ArticleVeterinary sciences2024
Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.
Article in Veterinary sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- Trimodal cytological integration of micronuclei assay, argyrophilic nucleolar organizer region staining, and cytomorphometry enhances diagnostic discrimination of canine gingival masses.Veterinary world · 2026Article
- Interpretable machine learning classification of cedar and cypress pollen on routine Durham slides for environmental exposure assessment.Frontiers in allergy · 2026Article
- Innovations, Applications, and Future Trends in Veterinary Diagnostic Technologies.Transboundary and emerging diseases · 2026Review
- The application of artificial intelligence in veterinary oncology: a scoping review.BMC veterinary research · 2025Article
- Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry.Veterinary pathology · 2025Article
Corrections and comments
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Authors and funding
22 authors.
Funding
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Abstract
The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists' NP estimates (
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Registered trials
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