Comparison of B-mode and Doppler ultrasonographic findings with histologic features of benign and malignant mammary tumors in dogs
Bibliographic record
Abstract
OBJECTIVE: To compare and correlate B-mode and color Doppler ultrasonographic characteristics with the histologic findings of benign and malignant mammary tumors in dogs. STUDY POPULATION: 49 mammary tumors in 26 dogs. PROCEDURES: Before excision, tumors were evaluated via B-mode and color Doppler ultrasonography to assess size, echogenicity, echopattern, acoustic transmission, invasiveness, and vascularity. Paraffin-embedded microsections of the tumors were stained with H&E and examined for presence of necrosis, cysts, cartilage, bone, mineralization, invasion of surrounding tissue, and tissue heterogeneity. To assess vascularity, the number and distribution of vessels that were stained by the Verhoeff van Gieson technique were recorded. RESULTS: Tumor echogenicity and echopattern on ultrasonographic images correlated with tissue heterogeneity detected histologically. Acoustic enhancement was correlated with the presence of necrotic or cystic areas. Tumor invasion into surrounding tissues as determined ultrasonographically did not correlate with the histologic findings. There was a significant correlation between the number of detected vessels and distribution of flow within the tumors determined via ultrasonographic and histologic examinations. CONCLUSIONS AND CLINICAL RELEVANCE: In canine mammary tumors, ultrasonographic characteristics appear to be correlated with histopathologic changes. Data suggest that ultrasonography may have an important role in the evaluation of mammary tumors in dogs, particularly in the evaluation of tissue composition and tumor vascularity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".