Prediction of low back fasciitis by machine learning method based on radiomics

Abstract Purpose: The aim of this study was to analyze lumbar fascial changes in patients with lower back pain using lumbar spine soft tissue-based MR imaging radiomics. Methods: We retrospectively analyzed the lumbar MRI of 380 patients with low back pain. Patients were randomly assigned to either the training (n = 187) or validation (n = 193) cohorts. Seven different machine learning algorithms were used to establish classification prediction models, and their prediction efficiency were evaluated with the best performance was selected as the final classification prediction model. Multivariate logistic regression analysis was used to create radiomics model and combined nomogram model and their predictive performance were evaluated using receiver operating characteristic (ROC) curves. Results:Among the seven machine learning models, the Lasso model exhibited the highest diagnostic efficiency with an AUC of 0.835. The radiomics nomogram integrated clinical and radiomics signature features, demonstrating strong performance in both the training and validation sets with AUC values of 0.97 and 0.96, respectively. AUC and DCA indicated that the radiomics nomogram model effectively diagnosed lumbar fasciitis. Conclusion: We developed an nomogram model that integrated clinical and radiomics features to help clinicians identify and predict low back fasciitis through soft tissue magnetic resonance.