Prediction of cancer-associated thrombosis by machine learning: results from the Vienna Cancer and Thrombosis Study.
Clinical evidence update from ESMO open
Clinical Context
Peer-reviewed veterinary literature continues to shape everyday decision-making for feline patients, especially when new evidence clarifies diagnosis, treatment selection, monitoring, or clinical outcomes.
What the Study Evaluated
A study published in ESMO open in 2026 evaluated prediction of cancer-associated thrombosis by machine learning: results from the Vienna Cancer and Thrombosis Study..
Key Findings
Improved risk prediction of cancer-associated venous thromboembolism (VTE) remains an unmet clinical need. We aimed to apply machine learning for the prediction of cancer-associated VTE. Data from the Vienna Cancer and Thrombosis Study (Vienna-CATS), a prospective cohort study that included patients with cancer from 2003-2019, were used. Patient characteristics and laboratory measurements (including various routine and experimental laboratory assays) were used to train and validate six classification models for VTE prediction. Monte Carlo cross-validation was conducted, with 80% of the samples randomly assigned to the training set and 20% to...
Why It Matters for Veterinary Professionals
For veterinary professionals, the practical value of this work lies in how the findings may support more structured clinical assessment, clearer monitoring, and more informed decisions for feline patients.
Practical Interpretation
The results should be interpreted in the context of the study design, population, inclusion criteria, and clinical setting. Application in practice should consider patient-specific risk factors, available diagnostics, local standards of care, and clinician judgment.
Clinical Takeaway
Overall, the study adds useful evidence for clinicians seeking to align daily practice with current veterinary research while maintaining a balanced, case-by-case approach.
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