MRI联合最小表观弥散系数值对原发性中枢神经系统淋巴瘤诊断与鉴别的价值

The value of MRI combined with minimum apparent diffusion coefficient value in diagnosis and differentiation of primary central nervous system lymphoma

摘要:

目的：在logistic回归模型下探讨常规MRI征象联合最小表观弥散系数值（minimum apparent diffusion coefficient value，ADCmin）对原发性中枢神经系统淋巴瘤（primary central nervous system lymphoma，PCNSL）诊断与鉴别的价值。方法：收集34例PCNSL临床及影像资料，所有病例均经临床及病理证实，观察分析常规MRI特征及检测ADCmin，另取36例高级别胶质瘤（high-grade glioma，HGG）作为对照组，统计学分析2组肿瘤各个MRI特征、ADCmin之间的差异，采用logistic回归分析，绘制受试者工作特征（receiver operating characteristics，ROC）曲线分析比较各指标单独诊断及联合诊断对2组肿瘤的诊断效能。结果：PCNSL组的囊变（或）坏死（或）出血征象所占比率明显小于HGG组（?字2=25.2，P＜0.001）；PCNSL组在边界清晰、累及中线结构或（和）脑室、均匀强化、存在“缺口征”和（或）“尖角征”、扩散加权成像（diffusion-weighted imaging，DWI）高信号征象上所占比率大于HGG组（均P＜0.05）；2组肿瘤瘤周水肿程度及占位程度无统计学差异（P >0.05）。PCNSL组ADCmin明显小于HGG组ADCmin（t=-7.962，P＜0.001），而2组对照侧ADC相比较无统计学意义（t=1.208，P=0.231）。ADCmin单独诊断时，以ADCmin=0.727×10-3 mm2/s为诊断2组的最佳诊断阈值，鉴别诊断2组的敏感度、特异度分别为97.1%、80.6%，曲线下面积最大（AUC=0.922），诊断效能最高。logistic回归方程模型为：Logistic（P）=15.269+3.963×均匀强化-24.695×ADCmin。ADCmin和均匀强化均是诊断PCNSL的影响因素，联合诊断最佳阈值为 0.46时，诊断PCNSL的AUC为0.977，鉴别诊断2组的敏感度、特异度分别为97.1％、94.4％（P<0.001），联合诊断效能最高。结论：常规MRI特征分析联合ADCmin为无创性鉴别PCNSL与HGG提供形态学及分子影像学依据，特别是对于常规MRI存在重叠表现的2种肿瘤，运用logistic回归模型下联合诊断可有效提高诊断效能，从而为两者的鉴别诊断、早期干预、及时调整患者治疗方案及判断预后提供可靠依据。

Abstract:

Objective：To investigate the value of conventional MRI signs combined with minimum apparent diffusion coefficient value（ADCmin） in the diagnosis and differentiation of primary central nervous system lymphoma（PCNSL） under logistic regression model. Methods：The conventional MRI characteristics and ADCmin of 34 cases of PCNSL confirmed by clinical and pathology were observed and analyzed. Another 36 cases of high-grade glioma（HGG） were selected as the control group. The differences in MRI characteristics and ADCmin between the two groups were statistically analyzed and compared，and logistic regression analysis was performed. The receiver operating characteristics（ROC） curve was used to analyze and compare the efficacy of single diagnosis and combined diagnosis for the two groups of tumors. Results：The proportions of cystic degeneration，necrosis and hemorrhage in PCNSL group were significantly lower than those in HGG group（?字2=25.2，P<0.001）. The proportion of PCNSL group in clear boundary，involving midline structure or（and） ventricle，uniform enhancement，existence of “notch sign” and（or） “sharp angle sign” and high signal signs on diffusion-weighted imaging（DWI） was higher than that of HGG group（all P<0.05）. There was no significant difference between the two groups in the degree of peritumoral edema and the degree of mass occupation（P >0.05）. The ADCmin of PCNSL group was significantly lower than that of HGG group（t=-7.962，P<0.001），while there was no statistical significance in the ADC value of the control side between the two groups（t=1.208，P=0.231）. When ADCmin was diagnosed separately，the optimal diagnostic cut-off point for the two groups was ADCmin=0.727×10-3 mm2/s，and the sensitivity and specificity of the two groups for differential diagnosis were 97.1% and 80.6%，respectively. The area under the curve was the largest（AUC=0.922） and the diagnostic efficiency was the highest. Logistic regression equation model was：Logistic（P）=15.269+3.963×uniform enhancement -24.695×ADCmin. Both ADCmin and homogeneous enhancement were influential factors for the diagnosis of PCNSL. When the optimal threshold of combined diagnosis was 0.46，the AUC of PCNSL was 0.977. The sensitivity and specificity of the two groups for differential diagnosis were 97.1% and 94.4%（P<0.001），with the highest efficacy of combined diagnosis. Conclusion：Routine MRI feature analysis combined with ADCmin can provide morphological and molecular imaging basis for non-invasive differentiation of PCNSL and HGG. Especially for the two tumors with overlapping manifestations of conventional MRI，the combined diagnosis under Logistic regression model can effectively improve the diagnostic efficiency，thus providing early diagnosis for patients with different tumors in the two groups，so as to provide reliable basis for early intervention，timely adjustment of treatment plan and judgment of prognosis value.