Cancer detection in clinical practice and using blood-based liquid biopsy: A retrospective audit of over 350 dogs
Bibliographic record
Abstract
BACKGROUND: Guidelines-driven screening protocols for early cancer detection in dogs are lacking, and cancer often is detected at advanced stages. HYPOTHESIS/OBJECTIVES: To examine how cancer typically is detected in dogs and whether the addition of a next-generation sequencing-based "liquid biopsy" test to a wellness visit has the potential to enhance cancer detection. ANIMALS: Client-owned dogs with definitive cancer diagnoses enrolled in a clinical validation study for a novel blood-based multicancer early detection test. METHODS: Retrospective medical record review was performed to establish the history and presenting complaint that ultimately led to a definitive cancer diagnosis. Blood samples were subjected to DNA extraction, library preparation, and next-generation sequencing. Sequencing data were analyzed using an internally developed bioinformatics pipeline to detect genomic alterations associated with the presence of cancer. RESULTS: In an unselected cohort of 359 cancer-diagnosed dogs, 4% of cases were detected during a wellness visit, 8% were detected incidentally, and 88% were detected after the owner reported clinical signs suggestive of cancer. Liquid biopsy detected disease in 54.7% (95% confidence interval [CI], 49.5%-59.8%) of patients, including 32% of dogs with early-stage cancer, 48% of preclinical dogs, and 84% of dogs with advanced-stage disease. CONCLUSIONS/CLINICAL IMPORTANCE: Most cases of cancer were diagnosed after the onset of clinical signs; only 4% of dogs had cancer detected using the current standard of care (i.e., wellness visit). Liquid biopsy has the potential to increase detection of cancer when added to a dog's wellness visit.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| 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 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".