HOUT-28. FIRST-LINE TEMOZOLOMIDE VS PCV FOR LOW GRADE GLIOMAS: A 11 YEARS REAL-WORLD DATA FROM CHUM, UNIVERSITY CENTER
Bibliographic record
Abstract
Abstract BACKGROUND Before 2012, Temozolomide (TMZ) was used for low-grade gliomas (LGG) to avoid Lomustine toxicity. After 2012 RTOG data, Procarbazine, Lomustine and Vincristine (PCV) given sequentially with radiotherapy became standard treatment with the side effects burden associated. METHODS We retrospectively reviewed our SARDO clinical database of patients with low-grade glioma LGG grades 2 and 3 between 2006 and 2017 at CHUM University Health Center. Molecular profile for these tumors is reflex and standard in our institution since 2016. RESULTS A total of 123 patients were identified with grade 2 and grade 3 LGG; 37 (30%) treated with PCV and 86 (70%) received TMZ. Median follow up was 11mo for PCV 11mo vs 22mo TMZ. Both groups were balanced in terms of median age, sex, neurologic symptoms and surgery rate. 53% patients had tumor untested for IDH1-2 and codeletion1p19q because of diagnostic before 2016. Disparities were noted with a predominance of grade 3 in the TMZ group (74% vs 27%, p< 0.01). TMZ was the preferred regimen before 2012 (100% vs 43%) and PCV became the standard of care after 2012 (0% vs 57%). Radiation use as first line treatment was 90%. The 4y OS was not significantly longer for PCV 50% compared with TMZ 47% with mOS between both groups (PCV NR vs TMZ 39.9mo, p=0.158). When controlled for tumor grade, the 2y OS was 80% for PCV vs 64% for TMZ,p=0.542. The 4y PFS was trendly longer for PCV group 78% than TMZ group 45%, p=0.148. CONCLUSION As we are still waiting for the prospective ongoing prospective trials comparing these 2 regimens, PCV and radiation are still standard of care regimens for grade 2 and 3 LGG. This retrospective data is not reassuring for a replacement for PCV with TMZ.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".