Kim H S, Kim S Y
Department of Diagnostic Radiology, Ajou University, School of Medicine, Mt. 5, Woncheon-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 442-749, Korea.
AJNR Am J Neuroradiol. 2007 Oct;28(9):1693-9. doi: 10.3174/ajnr.A0674. Epub 2007 Sep 20.
The purpose of this study was to determine whether qualitative and quantitative measures obtained with pulsed arterial spin-labeling (PASL) and apparent diffusion coefficients (ADC) improve glioma grading compared with conventional MR images.
We prospectively performed 2 qualitative consensus reviews in 33 suspected gliomas: 1) conventional MR images alone and 2) conventional MR images with PASL and ADC. To calculate the diagnostic performance parameters of PASL and ADC, we used a qualitative scoring system on the basis of the tumor perfusion signal intensity (sTP) and visual ADC scoring (sADC). We then analyzed quantitative regions of interest and calculated the ratio of the maximum tumor perfusion signal intensity (rTPmax) and the minimum ADC value (mADC).
Two observers diagnosed accurate tumor grades in 23 of 33 (70%) lesions in the first review and in 29 of 33 (88%) lesions in the second review. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for determining a glioma grading by using combined sTP and sADC scoring were 90.9, 90.9, 95.2, and 83.3%, respectively. Statistical analysis gave a threshold value of 1.24 for rTPmax and 0.98 x 10(-3) mm/s(2) for mADC to provide a sensitivity, specificity, PPV, and NPV of 95.5, 81.8, 91.3, and 90.1% and 90.9, 81.8, 90.9, and 81.8%, respectively. The receiver operator characteristic curve analyses showed no significant difference between the quantitative and combined qualitative parameters.
PASL and ADC significantly improve the diagnostic accuracy of glioma grading compared with conventional imaging.
本研究旨在确定与传统磁共振成像相比,脉冲动脉自旋标记(PASL)和表观扩散系数(ADC)所获得的定性和定量测量指标是否能改善胶质瘤分级。
我们对33例疑似胶质瘤患者进行了前瞻性的两项定性共识评估:1)仅使用传统磁共振成像;2)使用传统磁共振成像结合PASL和ADC。为计算PASL和ADC的诊断性能参数,我们基于肿瘤灌注信号强度(sTP)和视觉ADC评分(sADC)采用了定性评分系统。然后我们分析了感兴趣的定量区域,并计算了最大肿瘤灌注信号强度(rTPmax)与最小ADC值(mADC)的比值。
在第一次评估中,两位观察者对33个病变中的23个(70%)准确诊断出肿瘤分级,在第二次评估中对33个病变中的29个(88%)准确诊断出肿瘤分级。使用sTP和sADC联合评分确定胶质瘤分级的敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)分别为90.9%、90.9%、95.2%和83.3%。统计分析得出rTPmax的阈值为1.24,mADC的阈值为0.98×10⁻³mm/s²,其敏感性、特异性、PPV和NPV分别为95.5%、81.8%、91.3%和90.1%,以及90.9%、81.8%、90.9%和81.8%。受试者操作特征曲线分析显示定量参数与联合定性参数之间无显著差异。
与传统成像相比,PASL和ADC显著提高了胶质瘤分级的诊断准确性。