P.083 Radiology reporting of low-grade glioma growth underestimates tumor expansion
Bibliographic record
Abstract
Background: Surveillance by serial magnetic resonance imaging (MRI) is important in the management of diffuse low-grade gliomas (LGGs). Radiological interpretations of LGG scans, however, are typically qualitative and difficult to use clinically. Methods: We retrospectively compared radiological interpretations of LGG growth/stability to volume change measured by manual segmentation. Tumour diameter was measured to evaluate methods for assessing glioma progression, including RECIST criteria, Macdonald/RANO criteria, and mean tumour diameter/ellipsoid method. Results: Tumours evaluated as stable by radiologists grew a median 5.1 mL (11.1%) relative to the comparison scan. Those evaluated as having grown increased by 13.3 mL (23.7%). Diameter-based measurements corresponded well but tended to overestimate segmented volumes, and overestimation error increased with tumour size. Agreement with segmented volume improved from a mean difference of 17.6 to 4.5 to 3.9 mm for diameter and from 104.0 to 25.3 to 15.9 mL for volume with measurements in one, two, and three dimensions. Conclusions: Given evidence that LGG volume and growth are prognostic factors, lesions should be accurately measured. Current radiological reporting workflows fail to appreciate and communicate the true expansion of LGGs. Volumetric analysis remains the gold standard for growth assessment, but diametric measurements in three dimensions may be an acceptable alternative.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".