types and molecular profiles.
Results
Among the 98 analyzed tumors, astrocytomas accounted for 49%, medulloblastomas for
22%, and ependymomas for 16%. High Ki-67 index (>20%) was seen in 34% of cases,
primarily in medulloblastomas and high-grade gliomas. Positive GFAP and OLIG2 expression
was confirmed in most glial tumors. IDH1 mutations were found in 9% of samples, mostly in
diffuse gliomas, while BRAF V600E mutations were detected in 13%, mainly in pilocytic
astrocytomas. Automated quantification showed consistent and reproducible results. The use
of automated systems significantly reduced analysis time and improved marker detection
sensitivity, providing a strong basis for molecular classification and personalized treatment.
Conclusion
The integration of automated platforms into the analysis of pediatric brain tumors
enhances diagnostic accuracy, efficiency, and reproducibility. Combining histological
assessment with immunohistochemistry and molecular profiling on automated systems
enables comprehensive tumor characterization. This approach allows early identification of
high-risk tumors and molecular subtypes, guiding therapy choices. In the context of
Uzbekistan's developing healthcare infrastructure, implementing automated diagnostic tools
represents a significant advancement toward modern, personalized pediatric neuro-oncology.
The study highlights the practical value of technology-driven diagnostics in routine clinical
workflows and supports wider adoption of digital and molecular pathology in resource-
limited settings.
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