Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density

Osteoporosis is associated with a decrease of bone mineralized component as well as a increase of bone marrow fat. At present, there are few studies using radiomics nomogram based fat-water material decomposition (MD) images of dual-energy spectral CT as an evaluation method of abnormally low Bone Mineral Density (BMD). This study aims to establish and validate a radiomics nomogram based the fat-water imaging of dual-energy spectral CT in diagnosing low BMD.

Ninety-five patients who underwent dual-energy spectral CT included T11-L2 and dual x-ray absorptiometry (DXA) were collected. The patients were divided into two groups according to T-score, normal BMD(T ≥ -1) and abnormally low BMD (T < -1). Radiomic features were selected from fat-water imaging of the dual-energy spectral CT. Radscore was calculated by summing the selected features weighted by their coefficients. A nomogram combining the radiomics signature and significant clinical variables was built. The ROC curve was performed to evaluate the performance of the model. Finally, we used decision curve analysis (DCA) to evaluate the clinical usefulness of the model.

Five radiomic features based on fat-water imaging of dual-energy spectral CT were constructed to distinguish abnormally low BMD from normal BMD, and its differential performance was high with an area under the curve (AUC) of 0.95 (95% CI, 0.89-1.00) in the training cohort and 0.97 (95% CI, 0.91-1.00) in the test cohort. The radiomics nomogram showed excellent differential ability with AUC of 0.96 (95%CI, 0.91-1.00) in the training cohort and 0.98 (95%CI, 0.93-1.00) in the test cohort, which performed better than the radiomics model and clinics model only. The DCA showed that the radiomics nomogram had a higher benefit in differentiating abnormally low BMD from normal BMD than the clinical model alone.

The radiomics nomogram incorporated radiomics features and clinical factor based the fat-water imaging of dual-energy spectral CT may serve as an efficient tool to identify abnormally low BMD from normal BMD well.