胶质瘤的淋巴系统功能障碍和脑脊液潴留：血管周围空间扩散和体积分析的证据

抽象

背景

神经胶质瘤可能通过大脑结构变化损害淋巴功能并改变脑脊液 （CSF） 动力学，从而可能影响肿瘤周围脑水肿 （PTBE） 和体液清除。本研究通过沿血管周围空间弥散张量成像分析（DTI-ALPS）和体积磁共振成像（MRI）探讨了胶质瘤对淋巴系统功能和脑脊液体积的影响，阐明了肿瘤特征与淋巴系统破坏之间的关系。

方法

在这项前瞻性研究中，112 名神经胶质瘤患者和 56 名健康对照者接受了 MRI 以计算 DTI-ALPS 指数并对脑脊液、肿瘤和 PTBE 进行体积分析。采用统计学分析评估DTI-ALPS指数、肿瘤体积、PTBE体积与临床特征的关系。

结果

与对照组相比，胶质瘤患者的DTI-ALPS指数显著降低（1.266 ± 0.258 vs. 1.395 ± 0.174，p < 0.001）和更大的脑脊液容量（174.53 ± 34.89 cm³ vs. 154.25 ± 20.89 cm³，p < 0.001）。 DTI-ALPS指数与肿瘤体积（r = -0.353，p < 0.001） 和PTBE体积（r = -0.266，p = 0.015） 呈负相关。高级别神经胶质瘤与较低的 DTI-ALPS 指数和较大的 PTBE 体积相关（均 p < 0.001）。在多变量分析中，肿瘤分级成为 DTI-ALPS 指数的独立预测因子 （β = -0.244，p = 0.011）。

结论

神经胶质瘤与显着的淋巴功能障碍有关，DTI-ALPS 指数降低以及脑脊液和 PTBE 体积增加就证明了这一点。DTI-ALPS 指数是神经胶质瘤患者淋巴管破坏的潜在生物标志物，提供了对肿瘤相关液体变化和脑肿瘤相互作用病理生理学的见解。

背景

格林帕系统是最近被表征的网络，通过促进间质废物的清除和调节大脑内的流体动力学，在维持大脑稳态方面发挥着至关重要的作用[1]。该系统通过协调机制运行，其中动脉搏动推动脑脊液 （CSF） 沿血管周围间隙 （PVS） 运行，而配备水通道蛋白 4 （AQP4） 通道的星形胶质细胞末端足促进脑脊液流入脑实质，在那里它被交换为间质液 （ISF），然后将脑内溶质通过脑膜淋巴管排入颈部淋巴系统 [2，3,4]。肾上腺系统的正常运作对大脑健康至关重要，因为它可以清除神经毒性物质并有助于维持细胞外液平衡[5,6]。 研究表明，胶淋巴通路紊乱与各种神经系统疾病（如阿尔茨海默病、帕金森病和创伤性脑损伤）之间存在相关性[7,8,9,10]。然而，这些关联仍然是观察性的，需要进一步的研究来阐明它们的因果关系。

在脑肿瘤，尤其是神经胶质瘤中，大脑会发生显着的结构和功能变化，包括颅内压升高、血脑屏障（BBB）破坏和白质纤维束损伤[11,12]。 然而，神经胶质瘤对淋巴功能的潜在影响研究仍然不充分。初步动物研究表明，与野生型对照相比，使用示踪剂Gd-DTPA测量的AQP4敲除大鼠的间质液（ISF）清除时间显着延长（82.8 ± 6.95 vs. 52.60 ± 6.87 min，p < 0.05），这可能间接反映了淋巴活性的局部紊乱[13].这种局部淋巴功能障碍可能阻碍肿瘤源性炎症和免疫因子的及时清除，从而影响肿瘤细胞的代谢和生长。此外，在神经胶质瘤模型中，AQP4在肿瘤周围区域的表达显著升高，AQP4过表达与肿瘤周围脑水肿的严重程度呈正相关[14]。一项黑色素瘤小鼠模型研究表明，与野生型小鼠相比，AQP4缺陷小鼠表现出更严重的血管源性水肿和颅内压升高[15]。这些发现表明 AQP4 失调与脑流体动力学受损之间存在潜在联系。我们的临床前研究进一步表明，在胶质瘤模型中脑脊液外排显着减少，这可能会加剧肿瘤周围脑水肿（PTBE）并改变肿瘤微环境，从而影响瘤内给药的有效性[16]。然而，神经胶质瘤引起的结构和功能变化与淋巴功能之间的复杂关系仍然知之甚少，尤其是在人体研究中。这种知识差距凸显了深入研究这些变化如何影响大脑稳态和临床结果的必要性（图 11）。

图1

神经胶质瘤和淋巴通路示意图。脑脊液沿着小动脉的血管周围空间进入脑实质，在那里它经历由 AQP-4 介导的扩散和对流，与 ISF 交换溶质。这种交换后，脑脊液向静脉的血管周围空间移动，最终流向脑膜淋巴系统和颈部淋巴结。此外，液体流出可能通过其他未定义的途径发生，例如沿着嗅觉神经等神经束。脑脊液，脑脊液;ISF，间质液。

（改编自 BioRender.com 创作的图片。

传统上，评估淋巴功能依赖于侵入性方法，例如鞘内示踪剂递送或钆增强 MRI。虽然这些方法已经产生了有价值的见解，但它们的侵入性和有限的时间分辨率降低了其常规临床应用的可行性[17,18]。 幸运的是，MRI 技术的进步带来了有前途的无创替代方案。沿血管周围空间的扩散张量成像（DTI-ALPS）方法已被提议作为淋巴活性的潜在替代方法，因为它能够量化沿PVS的各向异性水扩散[19]。虽然较低的 DTI-ALPS 指数已被解释为淋巴功能障碍的潜在标志物，但这种关系需要进一步验证，因为将 ALPS 指数与人类间质溶质清除率联系起来的直接证据仍然有限。

本研究通过无创 DTI-ALPS 调查了神经胶质瘤对淋巴功能的影响。通过分析ALPS指数、PTBE和脑脊液体积之间的关系，我们旨在阐明神经胶质瘤如何破坏脑液稳态。这些发现可能为恢复淋巴功能和减轻肿瘤相关并发症的策略提供信息。

材料和方法

道德许可

本前瞻性研究是根据赫尔辛基宣言进行的，并获得了我院伦理审查委员会的授权（批准号2020109）。所有参与者在纳入研究之前都提供了书面知情同意书。

科目

本前瞻性研究共纳入2020年10月至2023年7月期间在我院神经外科接受治疗的112例胶质瘤患者（女性49例，平均年龄52.8岁±13.1岁）。其中，35例为低级别胶质瘤，77例为高级别胶质瘤。此外，从医院体检中心招募了 56 名年龄和性别匹配的健康志愿者（27 名女性，平均年龄 53.4 ± 10.7 岁）。符合以下标准的患者被排除在研究之外：（1）缺乏组织病理学和基因检测信息;（2）存在其他神经或精神疾病，如颅内感染或脑血管疾病;（3）明显的睡眠功能障碍;（4）MRI图像质量差。根据以下标准纳入健康志愿者：（1） 没有已知的神经或精神疾病史;（2）MRI扫描未检测到异常脑结构或病变;（3）没有可能影响中枢神经系统的全身性疾病，如糖尿病或高血压;（4） 目前没有服用可能影响中枢神经系统功能的药物。

核磁共振采集

MRI 数据是通过具有 24 通道头线圈的 3T 扫描仪（uMR 790，United Imaging Healthcare，Shanghai，China）获得的。DTI 的最大 b 值为 1000 s/mm²，使用 48 个扩散编码方向。采集参数如下：重复时间 （TR） = 5,116 ms，回波时间 （TE） = 74.20 ms，切片厚度 = 4 mm，切片数 = 40，视场 （FOV） = 224 × 224 mm²，翻转角度 = 90°，带宽 = 1800 Hz/Px。此外，在施用钆基造影剂之前和之后使用 3D T1 加权快速梯度回波序列 （3D T1W）， 钆布曲（Gadavist，拜耳，德国），通过流速为 1.5 mmol/s 的高压注射器以 0.1 mmol/kg 的剂量静脉内给药。获得 2D T2 加权快速自旋回波图像 （2D T2W） 以评估解剖特征和病变属性，同时采用 3D 液体衰减反转恢复 （FLAIR） 序列来最小化来自脑脊液的信号， 增强脑室周围和皮质病变的可视化。为了解释昼夜节律对淋巴系统活动的影响，本研究中的所有MRI扫描都计划在上午9：00至中午12：00之间进行[20]。有关 MRI 扫描参数的更全面详细信息，请参阅补充表 1。

图像分析

沿血管周围空间的弥散张量图像分析 （DTI-ALPS）

DTI-ALPS指数用于评估侧脑室水平髓静脉周围PVS内的水扩散速率。它主要用于指示大脑将液体从皮质下区域输送到侧脑室的能力，并已被用于间接评估大脑的整体淋巴功能[19]。分析过程可以概括为以下步骤：（1）DTI数据的预处理是在带有FMRIB软件库（FSL，6.0.1版，英国牛津大学，http://www.fmrib.ox）的Linux工作站上进行的。此步骤包括涡流校正、运动校正和颅骨剥离。（2）扩散张量是通过DTIFIT工具计算的，该工具生成颜色编码的分数各向异性（FA）图以及x、y和z轴上的扩散系数。（3）两名神经放射科医生在侧脑室水平概述了受影响肿瘤半球和相应对侧半球的四个4体素立方感兴趣体积（VOI），将这些VOI定位在投射和关联纤维区域，确保放置在不受直接肿瘤侵袭影响的区域（图。2 （C））。为确保一致性，通过在侧脑室水平选择一个固定平面来标准化ROI放置，其中投影和关联纤维在张量图上显示出明显的信号强度差异，从而促进精确定位。（4）两名放射科医生沿x轴、y轴和z轴维度对已识别VOI内体素水平的扩散系数D进行统计分析，分别确定Dxxproj、Dxxassoc、Dyyproj和Dzzassoc值。随后计算这些系数的平均值。DTI-ALPS指数最终通过以下公式建立：

图2

Flowchart of MRI image analysis. (A) Segmentation of 3D-T1 images into white matter, gray matter, and CSF via FSL software, followed by calculation of the CSF volume. (B) Manual delineation of brain tumor (blue area) and peritumoral edema (green area) VOIs on 3D T1 + C and T2 FLAIR images, respectively. (C) Processing flow of the DTI-ALPS index: preprocessing of diffusion tensor images via FSL. Four 4-voxel cubic VOIs were defined bilaterally at the lateral ventricle level to evaluate diffusion coefficients in projection and association fibers

$$\text{D}\text{T}\text{I}-\text{A}\text{L}\text{P}\text{S}\text{i}\text{n}\text{d}\text{e}\text{x}=\frac{\text{m}\text{e}\text{a}\text{n}(\text{D}\text{x}\text{x}\text{p}\text{r}\text{o}\text{j},\text{D}\text{x}\text{x}\text{a}\text{s}\text{s}\text{o}\text{c})}{\text{m}\text{e}\text{a}\text{n}(\text{D}\text{y}\text{y}\text{p}\text{r}\text{o}\text{j},\text{D}\text{z}\text{z}\text{a}\text{s}\text{s}\text{o}\text{c})}$$

Segmentation of CSF, tumor, PTBE, and volumetric analysis

Unenhanced 3D-T1W images were first registered to the MNI152 standard brain template and skull stripping was then performed using the bet tool in FSL to remove non-brain tissues. Subsequently, the images were divided into cerebral gray matter, cerebral white matter, and CSF via FAST Segmentation, an automated software for FSL, with the volume of CSF being calculated. The volume of interest (VOI) for the tumor, encompassing both enhancing and non-enhancing areas, including necrotic cores, was delineated on the enhanced 3D T1W images. The PTBE was specifically segmented on 3D T2 FLAIR images to accurately capture the extent of edema (Fig. 2 (A)). First, the 3D-T1W, 3D-T1W + C, 3D-T2-FLAIR, and 2D-T2W images were aligned through linear registration techniques in SPM12, implemented in MATLAB (R2016b, The MathWorks, Inc.). Two experienced neuroradiologists (with 4 and 7 years of experience in interpreting central nervous system MR images, respectively) independently carried out these segmentations through the image segmentation software ITK-SNAP (version 3.8.0, http://itksnap.org). After manual delineation was accomplished, volumetric calculations of the segmented VOIs were performed accurately via the integrated statistical analysis features within ITK-SNAP (Fig. 2 (B)).

Histopathological analysis

Tumor tissues resected during surgery were fixed in 4% buffered formalin and subsequently embedded in paraffin. The paraffin-embedded tissues were sectioned for histopathological and immunohistochemical analyses. Specifically, 4 μm thick sections were cut from the main tumor mass and stained with hematoxylin and eosin (H&E). The glioma type and grade were determined according to the 2016 WHO classification for cases prior to August 2021 and according to the 2021 WHO classification for cases thereafter [21, 22]. The expression of proteins such as glial fibrillary acidic protein (GFAP), alpha-thalassemia/mental retardation syndrome x-linked (ATRX), and oligodendrocyte transcription factor 2 (Olig-2) in tumors was detected via immunohistochemistry. Sanger second-generation sequencing was used to detect mutations in isocitrate dehydrogenase (IDH)1/2 and telomerase reverse transcriptase (TERT), O6-methylguanine-DNA methyltransferase (MGMT) methylation, epidermal growth factor receptor (EGFR) amplification, and chromosome 1p/19q codeletion [23]. To evaluate tumor cell proliferation, Ki-67 immunohistochemical staining was conducted, with the Ki-67 labeling index defined as the percentage of Ki-67-positive nuclei relative to the total number of malignant cells [24, 25].

Statistical analysis

Statistical analyses were conducted using SPSS statistical software (Version 28, IBM, Armonk, New York, United States, https://www.ibm.com/analytics/spssstatistics-software) and R software (Version 4.2.3, https://www.R-project.org/). Descriptive statistics were used to summarize the demographic and clinical characteristics of all the subjects. The normality of continuous variables was assessed via the Shapiro‒Wilk test. For normally distributed data, an independent samples t test or one-way ANOVA followed by the least significant difference (LSD) post hoc test was performed to compare imaging and clinical characteristics between groups. For nonnormally distributed data, the Wilcoxon rank-sum test or the Kruskal‒Wallis test combined with Dunn’s post hoc test was applied. Differences in the ALPS indices of the bilateral cerebral hemispheres of all the subjects were tested via paired t tests. Categorical variables were analyzed via Pearson’s chi-square test. Univariate linear regression was used to assess the correlation between the ALPS index and both demographic and glioma characteristics, and significant variables were included in multivariate regression to detect independent factors. The relationships between quantitative parameters, such as the ALPS index and CSF volume, were evaluated via Spearman’s rank correlation coefficient. A p value of less than 0.05 was considered statistically significant. All the statistical tests were two-tailed.

Results

Study population characteristics

Our study included a total of 168 individuals, with 112 individuals diagnosed with glioma (mean age 52.8 ± 13.1 years, including 49 females) and a control group of 56 healthy subjects (mean age 53.4 ± 10.7 years, with 27 females). The demographic and clinical profiles of the study participants are summarized in Table 1, and the process of patient recruitment and classification is depicted in Fig. 3. Preoperative MRI scans were conducted on all glioma patients, followed by tumor resection within three days of imaging.

Table 1 Clinical characteristics of glioma patients and healthy controls

Fig. 3

Flow chart of the study sample. In this investigation, 135 patients with brain tumors underwent MRI scanning. After screening by the exclusion criteria, 112 patients with gliomas were included in this study. In addition, 56 age- and sex-matched healthy controls were recruited

Comparative analysis of neuroimaging biomarkers

The comparative analysis of neuroimaging biomarkers yielded significant findings. Compared with healthy controls, glioma patients demonstrated a notably lower mean ALPS index, calculated as the average of both hemispheres (1.266 ± 0.258 vs. 1.395 ± 0.174, p < 0.001). Additionally, an increase in CSF volume was detected in the glioma group relative to the control group (174.53 ± 34.89 cm³ vs. 154.25 ± 20.89 cm³, p < 0.001). The DTI-ALPS index in the hemisphere affected by the tumor was significantly lower than that in the contralateral hemisphere (1.233 ± 0.297 vs. 1.299 ± 0.296, p = 0.016), with both sides showing lower values than healthy controls did (p < 0.001, p = 0.009) (Supplementary Table 2). Compared with low-grade gliomas, high-grade gliomas had a lower DTI-ALPS index (P = 0.013) and greater tumor volume and PTBE volume (all p < 0.001), and no statistically significant difference in CSF volume was observed between the two groups (p = 0.394). There were no statistically significant differences in neuroimaging biomarkers between IDH wild-type and IDH-mutant tumors (all p > 0.05). A comparison of neuroimaging markers across glioma types revealed that glioblastomas had larger tumor volumes and lower ALPS indices than astrocytomas and oligodendrogliomas did (p = 0.017 and p = 0.004, respectively) (Table 2; Fig. 4). Intergroup comparisons for different tumor subgroups are shown in Supplementary Fig. 1.

Table 2 Between-group comparisons of CSF, tumor, and PTBE volumes; DTI-ALPS index

Fig. 4

Intergroup Comparative Raincloud Plots of Quantitative MRI Data. (A) Intergroup comparative analysis of the ALPS index. (B) Comparative analysis of cerebrospinal fluid volume between groups. (C) Comparative analysis of tumor and PTBE volumes between high- and low-grade gliomas. * p < 0.05, ** p < 0.01, *** p < 0.001, ns = not statistically significant

Correlation analysis of glioma characteristics with the ALPS index

Univariate linear regression analysis revealed significant correlations between the ALPS index and certain demographic and tumor characteristics, including age (β = -0.228, p = 0.011), tumor grade (β = 0.222, p = 0.015) and Olig-2 expression (β = -0.284, p = 0.003). However, in the multivariate regression analysis, tumor grade was the only significant independent factor for the glioma ALPS index (β = -0.244, p = 0.011) (Supplementary Table 3). Furthermore, correlation analysis further illuminated the relationships among the ALPS index, tumor volume, and PTBE volume. A significant negative correlation existed between the ALPS index and tumor volume (r = 0.353, p < 0.001), with this association being particularly pronounced in low-grade gliomas (r = 0.439, p = 0.026). Additionally, the ALPS index was negatively correlated with PTBE volume (r = -0.266, p = 0.015). The positive correlation between tumor volume and PTBE volume (r = 0.427, p < 0.001) was consistent across both high-grade and low-grade gliomas (Fig. 5, Supplementary Table 4).

Fig. 5

Correlation plots of the ALPS index, brain tumor volume, PTBE volume, and cerebrospinal fluid volume. The upper triangles display Spearman correlation coefficients, indicating the strength and direction of the relationships. The lower triangles feature scatter plots with fitted lines and confidence intervals, illustrating the trends. The diagonal shows the probability density distributions of the variables. Total, overall correlation; LGG, low-grade glioma; HGG, high-grade glioma. Significance levels: *, p < 0.05; **, p < 0.01; ***, p < 0.001

Discussion

This prospective study provides novel insights into the complex relationship between gliomas and CSF dynamics by evaluating the ALPS index and volumetric parameters in glioma patients. Significant correlations between the ALPS index, tumor volume, and PTBE volume highlight the intricate interplay between tumor burden, glymphatic dysfunction, and cerebral edema.

Our findings revealed a significantly lower ALPS index in glioma patients than in healthy controls, suggesting impaired glymphatic function [26]. This finding is consistent with the findings of previous animal studies [27]. The glymphatic system plays a crucial role in clearing metabolic waste from the brain via perivascular pathways, facilitated by the movement of CSF and interstitial fluid [28]. Tumor-induced alterations in the brain microenvironment, including the disruption of perivascular spaces and compromised BBB integrity, likely contribute to this dysfunction [29]. Previous studies have shown that brain tumors can disrupt normal fluid dynamics through mechanical compression and vascular dysregulation [30, 31]. This impairment of the glymphatic system may exacerbate the accumulation of toxic metabolites, promoting tumor progression and contributing to the cognitive decline often observed in glioma patients [32, 33]. Furthermore, the ALPS index was lower in the tumor-affected hemisphere than in the contralateral hemisphere and on both sides than in healthy controls, indicating that tumors can directly affect glymphatic function in the affected hemisphere. This localized impairment may cause metabolic disturbances that extend beyond the immediate tumor region, potentially affecting neurological function in the contralateral hemisphere as well.

The inverse correlation between the ALPS index and both tumor volume and PTBE volume highlights the significant impact of tumor burden on glymphatic function and cerebral fluid balance. Larger tumors are typically associated with more extensive PTBE due to increased BBB permeability and subsequent extravasation of plasma components into the brain parenchyma [34]. This edema increases intracranial pressure, disrupts normal fluid clearance pathways, and further impairs glymphatic function.

The association of high-grade gliomas with a significantly lower ALPS index than low-grade gliomas suggests that tumor aggressiveness exacerbates these disturbances. Interestingly, while CSF volume was greater in glioma patients than in healthy controls, no significant difference was observed between high-grade and low-grade gliomas. This finding suggests that the increase in CSF volume may reflect a generalized disruption in intracranial fluid dynamics due to the presence of a tumor rather than being directly related to tumor aggressiveness.

The absence of significant differences in neuroimaging biomarkers between IDH wild-type and IDH mutant tumors is notable. These findings suggest that the IDH mutation status may not significantly influence glymphatic function or cerebral fluid dynamics in this context, which contrasts with the findings of several previous reports [26, 35]. IDH mutations are well-established markers of prognosis in gliomas, with IDH-mutant tumors generally associated with a better overall prognosis than IDH-wild-type tumors. This mutation often indicates favorable biological behavior and a different tumor microenvironment [36]. However, our results highlight the complexity of this relationship, suggesting that the IDH mutation status may not be as directly linked to glymphatic function as previously thought. Further research is warranted to explore the underlying mechanisms and validate these findings.

Additionally, our study revealed that glioblastomas presented significantly larger tumor volumes and lower ALPS indices than astrocytomas and oligodendrogliomas did, reflecting their aggressive characteristics and consequently severe glymphatic dysfunction. Moreover, glioblastomas mainly originate from astrocytes, which are important components of the PVS and express AQP4 at their endfeet, which play a key role in facilitating the exchange of CSF and ISF. The aggressive nature of glioblastomas may lead to more extensive destruction of the perivascular space and a reduction in AQP4-mediated fluid exchange, resulting in an observed reduction in the ALPS index.

Multivariate analysis identified tumor grade as an independent factor associated with the ALPS index, which is consistent with previous findings [35], underscoring the pivotal role of tumor aggressiveness in glymphatic dysfunction. Owing to their increased invasiveness and larger tumor burden, high-grade gliomas may further disrupt perivascular spaces and fluid clearance pathways. Additionally, the reduced expression of vascular endothelial growth factor-C (VEGF-C) observed in high-grade glioblastomas may hinder the formation and expansion of meningeal lymphatic vessels, further impairing glymphatic drainage [37, 38]. Although age and Olig-2 were not identified as independent predictors of the ALPS index in multivariate analysis, their correlation with the ALPS index remains clinically relevant. Numerous studies have demonstrated a strong negative correlation between age and glymphatic function [39, 40]. Olig-2 expression is similar in adult low-grade astrocytomas and oligodendrogliomas but is lower in IDH-wildtype glioblastomas [41, 42]. Therefore, higher Olig-2 expression may indicate lower tumor aggressiveness, resulting in less disruption to the glymphatic system and a higher ALPS index. The significant correlations between the DTI-ALPS index, tumor volume, and PTBE volume offer valuable insights into the underlying pathophysiological mechanisms. These findings suggest that as tumors grow and exert more pressure on surrounding tissues, they not only contribute to edema but also interfere with the ability of the brain to clear waste products effectively. This creates a vicious cycle in which impaired waste clearance further promotes tumor-associated pathology, potentially accelerating disease progression.

The results of this study offer promising clinical insights for glioma treatment. The observed link between tumor burden, PTBE, and glymphatic dysfunction suggests that targeting glymphatic impairment could be beneficial. Therapeutic strategies such as drugs that enhance the function of AQP4 and VEGF-C or cervical deep lymphovenous anastomosis (LVA) could promote the clearance of toxic substances, as well as the migration and activation of lymphocytes, such as CD8 T cells, thus contributing to the reduction in cerebral edema as well as an enhanced antitumor response [37, 43, 44]. Therefore, early detection of reduced glymphatic function through a lower ALPS index may guide more aggressive interventions for high-grade gliomas, potentially improving patient outcomes by slowing tumor progression and mitigating cognitive decline.

Nonetheless, several limitations should be considered in this study. First, the relatively small sample size, particularly when different glioma grades were compared, may limit the statistical power of our findings. Future studies should aim to include larger cohorts to validate these results. Second, glioma classification followed the 2016 WHO criteria before August 2021 and the 2021 criteria thereafter, introducing potential heterogeneity in tumor grading that may impact the interpretation of glioma subtypes’ effects on glymphatic function. Third, our study relies primarily on imaging data to infer glymphatic dysfunction, and the absence of molecular or histological validation leaves room for further investigation. The incorporation of advanced molecular imaging techniques and the exploration of markers such as AQP4 could provide deeper insights into glymphatic alterations in glioma patients. Finally, the cross-sectional design of this study does not allow for the assessment of dynamic changes in the glymphatic system over time. Longitudinal studies would be invaluable in understanding whether surgical or medical interventions can restore glymphatic function and improve clinical outcomes.

Conclusion

In conclusion, this study highlights the complex interactions among glioma growth, cerebral edema, and glymphatic dysfunction, with significant implications for understanding glioma pathophysiology. The DTI-ALPS index could serve as a potential biomarker for glymphatic function impairment, guiding future therapeutic strategies aimed at alleviating tumor-related cerebral edema and enhancing waste clearance. Further research is warranted to explore these mechanisms and their clinical applications.