Newly Diagnosed Multifocal GBM: A Monocentric Experience and Literature Review
Bibliographic record
Abstract
Glioblastomas with multiple foci at presentation (mGBMs) account for 2–35% of all GBMs. mGBMs have limited existing data and no standardized treatment. This study aims to determine their incidence, demographic and clinical features, outcome, and prognostic factors in terms of overall survival. We performed a monocentric retrospective study, reviewing patients treated at the Istituto Oncologico Veneto. Inclusion criteria were: new diagnosis of GBM and presence of multiple lesions on pre-treatment MRI. ECOG PS was used to evaluate clinical condition, RANO criteria for radiological assessment, and CTCAE v5.0 for treatment-related adverse events. The incidence of newly diagnosed mGBM was 7.2% and the study population consisted of 98 patients. Median age was 63 years, M:F ratio of 1.8:1, and a surgical approach was undertaken in 73 patients (mostly partial resection). MGMT was methylated in 47.5%, and 82 patients received active oncological treatment (65.9% radiotherapy plus temozolomide (RT + TMZ)). The disease control rate with RT + TMZ was 63%. Median OS of the entire study population was 10.2 months (95% CI 6.6–13.8), and median PFS was 4.2 months (95% CI 3.2–5.2). The ECOG PS, the extent of resection, and the RT + TMZ were significant prognostic factors in the univariate analysis for OS, but only the RT + TMZ was a significant independent OS predictor in the multivariate analysis (HR = 3.1, 95% IC 1.3–7.7, p = 0.014). The incidence of mGBM is not rare. RT + TMZ is confirmed to be an independent prognostic factor for survival and a safe and effective treatment. When feasible, RT + TMZ should be considered as a possible first-line treatment. The role of the extent of resection is still unclear.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".