Performance of Frozen Section Histopathology, Imprint Cytology and Fine‐Needle Aspirates for Detecting Canine Metastatic Mast Cell Tumour
Bibliographic record
Abstract
Intra-operative staging of canine mast cell tumour (MCT) currently relies on routine cytology to determine nodal metastasis. While frozen section nodal histopathology is commonly used in humans, its applicability to veterinary settings is poorly characterised. The goal of this study was to determine the diagnostic performance of frozen section (FS) histopathology for diagnosing metastatic MCT, as compared to a formalin-fixed histopathologic gold standard. Performances of imprint cytology (IC) and fine needle aspirates (FNA) were also evaluated. Forty-one lymph nodes from 20 dogs with MCT were collected and stained with haematoxylin and eosin (HE) and Giemsa (formalin-fixed and frozen tissues), and Wright Giemsa and toluidine blue (IC and FNA). Nineteen out of 20 primary tumours were low grade. Frozen HE sections had poor agreement as compared to formalin-fixed HE histopathology (κ = 0.15); however, diagnostic performance increased to a good level of agreement when interpretation was combined with Giemsa (κ = 0.46). FNA and IC using Wright Giemsa had agreement comparable to combined frozen section histopathology (κ = 0.51 and 0.43, respectively). Combined frozen sections had a sensitivity of 65% and specificity of 93%, which was the same as FNA. Challenges encountered in morphologic interpretation of frozen sections included inadequate sectioning quality, architectural disruption, ruptured cells, and background metachromatic staining. These data provide support for FS histopathology as a feasible strategy for intra-operative detection of metastatic MCT, with diagnostic agreement similar to conventional cytology. Performance of FS histopathology is conditional upon a metachromatic stain evaluated in parallel with HE.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".