A revised RTOG recursive partitioning analysis (RPA) model for glioblastoma based upon multiplatform biomarker profiles.
Bibliographic record
Abstract
2001 Background: The Radiation Therapy Oncology Group (RTOG) recursive partitioning analysis (RPA) model, which relies on clinical variables, has been used worldwide to establish distinct prognostic classes of patients (pts) with malignant glioma as well as eligibility criteria for clinical trials. In the present study, we have updated the RPA to include additional molecular variables, specifically for glioblastoma (GBM) patients treated in the temozolomide (TMZ)-era, to make the model more relevant, contemporary, and discriminatory. Methods: The dataset utilized was from RTOG 0525, a phase III study examining radiation (RT) with concurrent TMZ, followed by adjuvant standard dose vs. dose-dense TMZ in pts with newly-diagnosed GBM. 162 pts from RTOG 0525 had available tissues for profiling of key signaling molecules using the AQUA platform. Results: pAKT, c-met, and MGMT protein were each found to be significantly associated with adverse outcome on multivariate analysis. These variables were combined with clinical and genetic biomarkers (e.g., MGMT promoter methylation, IDH1 mutation, mRNA profiling) previously found to be of significance (MCP model, ASCO, 2011) to generate an even more robust, discriminatory RTOG RPA model. The explained variation for these three classification models was found to be 41.7 (Current RPA), 19 (MCP), and 14.9% (Clinical RPA), respectively, with higher values indicating better separation of prognostic groups (see table). Conclusions: The current RTOG RPA classification model, based upon incorporation of multi-platform biomarker analysis, holds promise for RT+TMZ-treated GBM patients; further validation of this model is planned. Financial Support: NCI grants U10 CA21661, U10 CA37422, U24 CA114734, 1RC2CA148190, 1RC2CA148190, RO1CA108633, and BTFC grant. [Table: see text]
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".