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Evaluating neurosurgical biopsies for CNS tumor diagnoses: An algorithmic and pattern based approach
M Adelita Vizcaino1, Aditya Raghunathan1
1Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
Indian Journal of Pathology & Microbiology
|May 13, 2022
Summary
The 2021 World Health Organization (WHO) CNS tumor classifications integrate molecular data, increasing diagnostic complexity. This study proposes an algorithmic approach combining histology, immunohistochemistry, and clinical data for accurate neuropathology diagnosis and ancillary testing selection.
Area of Science:
- Neuropathology
- Oncology
- Genomics
Background:
- The World Health Organization (WHO) classifications for Central Nervous System (CNS) tumors increasingly integrate molecular data.
- This integration adds complexity to surgical neuropathology practice.
- Accurate diagnosis relies on both traditional histological methods and molecular analysis.
Purpose of the Study:
- To present an algorithmic approach for evaluating CNS tumor biopsies.
- To assist neuropathologists in accurate diagnosis and grading.
- To guide the optimal selection of ancillary tests for CNS tumors.
Main Methods:
- Review of main histological patterns in CNS tumors.
- Integration of clinical and radiologic features.
- Development of a diagnostic algorithm for neuropathology.
Main Results:
- The proposed algorithm aids in recognizing key histological patterns.
- It facilitates the incorporation of clinical and radiologic information.
- It supports the selection of appropriate ancillary molecular tests.
Conclusions:
- A structured algorithmic approach is crucial for modern CNS tumor diagnosis.
- Combining traditional and molecular methods ensures accurate classification and grading.
- This strategy enhances the diagnostic yield in surgical neuropathology.
