Xing Xiaoying, Zhang Jiahui, Chen Yongye, Zhao Qiang, Lang Ning, Yuan Huishu
Department of Radiology, Peking University Third Hospital, Beijing, PR China.
Br J Radiol. 2020 Aug;93(1112):20190891. doi: 10.1259/bjr.20190891. Epub 2020 Jun 11.
To explore the value of related parameters in monoexponential, biexponential, and stretched-exponential models of diffusion-weighted imaging (DWI) in differentiating metastases and myeloma in the spine.
53 metastases and 16 myeloma patients underwent MRI with 10 b-values (0-1500 s/mm). Parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), the distribution diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) from DWI were calculated. The independent sample test and the Mann-Whiney test were used to compare the statistical difference of the parameter values between the two. Receiver operating characteristics (ROC) curve analysis was used to identify the diagnostic efficacy. Then substituted each parameter into the decision tree model and logistic regression model, identified meaningful parameters, and evaluated their joint diagnostic performance.
The ADC, D, and α values of metastases were higher than those of myeloma, whereas the D* value was lower than that of myeloma, and the difference was significant ( < 0.05); the area under the ROC curve for the above parameters was 0.661, 0.710, 0.781, and 0.743, respectively. There was no significant difference in the f and DDC values ( > 0.05). D and α were found to conform to the decision tree model, and the accuracy of model diagnosis was 84.1%. ADC and α were found to conform to the logistic regression model, and the accuracy was 87.0%.
The 3 models of DWI have certain values indifferentiating metastases and myeloma in spine, and the diagnostic performance of ADC, D, α and D*was better. Combining ADC with α may markedly aid in the differential diagnosis of the two.
Monoexponential, biexponential, and stretched-exponential models can offer additional information in the differential diagnosis of metastases and myeloma in the spine. Decision tree model and logistic regression model are effective methods to help further distinguish the two.
探讨扩散加权成像（DWI）单指数、双指数和拉伸指数模型中的相关参数在鉴别脊柱转移瘤和骨髓瘤中的价值。
53例转移瘤患者和16例骨髓瘤患者接受了具有10个b值（0 - 1500 s/mm²）的MRI检查。计算DWI的表观扩散系数（ADC）、真实扩散系数（D）、伪扩散系数（D*）、灌注分数（f）、分布扩散系数（DDC）和体素内水扩散异质性（α）等参数。采用独立样本t检验和Mann-Whiney U检验比较两者参数值的统计学差异。采用受试者工作特征（ROC）曲线分析确定诊断效能。然后将各参数代入决策树模型和逻辑回归模型，确定有意义的参数，并评估其联合诊断性能。
转移瘤的ADC、D和α值高于骨髓瘤，而D*值低于骨髓瘤，差异有统计学意义（P < 0.05）；上述参数的ROC曲线下面积分别为0.661、0.710、0.781和0.743。f和DDC值差异无统计学意义（P > 0.05）。发现D和α符合决策树模型，模型诊断准确率为84.1%。发现ADC和α符合逻辑回归模型，准确率为87.0%。
DWI的3种模型在鉴别脊柱转移瘤和骨髓瘤方面有一定价值，ADC、D、α和D*的诊断性能较好。联合ADC和α可能显著有助于两者的鉴别诊断。
单指数、双指数和拉伸指数模型可为脊柱转移瘤和骨髓瘤的鉴别诊断提供额外信息。决策树模型和逻辑回归模型是有助于进一步区分两者的有效方法。
