Bai Yan, Liu Taiyuan, Chen Lijuan, Gao Haiyan, Wei Wei, Zhang Ge, Wang Lifu, Kong Lingfei, Liu Siyun, Liu Huan, Roberts Neil, Wang Meiyun
Department of Medical Imaging, Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Department of Pathology, Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou, China.
Front Oncol. 2021 Oct 12;11:672265. doi: 10.3389/fonc.2021.672265. eCollection 2021.
To explore the efficacy of diffusion weighted imaging (DWI)-derived metrics under different models as surrogate indicators for molecular biomarkers and tumor microenvironment in gliomas.
A retrospective study was performed for 41 patients with gliomas. The standard apparent diffusion coefficient (ADC) and ADC under ultra-high values (ADC) ( values: 2500 to 5000 s/mm) were calculated based on monoexponential model. The fraction of fast diffusion (), pseudo ADC (ADC) and true ADC (ADC) were calculated by bi-exponential model ( values: 0 to 2000 s/mm). The apparent diffusional kurtosis (K) was derived from the simplified diffusion kurtosis imaging (DKI) model ( values: 200 to 3000 s/mm). Potential correlations between DWI parameters and immunohistological indices (i.e. Aquaporin (AQP)1, AQP4, AQP9 and Ki-67) were investigated and DWI parameters were compared between high- and low-grade gliomas, and between tumor center and peritumor. Receiver operator characteristic (ROC) curve and area under the curve (AUC) were calculated to determine the performance of independent or combined DWI parameters in grading gliomas.
The ADC and ADC at tumor center showed a stronger correlation with Ki-67 than other DWI metrics. The ADC, ADC and ADC at tumor center presented correlations with AQP1 and AQP4 while AQP9 did not correlate with any DWI metric. K showed a correlation with Ki-67 while no significant correlation with AQPs. ADC ( < 0.001) and ADC ( = 0.001) were significantly lower while the ADC ( = 0.006) and K ( = 0.005) were significantly higher in the high-grade than in the low-grade gliomas. ADC, , ADC, ADC, ADC, K at the tumor center had significant differences with those in peritumor when the gliomas grade became high ( < 0.05). Involving ADC and K simultaneously into an independent ADC model (AUC = 0.833) could further improve the grading performance (ADC+ADC+K: AUC = 0.923).
Different DWI metrics fitted within different -value ranges (low to ultra-high values) have different efficacies as a surrogate indicator for molecular expression or microstructural complexity in gliomas. Further studies are needed to better explain the biological meanings of these DWI parameters in gliomas.
探讨不同模型下扩散加权成像（DWI）衍生指标作为胶质瘤分子生物标志物和肿瘤微环境替代指标的效能。
对41例胶质瘤患者进行回顾性研究。基于单指数模型计算标准表观扩散系数（ADC）和超高值下的ADC（值：2500至5000 s/mm²）。通过双指数模型（值：0至2000 s/mm²）计算快速扩散分数（）、伪ADC（ADC）和真ADC（ADC）。表观扩散峰度（K）由简化扩散峰度成像（DKI）模型（值：200至3000 s/mm²）得出。研究DWI参数与免疫组织学指标（即水通道蛋白（AQP）1、AQP4、AQP9和Ki-67）之间的潜在相关性，并比较高级别和低级别胶质瘤以及肿瘤中心与瘤周之间的DWI参数。计算受试者工作特征（ROC）曲线和曲线下面积（AUC），以确定独立或联合DWI参数在胶质瘤分级中的性能。
肿瘤中心的ADC和ADC与Ki-67的相关性比其他DWI指标更强。肿瘤中心的ADC、ADC和ADC与AQP1和AQP4存在相关性，而AQP9与任何DWI指标均无相关性。K与Ki-67存在相关性，与水通道蛋白无显著相关性。高级别胶质瘤的ADC（<0.001）和ADC（=0.001）显著低于低级别胶质瘤，而ADC（=0.006）和K（=0.005）显著高于低级别胶质瘤。当胶质瘤分级较高时，肿瘤中心的ADC、、ADC、ADC、ADC、K与瘤周的这些参数存在显著差异（<0.05）。将ADC和K同时纳入独立的ADC模型（AUC = 0.833）可进一步提高分级性能（ADC+ADC+K：AUC = 0.923）。
在不同值范围（低至超高值）内拟合的不同DWI指标作为胶质瘤分子表达或微观结构复杂性的替代指标具有不同的效能。需要进一步研究以更好地解释这些DWI参数在胶质瘤中的生物学意义。
