Construction of a predictive model for chronic nonspecific low back pain based on clinical features and radiomics and its application values

English Keywords:Chronic nonspecific low back pain  Paraspinal muscles  Radiomics  Artificial intelligence  Multimodal data integration

English Abstract:

  【Abstract】 Objectives: To construct and validate a predictive model for chronic nonspecific low back pain(cNLBP) based on clinical and radiomics features through artificial intelligence technology. Methods: A total of 148 patients with cNLBP admitted to the First Affiliated Hospital of Dalian Medical University from May 2023 to May 2025 included retrospectively analyzed in this study(62 males, 86 females; aged 15-78 years). According to the visual analog scale(VAS) score of low back pain, the patients were divided into a mild pain group(n=52, VAS score<5) and a severe pain group(n=96, VAS score≥5). Clinical features, including lumbar lordosis angle, L4 vertebral cross-sectional area(CSA), L4-5 bilateral facet joint orientation angle, as well as the CSA, fat infiltration area, fat infiltration rate, and mean gray value of the L4-5 multifidus(MF) and erector spinae(ES), were obtained via MRI examination. Univariate and multivariate logistic regression analyses were employed to screen clinical features affecting pain severity and to construct a clinical model. Radiomics features of the MF and ES were extracted from lumbar MRI, comprising 13 categories: first-order statistics, 3D shape-based features, gray level co-occurrence matrix(GLCM), gray level dependence matrix(GLDM), gray level run length matrix(GLRLM), gray level size zone matrix(GLSZM), neighborhood gray tone difference matrix(NGTDM), and their wavelet transform, log-sigma feature, logarithm, square, square root, and local binary pattern(LBP) features. Key features highly correlated with pain grading were screened using the minimum redundancy maximum relevance(mRMR) method and least absolute shrinkage and selection operator(LASSO) regression. The data were randomly divided into a training set(n=119) and a test set(n=29) at a ratio of 8∶2. The training set was used for model training, while the test set was used to evaluate model performance and generalization ability. Multiple radiomics models for MF and ES were separately constructed using various artificial intelligence algorithms. The optimal radiomics models for MF/ES were selected based on performance metrics such as area under the curve(AUC), accuracy, sensitivity, and specificity. The clinical model was then combined with the optimal MF and ES radiomics models respectively to construct corresponding combined models. Model performance and stability were evaluated by calculating AUC, accuracy, sensitivity, and specificity, while predictive efficacy was assessed by plotting ROC curves, calibration curves, and decision curves. Results: Among the collected clinical features, univariate and multivariate logistic regression analyses showed that the right MF fat infiltration rate(OR=0.758, P<0.05) and left MF fat infiltration rate(OR=1.418, P<0.05) were independent predictors of pain severity in cNLBP patients. A clinical model was constructed based on these findings. In terms of radiomics, 8 key features for MF and 7 key features for ES were selected via the LASSO algorithm to construct respective radiomics models. The further constructed combined model based on MF features achieved an AUC of 0.8081, and the combined model based on ES features achieved an AUC of 0.8586, demonstrating better discrimination ability and clinical utility than single models. Decision curve analysis(DCA) indicated that compared to the single clinical model, the combined models provided higher net benefits across most threshold probability ranges. Calibration curve analysis showed good consistency between the predicted probabilities of the combined models and actual observations. Conclusions: Imaging indicators such as fatty infiltration of the MF are closely related to pain severity in cNLBP patients.