Use of MRI increases interobserver agreement on gross tumor volume for imaging‐diagnosed canine intracranial meningioma
Bibliographic record
Abstract
Abstract There is a lack of information regarding interobserver agreement on canine meningioma gross tumor volume (GTV) delineation, and on the impact of MRI on this agreement. The objectives of this retrospective, secondary analysis, observer agreement study were to describe agreement between veterinary radiation oncologists on GTV for canine intracranial meningioma, and to compare interobserver agreement between delineation based on CT alone and delineation based on fused CT‐MRI. Eighteen radiation oncologists delineated GTV for 13 dogs with an imaging diagnosis of meningioma on pre‐ and postcontrast CT, pre‐ and postcontrast T1‐weighted magnetic resonance, and T2‐weighted magnetic resonance images. Dice similarity coefficient (DSC), concordance index (CI), and center of volume (COV) were used to quantify interobserver agreement. Multilevel mixed models were used to examine the difference in volume, DSC, CI and COV 3D distance between CT and CT‐MR imaging. The mean volume for GTV contours delineated using fused CT‐MRI was larger than when CT alone was used for delineation (mean difference CT‐MR ‐ CT = 0.89 cm3, 95% CI 0.66 to 1.12, P < .001). Interobserver agreement on GTV was improved when MRI was used; the mean DSC and CI were higher, and the mean COV 3D distance was lower, when fused CT‐MRI was used than when CT alone was used (P < .001 for all differences). Based on our results, fused CT‐MRI is recommended for radiation therapy planning of canine intracranial meningioma.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".