弥散张量成像在术前预测胶质瘤患者异柠檬酸脱氢酶状态中的作用

Role of diffusion tensor imaging in the preoperative prediction of isocitrate dehydrogenase status in patients with glioma

摘要

目的 通过结合弥散张量成像(diffusion tensor imaging, DTI)及其他临床与影像学参数,为术前预测胶质瘤异柠檬酸脱氢酶(isocitrate dehydrogenase, IDH)状态提供依据。方法 采用回顾性研究,收集了2020年9月至2025年1月在西安交通大学第一附属医院完善术前MRI检查并经术后病理确诊的胶质瘤患者。在T2FLAIR图像中的肿瘤区域和正常脑白质(normal appearing white matter, NAWM)区域放置感兴趣区(region of interest, ROI),分析ROI内的各向异性分数(fractional anisotropy, FA)和平均扩散率(mean diffusivity, MD)。同时使用沿血管周围间隙的DTI(DTI along the perivascular space, DTI-ALPS)评估颅内类淋巴系统功能。通过常规MRI图像对肿瘤是否囊变及位置进行分类；通过蒙特利尔认知评估量表(MoCA)和中性粒细胞淋巴细胞比值(neutrophil-lymphocyte ratio, NLR)分别评估认知功能和机体炎症反应。将具有统计学差异(P＜0.05)的预测变量纳入二元Logistic分析并建立模型,对胶质瘤的IDH状态进行分类。结果 研究共纳入65例胶质瘤患者,包括27例IDH突变型及38例IDH野生型患者。与IDH突变型胶质瘤患者相比,IDH野生型胶质瘤患者的FA_MEAN/NAWM、NLR及MoCA得分较高,MD_MIN、MD_MEAN/NAWM及DTI-ALPS指数较低。将上述因素纳入到二元Logistic回归分析并建立分类模型,受试者工作特征曲线分析显示的曲线下面积为0.883(95% CI:0.803～0.963)。结论 结合DTI参数与临床特征构建的模型能够在术前有效分类胶质瘤IDH状态,显著提高术前诊断的准确性,从而便于制定更精准且有效的治疗策略。

Abstract

Objective To provide evidence for the preoperative prediction of isocitrate dehydrogenase (IDH) status in glioma by combining diffusion tensor imaging (DTI) with other clinical and imaging parameters. Methods This retrospective study enrolled glioma patients who underwent preoperative MRI examination at The First Affiliated Hospital of Xi'an Jiaotong University between September 2020 and January 2025 and were pathologically confirmed after surgery. Regions of interest (ROIs) were placed in the tumor region and the normal appearing white matter (NAWM) region on T2-FLAIR images, and fractional anisotropy (FA) and mean diffusivity (MD) within the ROIs were analyzed. In addition, DTI along the perivascular space (DTI-ALPS) was used to assess the function of the intracranial glymphatic system. Conventional MRI was used to classify the presence of cystic change and tumor location. Moreover, cognitive function and systemic inflammatory response were evaluated using the Montreal Cognitive Assessment (MoCA) and neutrophil-to-lymphocyte ratio (NLR), respectively. Predictive variables with statistically significant differences (P＜0.05) were included in binary Logistic regression analysis to establish a model for classifying glioma IDH status. Results We included a total of 65 glioma patients in this study: 27 patients with IDH-mutant glioma and 38 patients with IDH-wildtype glioma. Compared with patients with IDH-mutant glioma, those with IDH-wildtype glioma had higher FA _MEAN/ _NAWM, NLR, and MoCA scores, but lower MD _MIN, MD _MEAN/ _NAWM, and DTI-ALPS index. These factors were included in the binary Logistic regression analysis to establish a classification model. The receiver operating characteristic curve analysis showed that the area under the curve was 0.883 (95% CI: 0.803-0.963). Conclusion A model combining DTI parameters with clinical features can effectively classify glioma IDH status preoperatively and significantly improve the accuracy of preoperative diagnosis, thereby facilitating the development of more precise and effective treatment strategies.

关键词

胶质瘤 / 弥散张量成像(DTI) / 异柠檬酸脱氢酶(IDH) / 中性粒细胞-淋巴细胞比值(NLR)

Key words

glioma / diffusion tensor imaging (DTI) / isocitrate dehydrogenase (IDH) / neutrophil-lymphocyte ratio (NLR)

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基金资助

陕西省重点产业创新链项目(2024SF-ZDCYL-02-10)

2024年医学人工智能临床应用研究项目(YLXX24AIA021)