Utility of nuclear morphometry in the cytologic evaluation of canine cutaneous soft tissue sarcomas
Bibliographic record
Abstract
Cytopathologists lack reliable criteria to distinguish neoplastic from reactive spindle cells; however, with computer-based nuclear morphometry, it is now possible to more objectively and precisely quantify differences between selected populations of cells. Forty-four cutaneous soft tissue sarcomas and 5 cases of reactive spindle cell proliferations in the dog were morphometrically analyzed with regard to median and standard deviation (SD) of nuclear area, diameter (max, min, mean), radius (max, min), perimeter, and roundness. Overall, nuclei from reactive spindle cells were larger, with greater variation in nuclear size and shape. Significant differences (P < 0.05) were found for several nuclear parameters, including the median and SD of maximum diameter and radius, as well as the SD of roundness. No significant differences were found in nuclear parameters between soft tissue sarcomas divided by histologic grade, mitotic index, or tumor necrosis score. Analysis of the sources of variation indicated near-perfect intraobserver and substantial interobserver agreement. The largest source of variation was due to selection of different measurement fields, reflecting the inherent biological variation in nuclear size within the tumor cell population. The results indicate that nuclear morphometry on cytologic preparations is a reproducible method that may be able to differentiate cutaneous soft tissue sarcomas from reactive mesenchymal lesions in the dog. Further studies, including a larger number of cases, are warranted to assess repeatability of results.
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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 | 0.010 |
| 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 | 0.001 |
| 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".