Prospective phase II clinical trial of molecular glioblastoma (historical grade 2 and 3 IDH wildtype gliomas) preliminary novel exploratory analyses
Bibliographic record
Abstract
PURPOSE: Molecular glioblastoma (molGBM) is a variant lacking the full histopathological profile of glioblastoma. We report a trial aimed at addressing the optimal management of this newly recognized rarer form of glioma. METHODS: In this phase II study, molGBM patients were treated with radiation to a dose of 60Gy to the gross tumor volume (GTV) only, and a single smaller margin potentially as low as 1cm to the clinical tumor volume (CTV). As the trial is ongoing, we report on important exploratory biomarker findings correlating with median overall survival (mOS). Analysis included Kaplan-Meier and univariable/multivariable cox proportional hazard models. Available pre-operative tissue was subjected to epigenetic/DNA methylation profiling on the Infinium EPIC platform. RESULTS: From 2019 to 2023, 25 patients were enrolled based on initial pathology review, with 23 identified on 2nd review as grade 2 and 3 disease. 74% of patients received concurrent chemoradiotherapy with adjuvant chemotherapy. Of 9 patients with profiling, 5 were classified as mesenchymal subtype, while 4 matched to a variety of other phenotypes, including a novel F type GBM. Despite similar histological appearance corresponding to "lower grade glioma", molGBM classified as IDH-wildtype mesenchymal had mOS of 15.7 months (95% CI 15.5-NA) while the other tumors had a mOS of 37.7 months (95% CI 10.9-NA). CONCLUSION: Our results demonstrate underlying heterogeneity within the molGBM population, pointing to future hypothesis-generating risk stratification strategies. We also demonstrate the feasibility of CTV reduction with therapy intensification to set a practice standard for RT management of non-enhancing molGBM.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".