Use of CT and MR imaging in radiation therapy planning of imaging‐diagnosed canine intracranial meningioma achieves better tumor coverage than CT alone
Bibliographic record
Abstract
The aim of this retrospective, secondary analysis study was to quantify the dosimetric impact of the lack of interobserver agreement on gross tumor volume (GTV) delineation for canine meningioma. This study used a previously reported population of 13 dogs with GTVs contoured on CT alone and on registered CT-MR by 18 radiation oncologists. The "true" GTV was generated for each dog using a simultaneous truth and performance-level estimation algorithm, and "true" brain was defined as the whole brain minus true GTV. Treatment plans were generated for each dog and observer combination, using criteria applied to the observer's GTV and brain contours. Plans were then categorized as a pass (met all planning criteria for true GTV and true brain) or fail. A mixed-effects linear regression was performed to examine differences in metrics between CT and CT-MR plans and mixed-effects logistic regression was performed to examine differences in percentages of pass/fail between CT and CT-MRI plans. The mean percent coverage of true GTV by prescribed dose was higher for CT-MR plans than for CT plans (mean difference 5.9%; 95% CI, 3.7-8.0; P < 0.001). There was no difference in the mean volume of true brain receiving ≥24 Gy and in maximum true brain dose between CT plans and CT-MR plans (P ≥ 0.198). CT-MR plans were significantly more likely to pass the criteria for true GTV and true brain than CT plans (OR 1.75; 95% CI, 1.02-3.01; P = 0.044). This study demonstrated significant dosimetric impact when GTV contouring was performed on CT alone compared with CT-MR.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".