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Histopathologic and molecular profile of gliomas diagnosed in Lagos, Nigeria

  • 0Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MinnesotaUSA.
Neuro-Oncology Practice +

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November 18, 2024

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November 18, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Molecular testing significantly improves brain tumor diagnoses in Nigeria, addressing challenges in resource-limited settings. This study reclassifies gliomas using the 2021 WHO classification, revealing diagnostic disparities.

Area Of Science

  • Neuro-oncology
  • Molecular Pathology
  • Genomics

Background

  • Current brain tumor diagnosis relies on 2021 WHO integrated classification (histomorphology and molecular features).
  • Resource-limited settings often depend solely on histomorphology for neuro-oncology practice.
  • This study addresses the gap in applying advanced diagnostics in Nigeria.

Purpose Of The Study

  • To reclassify glioma cases from Lagos University Teaching Hospital using the 2021 WHO CNS tumor classification.
  • To evaluate the impact of incorporating molecular testing in a resource-limited setting.
  • To highlight global disparities in brain tumor diagnosis.

Main Methods

  • Reevaluation of 56 gliomas (from 55 patients) diagnosed between 2013-2021.
  • Molecular analysis using immunohistochemistry (IHC), chromosomal microarray, targeted next-generation sequencing, and methylation array profiling.
  • Application of 2021 WHO diagnostic criteria to archival formalin-fixed paraffin-embedded (FFPE) tissues.

Main Results

  • Initial morphologic diagnoses changed for 35% (18/52) of cases after applying 2021 WHO criteria.
  • Five gliomas (9.6%) were upgraded, and seven (14%) were downgraded.
  • Adequate DNA was obtained from 71% of archival FFPE tissue blocks.

Conclusions

  • Molecular testing integration substantially enhances brain tumor diagnostic accuracy in Nigeria.
  • The study underscores diagnostic challenges and global inequities in resource-limited settings.
  • Reclassification based on integrated diagnosis is crucial for optimal patient management.

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