Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
目的: 探讨基于MRI迭代最小二乘法水脂分离定量技术（IDEAL-IQ）序列的列线图模型在预测早期骨量丢失中的诊断效能。
方法: 收集同时行双能X线骨密度仪（DXA）测定和腰椎MRI IDEAL-IQ序列检查的被试59例，应用DXA分别测定L1~4椎体的骨密度值，利用IDEAL-IQ序列中FF图测量相应的FF值。根据DXA结果将59例分为骨量正常组（n=23）和低骨量组（n=36）。应用两样本t检验比较2组椎体的FF值，通过ROC曲线评估诊断效能；使用多因素logistic回归筛选预测骨量丢失的独立因素，构建可视化列线图预测模型并评估其预测效能。
结果: 骨量正常组和低骨量组椎体FF值分别为（38.84±6.75）%、（51.96±7.65）%，经ROC曲线分析FF值鉴别骨量正常组和低骨量组的AUC为0.797，敏感度、特异度分别为73.91%、80.56%；多因素logistics回归分析显示FF值、年龄以及BMI指数是预测早期骨量丢失的独立因素。列线图模型AUC为0.954（95% CI：0.806-0.957），校正曲线显示预测概率与实际概率具有良好的一致性。决策曲线分析（DCA）显示列线图模型较之于单一的FF值鉴别模型，体现出更高的净获益。
结论: IDEAL-IQ序列能够反映椎体骨髓脂肪含量，而联合FF值、年龄以及BMI的列线图模型可进一步提高预测效能，并为临床早期骨量丢失的预测提供一种可视化手段。
Keywords: lumbar spine; magnetic resonance imaging; nomogram; osteoporosis.