# Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs

## TL;DR

This paper develops a machine learning model using CT scans and pain perception to predict recovery in paralyzed dogs after spinal surgery.

## Contribution

A novel machine learning model combining CT radiomics and pain perception to predict functional recovery in paraplegic dogs.

## Key findings

- The model achieved 86.1% accuracy in predicting recovery of ambulation in paraplegic dogs.
- The model outperformed deep-pain perception alone in predicting recovery outcomes.
- Noncontrast CT radiomics provided significant prognostic information for spinal injury recovery.

## Abstract

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 (Testfull) and on the test set of DPN dogs (TestDPN). 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 Testfull, 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 TestDPN. 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.