Development and validation of a prognostic prediction model for lumbar-disc herniation based on machine learning and fusion of clinical text data and radiomic features

OBJECTIVE: Based on preoperative clinical text data and lumbar magnetic resonance imaging (MRI), we applied machine learning (ML) algorithms to construct a model that would predict early recurrence in lumbar-disc herniation (LDH) patients who underwent percutaneous endoscopic lumbar discectomy (PELD). We then explored the clinical performance of this prognostic prediction model via multimodal-data fusion. METHODS: Clinical text data and radiological images of LDH patients who underwent PELD at the Intervertebral Disc Center of the Affiliated Hospital of Gansu University of Traditional Chinese Medicine (AHGUTCM; Lanzhou, China) were retrospectively collected. Two radiologists with clinical-image reading experience independently outlined regions of interest (ROI) on the MRI images and extracted radiomic features using 3D Slicer software. We then randomly separated the samples into a training set and a test set at a 7:3 ratio, used eight ML algorithms to construct predictive radiomic-feature models, evaluated model performance by the area under the curve (AUC), and selected the optimal model for screening radiomic features and calculating radiomic scores (Rad-scores). Finally, after using logistic regression to construct a nomogram for predicting the early-recurrence rate, we evaluated the nomogram's clinical applicability using a clinical-decision curve. RESULTS: We initially extracted 851 radiomic features. After constructing our models, we determined based on AUC values that the optimal ML algorithm was least absolute shrinkage and selection operator (LASSO) regression, which had an AUC of 0.76 and an accuracy rate of 91%. After screening features using the LASSO model, we predicted Rad-score for each sample of recurrent LDH using nine radiomic features. Next, we fused three of these clinical features -age, diabetes, and heavy manual labor-to construct a nomogram with an AUC of 0.86 (95% confidence interval [CI], 0.79-0.94). Analysis of the clinical-decision and impact curves showed that the prognostic prediction model with multimodal-data fusion had good clinical validity and applicability. CONCLUSION: We developed and analyzed a prognostic prediction model for LDH with multimodal-data fusion. Our model demonstrated good performance in predicting early postoperative recurrence in LDH patients; therefore, it has good prospects for clinical application and can provide clinicians with objective, accurate information to help them decide on presurgical treatment plans. However, external-validation studies are still needed to further validate the model's comprehensive performance and improve its generalization and extrapolation.