Nilay BAKOĞLU MALINOWSKI, Emel ÇAKIR, İsmail SAYGIN
Journal of Current Hematology & Oncology Research - 2026;4(2):31-39
Aims : The 2016 WHO Classification of Tumors of the Central Nervous System introduced a paradigm shift by integrating molecular features with traditional histomorphology. This study aims to retrospectively re-evaluate glial tumor cases from a major tertiary center in light of these evolving classification criteria and provide a baseline for future molecular research. Methods : A retrospective analysis was conducted on 395 glial tumor cases diagnosed at the Karadeniz Technical University Faculty of Medicine, Department of Pathology, between 2005 and 2016. The cases were re-grouped according to the 2016 WHO criteria. Due to the lack of molecular data available during the archival period, cases were categorized under the "not otherwise specified" (NOS) group to establish a comprehensive database. Results : Among the 395 cases analyzed, glioblastoma was identified as the most frequent histological subtype (n=235). A male predominance was observed (56.5%), with mean and median ages of 48.21 and 50 years, respectively. The most common anatomical location was the frontal lobe, and histological grade IV was the most prevalent grade. Statistical analyses revealed a highly significant association between advancing age and higher tumor grade (chi2=68.45, p<.001), while gender distribution remained homogeneous across major histological groups (p= 0.042). These demographic and distribution data were consistent with global literature. Conclusion : The findings align with international demographic trends while highlighting the practical challenges of transitioning to molecular-based classifications. While the subsequent 2021 WHO Classification further emphasizes IDH status, "histologically defined" or "NOS" designations remain crucial for regions where molecular testing infrastructure is limited. This study provides a robust archival baseline that facilitates future molecular studies and serves as a reference for glial tumor characterization in resource-constrained settings.
