Canine, Feline, and Murine Mammary Tumors as a Model for Translational Research in Breast Cancer
Bibliographic record
Abstract
In veterinary medicine, mammary tumors are the most common neoplasms in female dogs and the third most frequent in cats, representing a significant challenge. Efforts have been directed toward adopting standardized diagnostic criteria to better understand tumor behavior and progression in these species. Meanwhile, the use of animal models has substantially advanced the understanding of comparative mammary carcinogenesis. These models provide critical insights into factors responsible for the disease in humans, with the expectation that such factors can be identified and controlled. In this context, this review presents a work based mainly on articles published by a research group specializing in mammary pathology (Laboratory of Comparative Pathology-Department of General Pathology-ICB/UFMG) and its collaborators, complementing their results with literature findings. The publications were categorized into animal research, experimental research, and human research. These studies addressed topics such as diagnosis, prognostic and predictive factors, tumor microenvironment, inflammation associated with tumors, treatment approaches, and factors influencing tumor growth. The conceptual network analysis underscores the importance of in vivo breast cancer models, both experimental and spontaneous, for understanding tumor progression mechanisms and therapeutic responses, offering valuable contributions to veterinary and human oncology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".