Application of Nomogram Based on Radiomics Label in Prediction of Treatment Scheme for Lumbar Disc Herniation

Abstract

Objective: To investigate and verify the efficiency and effectiveness the the value of nomogram based on radiomics label in predicting the treatment of lumbar disc herniation (LDH). Methods: The clinical medical records and imaging data of 200 patients with LDH diagnosed in the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine in the past 3 years have been analyzed retrospectively. The collected cases are randomly divided into a training group (n=140) and a test group (n=60) with ratio of 7:3. Two radiologists with experience in reading orthopaedics images are independently segmented ROI, the whole intervertebral disc with the most obvious protrusion in the sagittal plane T2WI of lumbar MRI as a mask (ROI) are sketched. The LASSO algorithm is used to filter the features after extracting the radiomics features. The multivariate Logistic regression model is used to construct a quantitative imaging Rad‑Score for the selected features with non-zero coefficients. The radiomics labels and nomogram were evaluated using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The calibration curve is used to evaluate the consistency between the nomogram prediction and the actual treatment plan. The DCA decision curve is used to evaluate the clinical applicability of nomogram. Result: Following the feature extraction, 11 radiomics features are used to construct the radiomics label for predicting the treatment plan of LDH, The nomogram was then constructed. The AUC was 0.930 (95%CI:0.865-0.995) with a sensitivity of 96.8% and a specificity of 90%. The calibration curve shows that there is a good consistency between the prediction and the actual observation. The DCA decision curve analysis shows that the nomogram of imaging group has a great potential for clinical application when the risk threshold is over 10%. Conclusion: The nomogram based on radiomics label has a good predictive value for the treatment of LDH and therefore can be used as a reference for clinical decision-making.