Tumor Volumetric Measurements in Surgically Inaccessible Pediatric Low-grade Glioma
Bibliographic record
Abstract
Tumor measurement is important in unresectable pediatric low-grade gliomas (pLGGs) to determine either the need for treatment or assess response. Standard methods measure the product of the largest 2 lengths from transverse, anterior-posterior, and cranio-caudal dimensions (SM, cm). This single-institution study evaluated tumor volume measurements (VM, cm) in such pLGGs. Of 50 patients treated with chemotherapy for surgically inaccessible pLGG, 8 met the inclusion criteria of having 2 or more sequential MRI studies of T1-weighted Fast-Spoiled Gradient Recalled acquisition. SM and VM were performed by 2 independent neuroradiologists. Associations of measurement methods with defined therapeutic response criteria and patient clinical status were assessed. The mean tumor size at the first MRI scan was 20 cm and 398 cm according to SM and VM, respectively. VM results did not differ significantly from SM-derived spherical volume calculations (Pearson correlation, P<0.0001) with a high interrater reliability. Both methods were concordant in defining the tumor response according to the current criteria, although radiologic progressive disease was not associated with clinical status (SM: P=0.491, VM: P=0.208). In this limited experience, volumetric analysis of unresectable pLGGs did not seem superior to the standard linear measurements for defining tumor response.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".