Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in...

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).

Multivariable prediction model development.

Paraplegic dogs with acute IVDE: 128 deep-pain positive and 86 deep-pain negative (DPN).

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.

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).

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.

Radiomics features from noncontrast CT, when integrated into a multimodal machine-learning algorithm, may be useful as an assistive tool for surgical decision making.