Utilization of radiomics model derived from lumbar CT images for grading the diagnosis of osteoarthritis in facet joints

Purpose Develop machine learning models utilizing computed tomography (CT) and the weishaupt grading criteria to assess the degeneration severity of facet joint of osteoarthritis (FJOA). Methods The machine learning model utilizes features extracted from patient Lumbar CT at the First People's Hospital of Nantong. Use 3D Slicer software to perform semi-automatic image segmentation on CT images and extract radiological features from the segmented regions. Preliminary screening of radiomic features extracted by radiomics using t-test and rank sum test with p<0.05 as the standard. Based on the core features selected by Lasso regression, construct random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) models. Use receiver operating characteristic (ROC) curves to evaluate the model's performance, considering metrics such as accuracy, recall, precision, F1 score, and area under curve (AUC). Results The radiomics package of 3D Slicer extracted 1037 radiomic features from ROI. The T-test combined with rank sum test preliminarily screened 589 radiomics features with statistical differences. Subsequently, Lasso regression was used to identify 28 core features. Develop machine learning models based on 28 core feature selections of RF, SVM, and KNN. The AUCs of RF model, SVM model and KNN model in the training set were 0.783, 0.803 and 0.693 respectively, and those in the validation set were 0.699, 0.719 and 0.671 respectively. Conclusion The machine learning model utilizing lumbar CT images can effectively assess lumbar facet joint degeneration. Through this model, diseases can be classified and diagnosed, and doctors can develop personalized treatment plans.