C.07 Predicting individualized risk of recurrence: development and validation of a DNA-methylation based nomogram in meningioma
Bibliographic record
Abstract
Background: Challenges in predicting risk of recurrence for individual patients with meningioma limits appropriate selection of patients who may benefit from adjuvant radiation therapy to delay recurrence. Here, we aimed to develop and validate a combined clinicomolecular predictor of early recurrence for individual patients with meningiomas. Methods: A methylation-based predictor of 5-year recurrence-free-survival (RFS) was developed using DNA-methylation profiles from a training cohort of 228 patients. Model performance was compared to a standard-of-care histological-based model using three independent cohorts (N=54 ;N=140; N=64 patients). Subsequently, a nomogram that integrated the methylome-based predictor with prognostic clinical factors was developed and validated. Results: The methylome-based predictor of 5-year RFS performed favorably compared to a grade-based predictor when tested using the three validation cohorts (ΔAUC=0.10, 95%CI 0.03 – 0.018) and was independently associated with RFS on multivariable Cox regression analysis (HR=3.6, 95%CI 1.8–7.2, P<0.001). A nomogram combining the methylome-predictor with clinical factors demonstrated greater discrimination for recurrence than a nomogram using clinical factors alone (ΔAUC=0.25, 95%CI 0.22–0.27) and resulted in two risk groups with distinct recurrence patterns (HR=7.7, 95%CI 5.3–11.1, P<0.001) and clinical implications. Conclusions: Our validated models provide important novel prognostic information that could be used to individualize decisions regarding post-operative therapeutic interventions in meningioma.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".