Application of radiomics model based on lumbar computed tomography in diagnosis of elderly osteoporosis

Abstract

A metabolic bone disease characterized by decreased bone formation and increased bone resorption is osteoporosis. It can cause pain and fracture of patients. The elderly are prone to osteoporosis and are more vulnerable to osteoporosis. In this study, radiomics are extracted from computed tomography (CT) images to screen osteoporosis in the elderly. Collect the plain scan CT images of lumbar spine, cut the region of interest of the image and extract radiomics features, use Lasso regression to screen variables and adjust complexity, use python language to model random forests, support vector machines, K nearest neighbor, and finally use receiver operating characteristic curve to evaluate the performance of the model, including precision, recall, accuracy and area under the curve (AUC). For the model, 14 radiolomics features were selected. The diagnosis performance of random forest model and support vector machine is good, all around 0.9. The AUC of K nearest neighbor model in training set and test set is 0.828 and 0.796, respectively. We selected the plain scan CT images of the elderly lumbar spine to build radiomics features model, which has good diagnostic performance and can be used as a tool to assist the diagnosis of osteoporosis in the elderly.

Keywords: CT images; elderly; machine learning; model prediction; osteoporosis.