ArticleJournal of veterinary internal medicine2026
A machine learning model for cancer screening in dogs using comprehensive circulating microRNA profiles.
Article in Journal of veterinary internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundMicroRNAs (miRNAs) are non-coding RNAs involved in cancer-related biological processes. To date, no studies have determined that liquid biopsy using miRNA can specifically identify dogs with cancer from a mixed population of dogs with and without non-malignant diseases. HYPOTHESIS/
objectivesTo assess the utility of a diagnostic model that differentiates dogs with cancer from a combined group of healthy dogs and dogs with non-malignant diseases, using miRNA profiles obtained by next-generation sequencing (NGS) and analyzed using machine learning. ANIMALS: A total of 574 dogs were enrolled in the study: 168 with cancer, 138 with non-malignant diseases, and 268 healthy controls.
methodsPlasma samples from all dogs were analyzed by NGS to generate comprehensive miRNA profiles. Models were developed using DataRobot, based on the 50 most highly expressed miRNAs. The optimal model was selected based on area under the curve (AUC) results obtained using 5-fold cross-validation.
resultsThe miRNA-based model accurately distinguished dogs with cancer from those without cancer, achieving an AUC of 0.907, with both sensitivity and specificity of 0.85. CONCLUSIONS AND CLINICAL IMPORTANCE: A model integrating NGS-derived miRNA profiles with machine learning can serve as a diagnostic approach for cancer detection in dogs. Such a model can distinguish dogs with cancer from both healthy dogs and those with non-malignant disease. These findings suggest that such a model could be used as a screening test for dogs with cancer in veterinary practice.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.