PATH-46. DIAGNOSTIC IMPACT OF THE CNS TUMOR METHYLATION PROFILING IN A NEUROPATHOLOGY CONSULT PRACTICE
Bibliographic record
Abstract
Abstract DNA methylation profiling coupled with the application of CNS tumor methylation classifier has contributed to precise and accurate diagnostics for a range of tumor types involving the central nervous system. The impact and characteristics of methylation profiling on tumor diagnosis has not been fully assessed in the setting of neuropathology consultation practice. A consecutive series of 1,258 surgical neuropathology samples obtained primarily in a consultation practice were profiled over 2-year period and analyzed using the DKFZ/Heidelberg CNS tumor methylation classifier. Among the 1,045 cases received from outside institutions for consultation, the classifier was able to refine a histologically diagnosed entity (e.g. medulloblastoma) in 13.3% (n = 139) cases. A substantially new diagnosis was able to be rendered in an additional 17.9% (n = 187) cases, many of which could be confirmed using orthogonal methods. A “suggestive” (0.30-0.84) classifier score was found in 23% (242) cases and we found that complementary methods (UMAP, t-SNE and nearest-neighbors) were able to resolve this uncertainty in 118 cases. We found tumor purity significantly associated with varied classifier score (p = 1.53e-11). Computational tumor purity adjustment by deconvolution on a subset of gliomas provided a proof-of-concept to resolve diagnostics in the setting of low tumor purity. Overall, this work directly assesses the benefit of methylation classification in a set of diagnostically challenging CNS tumors, addresses tumor purity diminished methylation signal and provides complementary approaches to address diagnostics in cases of low-confidence classifier scores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".