Value of a nomogram model based on IDEAL-IQ for predicting early bone mass loss

Abstract

To assess the diagnostic efficiency of a nomogram model based on iterative decomposition of water and fat with echo asymmetry and least-squares estimation- iron quantification (IDEAL- IQ) for predicting early bone loss of the lumbar vertebrae. Fifty-nine volunteers and patients with osteoporosis underwent examinations with both dual-energy X-ray absorptiometry (DXA) to determine bone mineral density (BMD) of L1-4 vertebrae and lumbar magnetic resonance imaging (MRI) with IDEAL-IQ sequence for measurement of bone marrow FF of L1-4 vertebrae. According to the results of DXA, the subjects were divided into normal bone mass group (n=23) and osteopenia group (n=36). The FF values of the two groups were compared and the diagnostic efficacy of the FF value was evaluated using ROC curve analysis. Multivariate logistic regression analysis was used to identify the independent factors for predicting bone mass loss, and a visual nomogram model was constructed and its diagnostic efficiency was assessed. The FF value of the vertebrae was significant lower in normal bone mass group than in osteopenia group [(38.84±6.75)% vs (51.96±7.65)%, P < 0.05). ROC curve analysis showed that the AUC of the FF value for differentiating normal bone mass and osteopenia was 0.797 with a cutoff value of 46.85%, a sensitivity of 73.91% and a specificity of 80.56%. Multivariate logistics regression analysis identified the FF value, age and BMI as the independent factors for predicting bone mass loss. The diagnostic AUC of the nomogram model was 0.954 (95% CI: 0.806-0.957), and the predicted probability of the model was in good agreement with the actual probability. Decision curve analysis showed that the nomogram model could provide more net benefit than the FF vale alone. FF value of MRI IDEAL- IQ sequence can reflect bone marrow fat content of the vertebral body, and the nomogram model incorporating the FF value, age, and BMI can further improve the predictive efficiency to provide a visual modality for predicting early bone mass loss.