NIMG-27USING MR PERFUSION TO STRATIFY PATIENTS WITH TREATED HIGH-GRADE GLIOMAS: DOES IT CORRELATE WITH OVERALL SURVIVAL?
Bibliographic record
Abstract
BACKGROUND: MR perfusion (MRP) is a relatively new technique, which may aid in identifying tumour recurrence in those with radically treated high-grade gliomas (HGG). Salvage therapies can diminish quality of life; therefore, it is imperative to identify those with true recurrence who may benefit from additional treatment. We aim to assess the relationship between MRP and overall survival to establish the role of MRP as a clinical decision-making tool as well as a prognostic assessment tool. METHODS: A retrospective chart review of adult patients with histopathologically confirmed diagnosis of HGG and who have had at least one post-treatment MRP scan between January 2011 and April 2014 was conducted. MRP studies were interpreted by experienced radiologists based on relative cerebral blood volume (rCBV) colour maps. Survival data was collected from medical records and obituary databases. RESULTS: Sixty-one patients (mean age 56 years) with HGG (18 grade III, 43 grade IV) were included in the analysis. An increased survival benefit was observed in patients with negative MRP (median survival = 10 months) compared to those with positive MRP (median survival = 5 months), [hazard ratio (HR) = 2.51; 95% confidence interval (CI): 1.09 to 5.76; p = 0.03]. The adjusted analysis including all pre-selected variables demonstrated a similar relationship between MRP and overall survival [HR = 2.53; 95% CI: 1.05 to 6.14; p = 0.04]. Other independent predictors did not demonstrate a statistically significant association with overall survival. CONCLUSION: Our data suggest that a positive MRP result may signify the presence of highly active tumour and thus significantly lower overall survival. This implicates a prognostic role for the use of serial MRP studies to identify a time point in which tumour recurrence becomes highly aggressive, which can affect treatment determination and, more importantly, aid in clinical decision-making.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".