Fluorine‐18 Fluorodeoxyglucose‐Positron Emission Tomography/Computed Tomography for Detection of Lymph Node Metastasis in Canine Mast Cell Tumour
Bibliographic record
Abstract
ABSTRACT Lymph node (LN) metastasis has been associated with shorter survival times in dogs with mast cell tumour (MCT), and treatment of metastatic LN with lymphadenectomy or irradiation has been demonstrated to improve outcomes. Identification of metastatic LN in dogs with MCT is therefore of both prognostic and therapeutic significance. The aim of this prospective, exploratory study was to investigate whether fluorine‐18 fluorodeoxyglucose‐positron emission tomography/computed tomography ( 18 F‐FDG‐PET/CT) is a useful staging tool for the detection of metastatic LN in dogs with cutaneous or subcutaneous MCT, using histopathology as the gold standard. Sixteen client‐owned dogs with cytologically or histologically confirmed cutaneous or subcutaneous MCTs underwent full‐body 18 F‐FDG‐PET/CT followed by surgical removal and histopathology of the primary tumour and regional LN(s). The maximum standard uptake value (SUV max ) of the tumour and LN(s) was measured. Primary tumours were graded using both the Patnaik and Kiupel grading systems, and mitotic count was tabulated. LNs were categorised based on Weishaar's histologic criteria for nodal metastasis. Eighteen primary tumours were excised: six subcutaneous and 12 cutaneous MCTs. Of 33 excised regional LNs, 18 (55%) were categorised as metastatic (≥ HN2). There was no difference between the median SUV max of metastatic (3.88) and nonmetastatic LNs (3.16) ( p = 0.41). SUV max was positively correlated with the mitotic count of the primary tumour ( p = 0.02). The results of this exploratory study suggest that 18 F‐FDG‐PET/CT may not be useful for identifying metastatic LNs in canine MCT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".