Clinical evaluation of the CrossNN DNA methylation classifier for central nervous system tumors
In brief
Using two DNA-methylation classifiers adds about ten percent more accurate CNS tumor diagnoses
In a real-world series of 205 CNS tumors, the new CrossNN classifier correctly identified 89% of cases and performed as well as the established Heidelberg tool. When the two classifiers were applied together, clinically informative and correct diagnoses rose by nearly ten percent, a benefit confirmed in a separate 41-case validation set. This dual-classifier approach may boost diagnostic confidence, but workflow integration and cost remain to be defined.
- Journal
- Brain pathology (Zurich, Switzerland) (Q1)
- Published
- 2 September 2026
- Study design
- Prospective / inception cohort
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Jonas Dahnoun, Léon C van Kempen, Senada Koljenović, Ken Op de Beeck, Tomas Menovsky, Bart Feyen, et al.
- PMID
- 42686041
- DOI
- 10.1111/bpa.70137
Why clinicians should know about it
- Picked for Pathology and Forensic Medicine (paper of the day, 5 September 2026): CrossNN DNA methylation classifier for CNS tumor diagnosis
Abstract
DNA methylation profiling is an integral diagnostic tool in the classification of central nervous system (CNS) tumors. While the Heidelberg CNS Tumor Methylation Classifier is widely used to support CNS tumor diagnostics, new classifiers such as CrossNN are emerging. However, their clinical performance and added value within routine diagnostic workflows remain insufficiently explored. In this study, we evaluated the diagnostic performance of the CrossNN classifier in a real-world CNS tumor cohort and compared it with the established Heidelberg classifier to assess its potential as both a non-inferior alternative and a complementary tool to improve diagnostic accuracy. A retrospective cohort of CNS tumors profiled using Illumina Human Methylation 930k EPIC v2 BeadChip arrays was analyzed. Classifier outputs were compared with integrated WHO CNS5 (2021) diagnoses. In addition, CrossNN and Heidelberg outputs were harmonized to WHO CNS5 (2021) tumor type levels and evaluated both individually and within sequential and parallel diagnostic workflows. The proposed workflows were subsequently assessed in an independent prospective validation cohort. Among 205 samples, CrossNN correctly classified 88.8% of cases and demonstrated 86.8% concordance with the Heidelberg classifier. CrossNN demonstrated non-inferior classification performance compared with the Heidelberg classifier. Combining both classifiers increased the number of clinically informative and correct classifications by nearly 10%. This finding was confirmed in an independent validation cohort of 41 samples. In conclusion, these results demonstrate the complementary strength of the CrossNN and Heidelberg classifiers as a dual-classifier strategy to improve diagnostic confidence and accuracy in routine CNS tumor diagnostics.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.