Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs
Year: 2025
Type: article
Abstract: Objective To 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 design Multivariable prediction model development. Sample population Paraplegic dogs with acute IVDE: 128 deep‐pain positive and 86 deep‐pain negative (DPN). Methods Radiomics 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 full ) and on the test set of DPN dogs (Test DPN ). Deep‐pain perception alone served as the control. Results Recovery 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 full , and an AUC of .7692 (95% CI: .6250–.9000), accuracy of 72.7% (95% CI: 50.0%–90.9%), sensitivity of 53.8% (95% CI: 25.0%–80.0%), and specificity of 100.0% (95% CI: 100.0%–100.0%) on Test DPN . Deep‐pain perception had an AUC of .8088 (95% CI: .7273–.8871), accuracy of 69.8% (95% CI: 55.8%–83.7%), sensitivity of 61.8% (95% CI: 45.5%–77.4%), and specificity of 100.0% (95% CI: 100.0%–100.0%), which was different from that of the model ( p = .02). Conclusion Noncontrast 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.
Source: Veterinary Surgery
DOI: 10.1111/vsu.70016
Authors: Daniel Low, Scott Rutherford
Language: English
Subfield: Small Animals
Field: Veterinary
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,000
OpenAlex ID: W4414745607