P.043 Presence of infiltrative glioblastoma cells in an isolated area of diffusion restriction
Bibliographic record
Abstract
Background: Diffusion weighted imaging (DWI) has useful diagnostic and predictive value in the assessment of glial tumors. Most studies evaluating the use of DWI in glioblastomas have done so in regions that overlap with abnormal T2/fluid attenuation inversion recovery (FLAIR) signal or contrast enhancement. Isolated DWI abnormalities, which do not overlap with contrast enhancing lesions, are less commonly described. Their relationship with the tumour, and implications for prognosis, are not well understood, though it has been speculated that these lesions may represent infiltrative tumour cells. To our knowledge, this is the first reported case where the presence of infiltrative tumour cells in an area of diffusion restriction has been confirmed via biopsy. Methods: A ring enhancing lesion and isolated DWI hyperintensity from a newly diagnosed patient were biopsied separately. Results: Pathological specimens from both targets were identified as glioblastoma (WHO Grade IV), negative for IDH-1 R132H mutation, with methylated MGMT promoter. Conclusions: In patients with glioblastoma, DWI hyperintensities distant from areas of abnormal T2/FLAIR or contrast enhancement can contain infiltrative tumour cells. The presence of isolated diffusion restriction may be a useful predictor of disease progression and prognosis but further investigation into the nature and behavior of isolated DWI lesions is required.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".