DNA Methylation-Based Classification for Central Nervous System Tumours: A Health Technology Assessment.
Bibliographic record
Abstract
Background: Central nervous system (CNS) tumours occur when abnormal cells form in the tissues of the brain and/or spinal cord. Conventional testing for CNS tumour classification involves histopathological evaluation and molecular markers. More recently, DNA methylation-based classifier tests are being used as an adjunct tool in addition to conventional tests to help with CNS tumour classification. We conducted a health technology assessment of DNA methylation-based classifier tests for CNS tumours, which included an evaluation of effectiveness, cost-effectiveness, and budget impact of publicly funding DNA methylation-based classifier tests for CNS tumours. After considering the likely effects of testing on the patient experience, we determined not to perform an analysis of patient preferences and values. Methods: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS) and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria.We performed a systematic economic literature search and developed a decision-analytic model to evaluate the cost-effectiveness of using DNA methylation-based classifier tests. We also analyzed the budget impact of publicly funding DNA methylation-based classifier tests. All costs were expressed in 2024 CAD. Results: We included 38 studies in the clinical evidence review. Compared with conventional testing alone, DNA methylation-based classifier tests are an adjunct tool that may improve CNS tumour classification (GRADE: Moderate). The tests may improve downstream patient outcomes, although the evidence is very uncertain (GRADE: Very low). Unclassifiable test results may increase time to treatment, but the evidence is very uncertain (GRADE: Very low).We did not identify any studies that met the inclusion criteria for our economic literature review. We estimated that there were about 716 patients with challenging diagnostic primary CNS tumours in Ontario each year. The cost of clinical-based DNA methylation profiling for CNS tumours was $1,500 per patient. The annual incremental costs of second-tier DNA methylation classifier tests (after the use of conventional test) would be $1,074,738 for all challenging diagnostic cases, and DNA methylation-based classifier tests improved the diagnosis for 195 patients. The incremental cost-effectiveness ratio (ICER; i.e., the incremental cost per case with an improvement in primary CNS tumour classification) was $5,521. Scenario analyses showed that for children aged 0 to 14 years, the ICER was reduced to $2,683. Publicly funding second-tier DNA methylation-based classifier testing for challenging diagnostic cases of primary CNS tumours would result in a budget increase of about $1 million per year, with total additional costs of about $5.4 million over 5 years to test 3,600 patients. The budget increase for funding subgroup populations (e.g., children, patients with malignant tumours) would be smaller. If DNA methylation-based classifiers are used as first-tier tests for all patients with newly diagnosed primary CNS tumours, the additional funding costs would be about $4 million per year, with total additional funding costs of about $21 million over the initial 5-year period. Conclusions: DNA methylation-based classifier tests are an adjunct tool that may improve CNS tumour classification compared with conventional testing alone. Given that there are no empirical willingness-to-pay thresholds for an improvement in primary CNS tumour classification, the cost-effectiveness of DNA methylation-based classifier cannot be determined. Publicly funding second-tier DNA methylation-based classifier tests for challenging diagnostic primary CNS tumours would result in a total budget increase of about $5.4 million over 5 years. Public funding DNA methylation-based classifiers as first-tier tests for all patients with newly diagnosed primary CNS tumours would result in total budget increase of around $21 million over the next 5 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.019 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".