E.2 Proof of concept for liquid biopsy: positive correlation between extracellular vesicles shed by high grade gliomas and volume of hypervascular tumour tissue on MRI
Bibliographic record
Abstract
Background: High grade gliomas (HGGs) shed extracellular vesicles (EVs) into the bloodstream. EV-derived RNA (EV-RNA) can be detected in plasma, making it a potential biomarker for HGG recurrence after treatment. We sought to establish a baseline relationship between EV-RNA in plasma and hypervascular HGG tissue on MRI. Methods: Eight patients with a new diagnosis of HGG had measurements of plasma EV-RNA and contemporaneous dynamic susceptibility contrast (DSC) MRI. Patient-specific median signal intensity of corpus callosum (mSI-CC) was determined from 10 measurements on the relative cerebral blood volume (rCBV) map. Tumour tissue with signal intensity > mSI-CC and > 2x, > 3x, > 4x and > 5x mSI-CC was segmented on the rCBV map. EV-RNA plasma concentration was correlated with tissue volumes. Results: Pearson correlation showed a significant positive relationship between EV-RNA plasma concentration and tissue volume with signal intensity > mSI-CC (r(6) = 0.899, p = 0.002). No significant relationship could be detected for progressively smaller tissue volumes with signal intensity > 2x, > 3x, > 4x and > 5x mSI-CC. Conclusions: EV-RNA plasma concentration correlates strongly with the total volume of hypervascular HGG tissue on DSC MRI at baseline and merits further evaluation as a biomarker of tumour behaviour in longitudinal imaging studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".