Melanocytoma-like melanoma may be the missing link between benign and malignant uveal melanocytic lesions in humans and dogs: a comparative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The cutoff presented in the current classification of canine melanocytic lesions by Wilcock and Pfeiffer is based on the clinical outcome rather than morphological concepts. Classification of tumors based on morphology or molecular signatures is the key to identifying new therapies or prognostic factors. Therefore, the aim of this study was to analyze morphological findings in canine melanocytic lesions based on classic malignant morphologic principles of neoplasia and to compare these features with human uveal melanoma (HUM) samples. In total, 64 canine and 111 human morphologically malignant melanocytic lesions were classified into two groups (melanocytoma-like or classic melanoma) based on the presence or absence of M cells, respectively. Histopathological characteristics were compared between the two groups using the χ-test, t-test, and multivariate discriminant analysis. Among the 64 canine tumors, 28 (43.7%) were classic and 36 (56.3%) were melanocytoma-like melanomas. Smaller tumor size, a higher degree of pigmentation, and lower mitotic activity distinguished melanocytoma-like from classic tumors with an accuracy of 100% for melanocytoma-like lesions. From the human series, only one case showed melanocytoma-like features and had a low risk for metastasis characteristics. Canine uveal melanoma showed a morphological spectrum with features similar to the HUM counterpart (classic melanoma) and overlapped features between uveal melanoma and melanocytoma (melanocytoma-like melanoma). Recognition that the subgroup of melanocytoma-like melanoma may represent the missing link between benign and malignant lesions could help explain the progression of uveal melanoma in dogs; these findings can potentially be translated to HUM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it