# Deep learning-radiomics assessment of intervertebral disc and paraspinal muscle heterogeneity for predicting postoperative recurrent lumbar disc herniation

## TL;DR

A deep learning-radiomics model using intervertebral disc features effectively predicts postoperative recurrence of lumbar disc herniation.

## Contribution

A novel DL-radiomics model combining disc and muscle features is proposed for predicting postoperative recurrence of lumbar disc herniation.

## Key findings

- Disc Radscore showed strong predictive ability (AUC 0.857) for postoperative recurrence.
- Muscle Radscore had moderate performance (AUC 0.718), while Pfirrmann grade was poor (AUC 0.506).
- Combined disc-muscle analysis was less stable than disc Radscore alone.

## Abstract

Although imaging and paraspinal muscle parameters are linked to postoperative recurrent lumbar disc herniation (PRLDH), micro-level texture characteristics and their interactions remain underexplored. This study applied deep learning (DL)-radiomics to quantify the microstructural heterogeneity of responsible intervertebral discs and paraspinal muscles (L3-S1), and assessed a combined disc-muscle model for predicting PRLDH.

Clinical and imaging data from 170 lumbar disc herniation (LDH) patients undergoing percutaneous transforaminal endoscopic surgery (Jan 2022-Dec 2024) were retrospectively analyzed. DL and radiomics features were extracted from intervertebral discs and paraspinal muscles. Feature selection via mutual information was followed by construction of a DL-radiomics Radscore model. Internal validation used leave-one-out, 10-fold cross-validation, and bootstrapping. Pfirrmann grading performance was compared with the disc Radscore, and potential disc-muscle interactions were explored using optimal cutoffs.

Among 170 patients, 39 had postoperative recurrence. Disc Radscore included 2 DL and 3 radiomics features, while muscle Radscore comprised 2 DL and 5 radiomics features. The disc Radscore demonstrated good predictive ability (AUC 0.857, 95% CI 0.797–0.918) across validation methods (AUC 0.846–0.857). Muscle Radscore showed moderate performance (AUC 0.718, 95% CI 0.627–0.809). Pfirrmann grade poorly predicted recurrence (AUC 0.506, 95% CI 0.412–0.600). Combined disc-muscle analysis was less stable than disc Radscore alone.

DL-radiomics-derived intervertebral disc Radscore robustly predicts PRLDH. While combined disc-muscle assessment is less consistent, their interactions may inform postoperative risk stratification and management in LDH patients.