CT features of subcutaneous, intermuscular, and intramuscular mast cell tumors in dogs
Bibliographic record
Abstract
Surgical removal is the treatment of choice for subcutaneous (SC), intermuscular (InterM), and intramuscular (IntraM) mast cell tumors (MCTs). Advanced imaging (CT or MRI) is frequently used for presurgical planning, but InterM and IntraM MCTs can be difficult to identify and delineate on CT. Aims of the current retrospective, diagnostic accuracy, observer agreement study were to describe the imaging features of SC, InterM, and IntraM MCTs on CT and to assess the limitation of CT to identify the full local extent of the MCT. Inclusion criteria for the study were dogs with a cytologically or histologically diagnosed MCTs determined to be SC, InterM, or IntraM MCT based on histology and/or a CT scan performed in the gross disease setting. Two board-certified veterinary radiologists reviewed the CT images and recorded location, contrast enhancement pattern, and delineation between the normal and abnormal tissue. Sensitivity and specificity of CT for determining location (SC/InterM versus IntraM) was 85.71% and 55.56%, respectively, when compared to consensus location based on surgical pathology report/CT/MRI review. There was a low inter-rater agreement for delineation (kappa: 0.150 (-0.070 to 0.370) and measurement had a low/moderate correlation (rho: 0.4667 to 0.5792). Upon review by a surgical oncologist, CT findings were deemed insufficient for curative surgical planning in 13 of 16 due to inadequate definition of tumor depth, compartment boundary (fascial plane) or MCT margins. The use of CT for presurgical planning of SC/InterM/IntraM MCT dogs has limitations, especially when differentiating MCT from the adjacent muscle.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".