Acute myeloid leukemia with peripheral lymph node involvement in dogs: A retrospective study of 23 cases
Bibliographic record
Abstract
Acute myeloid leukemia (AML) can infiltrate extramedullary tissues, such as the liver, spleen, and lymph nodes and can be difficult to differentiate from lymphoma in cytologic and histologic specimens. Our goal was to identify cytologic features that would support a diagnosis of AML in peripheral lymph node aspirates, for which we used the term extramedullary AML (eAML). Medical records of 23 dogs with a diagnosis of AML and archived lymph node aspirate smears from 2016 to 2024 were reviewed across 4 institutions. Inclusion criteria included ≥50% myeloid blasts plus differentiating myeloid cells in lymph node smears, confirmation of myeloid lineage by flow cytometric analysis, and complete medical records. Peripheral lymphadenopathy was the reason for presentation (9/23, 39%) or was found incidentally on physical examination (14/23, 61%). Most dogs were bi- or pancytopenic (18/23, 78%), with blasts identified in blood smears of 18 dogs (78%). Initial lymph node aspirate interpretations included hematopoietic neoplasia (8/21, 38%), AML (6/21, 29%), lymphoma (5/21, 24%), lymphoid hyperplasia (1/21, 5%), and granulocytic precursor infiltrates (1/21, 5%). On lymph node smear review, cytologic features supporting an eAML were differentiating granulocytes, blasts with myeloid features or promonocytes, dysplastic changes in myeloid cells, and retention of residual lymphocytes. The median survival was 22 days (range = 1-360 days), and 69% of 16 dogs given chemotherapy or glucocorticoids lived for 30 days or more. Our study highlights the importance of hemogram results and lymph node aspirate smear examination for morphologic features of myeloid differentiation to help diagnose eAML in lymph node smears.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".