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000285055 1001_ $$aSill, Martin$$b0
000285055 245__ $$aAdvancing CNS tumor diagnostics with expanded DNA methylation-based classification.
000285055 260__ $$aCambridge, Mass.$$bCell Press$$c2026
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000285055 520__ $$aDNA methylation-based classification is now central to contemporary neuro-oncology, as highlighted by the World Health Organization (WHO) classification of central nervous system (CNS) tumors. We present the Heidelberg CNS Tumor Methylation Classifier version 12.8 (v12.8), trained on 7,495 methylation profiles, which expands recognized entities from 91 classes in version 11 (v11) to 184 subclasses. This expansion is a result of newly identified tumor types discovered through our large online repository and global collaborations, underscoring CNS tumor heterogeneity. The random forest-based classifier achieves 95% subclass-level accuracy, with its well-calibrated probabilistic scores providing a reliable measure of confidence for each classification. Its hierarchical output structure enables interpretation across subclass, class, family, and superfamily levels, thereby supporting clinical decisions at multiple granularities. Comparative analyses demonstrate that v12.8 surpasses previous versions and conventional WHO-based approaches. These advances highlight the improved precision and practical utility of the updated classifier in personalized neuro-oncology.
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000285055 650_7 $$2Other$$aCNS tumors
000285055 650_7 $$2Other$$aDNA methylation
000285055 650_7 $$2Other$$aMLOps
000285055 650_7 $$2Other$$aartificial intelligence
000285055 650_7 $$2Other$$aclassification
000285055 650_7 $$2Other$$aepigenetics
000285055 650_7 $$2Other$$amachine learning
000285055 650_7 $$2Other$$amolecular diagnostics
000285055 650_7 $$2Other$$aprecision medicine
000285055 650_7 $$2Other$$atumor heterogeneity
000285055 7001_ $$aSchrimpf, Daniel$$b1
000285055 7001_ $$aPatel, Areeba$$b2
000285055 7001_ $$aSturm, Dominik$$b3
000285055 7001_ $$aJäger, Natalie$$b4
000285055 7001_ $$aSievers, Philipp$$b5
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