Clinical evaluation of the CrossNN DNA methylation classifier for central nervous system tumors.

Authors

Jonas Dahnoun, Léon C van Kempen, Senada Koljenović, Ken Op de Beeck, Tomas Menovsky, Bart Feyen, Melek Ahmed, Anne Sieben, Karen Zwaenepoel

Published in

Brain pathology (Zurich, Switzerland). Pages e70137. Sep 02, 2026. Epub Sep 02, 2026.

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.

PMID:
42686041
Bibliographic data and abstract were imported from PubMed on 03 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.