Background Measurement of bone mineral density (BMD) is the most important method to diagnose osteoporosis. However, current BMD measurement is always performed after a fracture has occurred. Purpose To explore whether a radiomic model based on abdominal computed tomography (CT) can predict the BMD of lumbar vertebrae. Material and Methods A total of 245 patients who underwent both dual-energy X-ray absorptiometry (DXA) and abdominal CT examination (training cohort, n = 196; validation cohort, n = 49) were included in our retrospective study. In total, 1218 image features were extracted from abdominal CT images for each patient. Combined with clinical information, three steps including least absolute shrinkage and selection operator (LASSO) regression were used to select key features. A two-tier stacking regression model with multi-algorithm fusion was used for BMD prediction, which can integrate the advantages of linear model and non-linear model. The prediction results of this model were compared with those using a single regressor. The degree-of-freedom adjusted coefficient of determination (Adjusted-R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the regression performance. Results Compared with other regression methods, the two-tier stacking regression model has a higher regression performance, with Adjusted-R2, RMSE, and MAE of 0.830, 0.077, and 0.06, respectively. Pearson correlation analysis and Bland–Altman analysis showed that the BMD predicted by the model had a high correlation with the DXA results (r = 0.932, difference = −0.01 ± 0.1412 mg/cm2). Conclusion Using radiomics, the BMD of lumbar vertebrae could be predicted from abdominal CT images.
Deep Learning based Segmentation of Lumbar Vertebrae from CT Images
