ArticleVeterinary surgery : VS2025
Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs.
Article in Veterinary surgery : VS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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- Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs.Veterinary surgery : VS · 2025Article
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Abstract
objectiveTo develop a computed tomography (CT)-radiomics-based machine-learning algorithm for prediction of functional recovery in paraplegic dogs with acute intervertebral disc extrusion (IVDE). STUDY
designMultivariable prediction model development. SAMPLE POPULATION: Paraplegic dogs with acute IVDE: 128 deep-pain positive and 86 deep-pain negative (DPN).
methodsRadiomics features from noncontrast CT were combined with deep-pain perception in an extreme gradient algorithm using an 80:20 train-test split. Model performance was assessed on the independent test set (Test
resultsRecovery of ambulation was recorded in 165/214 dogs (77.1%) after decompressive surgery. The model had an area under the receiver operating characteristic curve (AUC) of .9118 (95% CI: .8366-.9872), accuracy of 86.1% (95% CI: 74.4%-95.4%), sensitivity of 82.4% (95% CI: 68.6%-93.9%), and specificity of 100.0% (95% CI: 100.0%-100.0%) on Test
conclusionNoncontrast CT-based radiomics provided prognostic information in dogs with severe spinal cord injury secondary to acute intervertebral disc extrusion. The model outperformed deep-pain perception alone in identifying dogs that recovered ambulation following decompressive surgery. CLINICAL SIGNIFICANCE: Radiomics features from noncontrast CT, when integrated into a multimodal machine-learning algorithm, may be useful as an assistive tool for surgical decision making.
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