Lu Shan Shan, Kim Sang Joon, Kim Namkug, Kim Ho Sung, Choi Choong Gon, Lim Young Min
1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 138-736, Korea.
AJR Am J Roentgenol. 2015 Apr;204(4):827-34. doi: 10.2214/AJR.14.12677.
This study intended to investigate the usefulness of histogram analysis of apparent diffusion coefficient (ADC) maps for discriminating primary CNS lymphomas (PCNSLs), especially atypical PCNSLs, from tumefactive demyelinating lesions (TDLs).
Forty-seven patients with PCNSLs and 18 with TDLs were enrolled in our study. Hyperintense lesions seen on T2-weighted images were defined as ROIs after ADC maps were registered to the corresponding T2-weighted image. ADC histograms were calculated from the ROIs containing the entire lesion on every section and on a voxel-by-voxel basis. The ADC histogram parameters were compared among all PCNSLs and TDLs as well as between the subgroup of atypical PCNSLs and TDLs. ROC curves were constructed to evaluate the diagnostic performance of the histogram parameters and to determine the optimum thresholds.
The differences between the PCNSLs and TDLs were found in the minimum ADC values (ADCmin) and in the 5th and 10th percentiles (ADC5% and ADC10%) of the cumulative ADC histograms. However, no statistical significance was found in the mean ADC value or in the ADC value concerning the mode, kurtosis, and skewness. The ADCmin, ADC5%, and ADC10% were also lower in atypical PCNSLs than in TDLs. ADCmin was the best indicator for discriminating atypical PCNSLs from TDLs, with a threshold of 556×10(-6) mm2/s (sensitivity, 81.3 %; specificity, 88.9%).
Histogram analysis of ADC maps may help to discriminate PCNSLs from TDLs and may be particularly useful in differentiating atypical PCNSLs from TDLs.
本研究旨在探讨表观扩散系数（ADC）图的直方图分析在鉴别原发性中枢神经系统淋巴瘤（PCNSL），尤其是非典型PCNSL与瘤样脱髓鞘病变（TDL）中的作用。
本研究纳入了47例PCNSL患者和18例TDL患者。在将ADC图与相应的T2加权图像配准后，将T2加权图像上的高信号病变定义为感兴趣区（ROI）。从包含每个层面整个病变的ROI以及逐像素基础上计算ADC直方图。比较所有PCNSL和TDL以及非典型PCNSL和TDL亚组之间的ADC直方图参数。构建ROC曲线以评估直方图参数的诊断性能并确定最佳阈值。
在PCNSL和TDL之间发现最小ADC值（ADCmin）以及累积ADC直方图的第5和第10百分位数（ADC5%和ADC10%）存在差异。然而，平均ADC值以及关于众数、峰度和偏度的ADC值未发现统计学意义。非典型PCNSL中的ADCmin、ADC5%和ADC10%也低于TDL。ADCmin是区分非典型PCNSL与TDL的最佳指标，阈值为556×10(-6) mm2/s（敏感性，81.3%；特异性，88.9%）。
ADC图的直方图分析可能有助于鉴别PCNSL与TDL，并且在区分非典型PCNSL与TDL方面可能特别有用。
