Gross target volume contouring in canine extra‐axial brain tumors: Effects of magnetic resonance image slice thickness and time between subsequent image sets
Bibliographic record
Abstract
Accurate determination of the gross target volume (GTV) is critical in radiation treatment planning, as errors could result in underdosing of the tumor or overdosing of nearby organs at risk. This multicenter retrospective observational serial measurement study evaluated the effects of variations in MRI slice thickness and a time delay between the diagnostic (MRI-1) and RT planning (MRI-2) MRIs GTV contouring in dogs with presumed meningiomas. The hypothesis was that the GTV would increase in size with time on T1-weighted sequences with contrast. Inclusion required paired MRI acquisition within 3 months. The GTV was contoured on each MRI. Forty-six dogs were included. Slice thickness was significantly different (P < .001) between MRIs: MRI-1 had a median of 3.9 mm (range: 0.8-6 mm; only two dogs <2 mm), and MRI-2 had a median of 0.9 mm (range: 0.6-4.5 mm; only two dogs >2 mm). The median time between MRIs was 22 days (range: 8-74 days). The MRI-1 GTV was significantly different from MRI-2 GTV (P < .0001); thirty (65%) were larger, five were equal in size, and 12 were smaller than the MRI-2 GTV. This difference in GTV is likely due to the slice thickness differences between MRI acquisitions rather than changes in tumor size due to the short time interval between MRI-1 and MRI-2. This finding highlights the differences between diagnostic and RT treatment-planning MRIs. For brain tumor target contouring, an MRI at the same time as the RT planning CT with <1 mm slice thickness, 3D acquisitions, and anisotropic voxel is recommended.
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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 | 0.003 |
| 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) | 0.000 | 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".