PC3 - 180 Is Conventional and Perfusion MRI Useful in Predicting Histopathology Defined Percentage of Recurrence in High Grade Gliomas
Bibliographic record
Abstract
New enhancing lesions after surgery and chemoradiation for high grade glioma commonly contain variable proportions of tumor recurrence (TR), tissue necrosis and treatment related changes. Our purpose is to determine whether the pattern of contrast enhancement and perfusion MR parameters correlate with the percentage of TR in these lesions. Methods: We prospectively enrolled 30 patients with high grade gliomas who presented with a new enhancing lesion suspicious for tumor recurrence. Each patient underwent conventional MRI with DCE and DSC perfusion MRI. The pattern of enhancement was classified by a blinded neuroradiologist in 5 different categories (solid, focal nodular, peripheral rim, hazy, punctate). A hot spot region-of-interest analysis was performed for each parametric map (Ktrans, AUC, Vp, corrected CBV). TR percentage was defined histopathologically. The lesions were categorized into predominant TR (=tumor>70%), predominant treatment related changes (T=<35%) and mixed lesions (35 %< T=<70%). Differences between the groups were assessed via Kruskal-Wallis and Mann-Whitney U tests. Results: There were 32 lesions (4 predominantly treatment related lesions,5 mixed lesions,23 predominant tumor recurrence). There is no significant difference in the enhancement pattern between the three groups (p=0.18).Statistically significant difference was only seen for corrected CBV between the three groups (p=0.01), mainly between the mixed and predominant tumor groups. The rest of the perfusion parameters did not show a statistically significant difference between the groups(p>0.05). Conclusion: Corrected CBV might be useful in predicting the proportion of tumor recurrence in post-treatment high grade gliomas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".