HOUT-14. PROGNOSTIC IMPACT OF FIRST PSEUDOPROGRESSION ON MRI IN GLIOBLASTOMA, AN 11 YEARS EXPERIENCE FROM A CANADIAN UNIVERSITY CENTER
Bibliographic record
Abstract
Abstract BACKGROUND In glioblastomas (GBM) patients the first post-radiation MRI is usually difficult to interpret given the post-radiotherapy enhancement and possible pseudo-progression which is present in almost 50% of the patients. METHODS We retrospectively analyzed all patients with GBM treated between 2006 and 2017 at the CHUM (SARDO database). If the first brain MRI done within 3 months after the systemic treatment showed progression of contrast, these patients were considered pseudoprogressors (PsP) while the patients who had stable or response to treatment were the non-progressors (nP). If progression persisted in subsequent MRI with a change of treatment within 6 months, they were considered early progressors (eP). If subsequent MRI improved or was stable, they were classified as nP (or true pseudo-progression). RESULTS In our cohort of 470 patients with GBM, 57.7% were nP and 42.3% were PsP after the first post-treatment imagery. The median follow-up was 10 months. The nP had a longer mOS 15.3m vs 11.3m, p < 0.001, regardless of subsequent evolution. After the second assessment, 67.8% of PsP patients were then considered as eP and 36.4% of nP patients also progressed within 6 months. The nP either after the first or second evaluation had the same mOS (19.9m vs 18.3m), just like the eP (9.3m vs 8.6m), independently of the subsequent treatment. No demographic, molecular or clinical factor predicted PsP, except for tumor size (> 5cm, p=0.024). PsP incidence was similar between 2006–2011 (PsP 57.8%) and 2012–2017 (42.2%). The 1y OS with pseudo progression at the first MRI was 39.7% vs 54.8% with no progression (p=0.001) which has a meaningful impact for the patient. CONCLUSION Pseudo-progression is frequent (42%) in glioblastoma and predicts a poorer prognosis with 1y OS of 39,7%. In fact, PsP patients have more than two-thirds chance to progress precociously.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".