基于读出分段扩散加权成像的表观扩散系数直方图分析在鼻咽淋巴瘤与鼻咽癌鉴别诊断中的价值
Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging.
作者信息
Song Chengru, Cheng Peng, Cheng Jingliang, Zhang Yong, Xie Shanshan
机构信息
Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of radiotherapy, Henan Provincial People's Hospital, Zhengzhou, China.
出版信息
Front Oncol. 2021 Mar 12;11:632796. doi: 10.3389/fonc.2021.632796. eCollection 2021.
BACKGROUND
This study aims to explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC) following readout-segmented echo-planar diffusion-weighted imaging (RESOLVE sequence).
METHODS
Thirty-eight patients with NPL and 62 patients with NPC, who received routine head-and-neck MRI and RESOLVE (b-value: 0 and 1,000 s/mm) examinations, were retrospectively evaluated as derivation cohort (February 2015 to August 2018); another 23 patients were analyzed as validation cohort (September 2018 to December 2019). The RESOLVE data were obtained from the MAGNETOM Skyra 3T MR system (Siemens Healthcare, Erlangen, Germany). Fifteen parameters derived from the whole-lesion histogram analysis (ADC, variance, skewness, kurtosis, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, and ADC) were calculated for each patient. Then, statistical analyses were performed between the two groups to determine the statistical significance of each histogram parameter. A receiver operating characteristic curve (ROC) analysis was conducted to assess the diagnostic performance of each histogram parameter for distinguishing NPL from NPC and further tested in the validation cohort; calibration of the selected parameter was tested with Hosmer-Lemeshow test.
RESULTS
NPL exhibited significantly lower ADC, variance, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC and ADC, when compared to NPC (all, < 0.05), while no significant differences were found on skewness and kurtosis. Furthermore, ADC revealed the highest diagnostic efficiency, followed by ADC and ADC. Optimal diagnostic performance (AUC = 0.790, sensitivity = 91.9%, and specificity = 63.2%) could be achieved when setting ADC = 1,485.0 × 10 mm/s as the threshold value. The predictive performance was maintained in the validation cohort (AUC = 0.817, sensitivity = 94.6%, and specificity = 56.2%).
CONCLUSION
Whole-lesion ADC histograms based on RESOLVE are effective in differentiating NPC from NPL.
背景
本研究旨在探讨全病变表观扩散系数（ADC）直方图分析在读出分段回波平面扩散加权成像（RESOLVE序列）后鉴别鼻咽淋巴瘤（NPL）与鼻咽癌（NPC）中的应用价值。
方法
回顾性评估38例NPL患者和62例NPC患者，这些患者均接受了常规头颈MRI和RESOLVE（b值：0和1000 s/mm²）检查，作为推导队列（2015年2月至2018年8月）；另外23例患者作为验证队列进行分析（2018年9月至2019年12月）。RESOLVE数据来自MAGNETOM Skyra 3T MR系统（西门子医疗，德国埃尔朗根）。为每位患者计算从全病变直方图分析得出的15个参数（ADC、方差、偏度、峰度、ADC₁、ADC₂、ADC₃、ADC₄、ADC₅、ADC₆、ADC₇、ADC₈、ADC₉、ADC₁₀和ADC₁₁）。然后，对两组进行统计分析以确定每个直方图参数的统计学意义。进行受试者操作特征曲线（ROC）分析以评估每个直方图参数区分NPL与NPC的诊断性能，并在验证队列中进一步测试；使用Hosmer-Lemeshow检验对所选参数进行校准测试。
结果
与NPC相比，NPL的ADC、方差、ADC₁、ADC₂、ADC₃、ADC₄、ADC₅、ADC₆、ADC₇、ADC₈、ADC₉、ADC₁₀和ADC₁₁均显著降低（均P<0.05），而偏度和峰度无显著差异。此外，ADC显示出最高的诊断效率，其次是ADC₁和ADC₂。当将ADC = 1485.0×10⁻³mm²/s作为阈值时，可实现最佳诊断性能（AUC = 0.790，灵敏度 = 91.9%，特异性 = 63.2%）。预测性能在验证队列中得以维持（AUC = 0.817，灵敏度 = 94.6%，特异性 = 56.2%）。
结论
基于RESOLVE的全病变ADC直方图在鉴别NPC与NPL方面有效。
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