Original Research••

Correlation of enlarged perivascular spaces in basal ganglion and cancer-associated stroke: a case–control study in China

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Abstract

Introduction The incidence of cancer-associated ischaemic stroke (IS) is increasingly prevalent. This study aimed to assess the levels of enlarged perivascular spaces in basal ganglion (BG-EPVS) in cancer-associated patients who had a stroke compared with the control group, and to investigate the diagnostic utility of BG-EPVS in the context of cancer-associated stroke.

Method A matched case–control study was conducted in Xiamen, China. A total of 184 IS patients (cancer vs control=1:1) were recruited. The severity of BG-EPVS was graded using high-resolution MRI. Patients’ gender, age, clinical risk factors, other imaging changes and laboratory findings information at admission were collected. Logistic regression models were constructed and subgroup analysis by cancer treatment.

Result Overall, 65.22% of the 184 subjects were male, with a mean (SD) age of 68.83±10.52 years. BG-EPVS had a significant influence on cancer-associated stroke (OR=1.85 (95% CI 1.29, 2.71), p=0.001) after adjusting for gender, age, clinical risk factors, other imaging changes and laboratory findings. The area under the curve of the diagnosis model that combined BG-EPVS and other factors was 0.848 (95% CI 0.787, 0.896), significantly higher than the other three models. Subgroup analysis suggested a heightened association between BG-EPVS and cancer-associated stroke within the cancer treatment group.

Conclusion In conclusion, this is the first study to assess the diagnosis values of BG-EPVS on cancer-associated stroke and helps us understand the pathogenesis of cancer-associated stroke. Our findings demonstrate the effectiveness of BG-EPVS in diagnosing IS patients who may carry underlying cancer.

What is already known on this topic

The prevalence of underlying cancer disease in ischaemic stroke (IS) patients is higher than in the general population.
No studies have yet explored the correlation between enlarged perivascular spaces in basal ganglion (BG-EPVS) and cancer-associated stroke diagnosis.

What this study adds

Combining BG-EPVS levels and other indicators can improve the accuracy of diagnosing cancer-associated stroke, and will help to identify IS patients who may carry underlying cancer.
The impact of BG-EPVS on cancer-associated stroke was more pronounced in the group receiving cancer treatment compared with the group not receiving cancer treatment.

How this study might affect research, practice or policy

This study helps us understand the pathogenesis of cancer-associated stroke, and promotes the development and application of clinical diagnostic tools.

Introduction

Ischaemic stroke (IS) and cancer represent the two leading causes of mortality among the elderly population.1 IS can manifest either subsequent to or prior to a cancer diagnosis.2 Multicentre studies conducted in Europe have revealed that the prevalence of underlying cancer disease in IS patients is higher compared with the general population.3 4

Several potential causes of cancer-associated stroke have been proposed, encompassing both direct and indirect mechanisms. These include compression, embolism or invasion of blood vessels; cancer-induced activation of the coagulation cascade; hyperactivity of fibrinolysis-system; and treatment-related side effects.5 Prior investigations have elucidated several factors independently linked with active cancer in IS patients. These include undetermined aetiology stroke, inflammatory markers, hypercoagulability indicators.6 7

Enlarged perivascular spaces (EPVS) are fluid-filled cavities around small brain vessels seen on MRI. EPVS may manifest in association with a range of pathological conditions like cerebral small vessel disease, abnormal protein accumulation, blood–brain barrier (BBB) leakage or neuroinflammation.8–11 Cancer has the potential to induce systemic inflammatory responses, thereby impacting the central nervous system and resulting in neuroinflammation.12 The systemic inflammatory markers may serve as an indicator to identify the risk for EPVS, especially for basal ganglion EPVS (BG-EPVS).13 The disruption of BBB and inflammation associated with BG-EPVS may also in turn create a more favourable environment for the occurrence of stroke in individuals with cancer.14 15 Research on the link between BG-EPVS and cancer-associated strokes is necessary to elucidate the underlying mechanisms involved.

Numerous studies have highlighted the role of cancer treatment side effects as potential mechanisms for cancer-associated stroke.16 Structural changes in the brain resulting from cancer and its treatment, such as reductions in global and local grey matter volumes, compromised white matter microstructural integrity and alterations in brain networks, have been extensively documented in the literature.17 Additionally, prior research has suggested a potential link between radiation therapy and the development of EPVS.18 19 Consequently, there is a need for further investigation into the inconsistent role of BG-EPVS in cancer-associated stroke among individuals who have undergone cancer treatment.

For patients and doctors in the field of neurology, it is of great significance to identify cancer-associated stroke early. Given the potential involvement of BG-EPVS in the pathogenesis of cancer-associated stroke, this study aims to conduct a case–control analysis to assess the levels of BG-EPVS and to investigate its diagnostic value in the context of cancer-associated stroke. We hypothesise that the cancer group will have higher levels of BG-EPVS compared with the control group. And considering the potential impact of cancer treatment, we also hypothesise that the role of BG-EPVS in the diagnosis of cancer-associated stroke may vary depending on whether cancer treatment has been administered.

Methods

Study design and patients

This is a retrospective case–control study that includes IS patients who visited the Neurology Clinic at the First Affiliated Hospital of Xiamen University from January 2014 to December 2018. The inclusion criteria were specified as follows: (1) IS cases adhering to the Baltimore-Washington Cooperative Young Study Criteria and (2) MRI scans consistently reflecting infarctions in line with clinical presentations. The definition of cancer-associated stroke entailed: (1) previously ascertained malignancy, (2) recent cancer diagnosis and (3) ongoing cancer treatment, all occurring within 12 months preceding or following the index stroke. Initially, 119 patients with cancer-associated IS were included in the study. However, patients were subsequently excluded if they met the specified exclusion criteria as follows: (1) the presence of risk factors for cardioembolic stroke, such as atrial fibrillation, valvular heart disease and prosthetic valve replacement; (2) index arterial stenosis responsible for cerebral infarction exceeding 50%; (3) substantial brain parenchymal atrophy; and (4) haematological malignancies and primary intracranial malignancies or intracranial metastases, given their anticipated distinct underlying mechanisms for cerebral infarction.20 Ultimately, a cohort of 92 IS patients with cancer (designated as the cancer group) was selected. Meanwhile, 184 patients who had a stroke without cancer admitted during the same period were randomly selected. A matched cohort of 92 IS patients without cancer (designated as the control group) was further identified using propensity score matching in a 1:1 ratio, ensuring equivalence in age and gender between the groups. A detailed patients’ inclusion/exclusion flow was depicted in figure 1. The matching procedure and the results, both before and after matching, are visualised in online supplemental figure 1.

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Figure 1

Study flowchart. This flowchart illustrated the number of patients at each stage of the screening process, including the reasons for exclusion.

Clinical characteristics

Based on a comprehensive review of relevant literature and expert consensus, the following variables were systematically extracted from the electronic medical record system to serve as observational markers: (1) demographics: sex, age, (2) clinical risk factors: hypertension, diabetes, hyperlipidaemia, smoke, alcohol, (3) other imaging changes: ischaemic lesions regions, involvement of multiple vascular territories, lacunar infarction and (4) laboratory assessments: haemoglobin (g/L), platelet count (10⁹ /L), high-density lipoprotein (mmol/L); lactic dehydrogenase (U/L), D-dimer (μg/mL), fibrinogen (g/L). Diabetes, hypertension and dyslipidaemia are diagnosed based on either self-report by the patient or by meeting the diagnostic criteria established through routine clinical examinations administered on admission. Self-reported behaviours of smoking and drinking, whether past or present, were operationally defined as instances of smoking and drinking, respectively. Ischaemic lesions regions include internal carotid artery, vertebrobasilar artery and both. Besides, in the cancer group, cancer type and cancer treatment were recorded. Cancer treatment is defined as the patient receiving relevant treatments such as surgical treatment, radiotherapy and chemotherapy. All the indicators mentioned above were collected on admission.

All patients with IS underwent brain MRI scans before diagnosis. MRI images (field of view, 36×22 cm; matrix 128×128; standard axial T1W1, T2W1 (repetition time (TR) shortest, echo time (TE) 100 ms), axial diffusion-weighted imaging (DWI) acquisitions and axial fluid-attenuated inversion recovery (FLAIR) (TR 8000 ms, TE 120 ms) images, all with slice thickness of 6 mm and gaps of 1 mm) were obtained with a 3.0 Tesla MRI scanner.

Rating of BG-EPVS

High-resolution MRI was used to assess EPVS, employing a range of MRI sequences including axial T1-weighted, axial T2-weighted, axial DWI and coronal FLAIR. BG-EPVS were defined as cerebrospinal fluid-like signal lesions (hypointense on T1 and hyperintense on T2) of dots, of round or linear shape, depending on the plane in which they intersected, having smooth margins and located in the basal ganglia supplied by perforating arteries. And the usual diameter of EPVS is >1 mm and <3 mm.21 Similar lesions (hypointense on T1 and hyperintense on T2) had to be differentiated from lacunar infarction. Only lesions following the perforating vessels and without a T2-hyperintense rim on FLAIR imaging, commonly appearing bilaterally, were usually regarded as BG-EPVS (figure 2).22 Two experienced neurologists (QM and JF) independently analysed MRI images, blinded to all clinical data, to assess the quantity and grade of BG-EPVS. The inter-rater reliability, as measured by Cohen’s kappa coefficient, was 0.88 (95% CI 0.85, 0.937), p<0.001, indicating a high level of agreement.

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Figure 2

MRI images (thalamic level) of BG-EPVS (A–D) and lacunar infarction (E–F). T1 (A) and FLAIR (C) show hypointense areas in the basal ganglia on both sides. T2 (B) obtained at the same level shows that these areas are hyperintense. DWI (D) shows no restricted diffusion in these areas. Chronic lacunar infarction is hyperintense on T2 (E), hyperintense on FLAIR (F) or hyperintense at the edge of the central hypointense area due to gliosis (white arrowhead). BG-EPVS, enlarged basal ganglion perivascular space; DWI, diffusion-weighted imaging; FLAIR, fluid-attenuated inversion recovery.

BG-EPVS were assessed using a 4-level severity scale based on the slice exhibiting the highest number of BG-EPVS. The severity levels were defined as follows: degree 1, comprising 0–5 BG-EPVS; degree 2, comprising 5–10 BG-EPVS; degree 3, comprising more than 10 BG-EPVS but still numerable; and degree 4, comprising innumerable BG-EPVS (figure 3). Additionally, BG-EPVS were further classified into high-grade BG-EPVS (more than 10 BG-EPVS) and low-grade BG-EPVS.

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Figure 3

Severity level of the BG-EPVS. Degree 1 (A), BG-EPVS<5; degree 2 (B), 5<BG-EPVS<10; degree 3 (C), BG-EPVS>10; degree 4 (D), BG-EPVS=innumerable. BG-EPVS, enlarged basal ganglion perivascular space.

Statistical analyses

First, descriptive statistics for IS patients’ characteristics were tabulated. Pearson’s χ² test, and Welch two sample t-test were used to compare the differences between cancer group and control group. Second, multivariable logistic regression analysis was used to explore the impact of different clinical characteristics on cancer-associated stroke (models 1–4). Model 1 includes variables related to age, gender and clinical risk factors. Model 2 additionally includes variables related to other imaging changes on top of model 1. Model 3 further includes variables related to laboratory findings on top of model 2. Model 4 evaluates the impact of BG-EPVS on cancer-associated stroke on top of model 3. The ORs and their 95% CIs were estimated. Receiver operating characteristic (ROC) curves were plotted for different models, and the area under the curve (AUC) is compared. The sensitivity and specificity of the diagnostic models (model 3 and model 4) were also evaluated. BG-EPVS is included in the analysis using four ordinal categories. Besides, we further compare the influence of binary BG-EPVS (high-grade BG-EPVS vs low-grade BG-EPVS) with four ordinal categories of BG-EPVS on the results. Third, considering the influence of cancer treatment, we further conduct exploratory subgroup analysis based on cancer treatment status. Describe the basic characteristics of patients according to cancer treatment and explored the relationship of BG-EPVS and cancer-associated stroke in each subgroup of cancer treatment. We explore the ORs of BG-EPVS on cancer-treated stroke in patients with cancer who have received treatment, compared with those who have not received treatment. Additionally, we explore the impact of BG-EPVS on cancer-associated stroke in cancer-treated patients and cancer-untreated patients across different treatment types, age groups and sex, separately. The subgroup analysis results were depicted in the forest plot using the ‘forestploter’ package in R. All statistical calculations were performed using R (V.4.3.2, 2023 R Foundation for Statistical Computing). P values below 0.05 were deemed to signify statistical significance, with p values below 0.1 in the subgroup analysis indicating a potential trend towards significance.

Results

Clinical characteristics of patients

Table 1 presents the basic clinical characteristics of 184 IS patients. The average age was 68.83±10.52 years, with males accounting for 65.22% of the sample. Compared with the control group, the cancer group had a higher proportion of individuals with diabetes, hypertension, hyperlipidaemia, smokers and multiple vascular territories affected. In terms of laboratory findings, the cancer group had lower haemoglobin levels and higher D-dimer and fibrinogen levels. Notably, the cancer group had significantly higher levels of BG-EPVS compared with the control group (p=0.002).

Table 1

Baseline characteristics of patients by study group

Furthermore, the top three cancer types among the included cancer group patients were lung cancer, bowel cancer and stomach cancer. Among these, 59 (64.13%) patients had received cancer treatment.

Impact of BG-EPVS on cancer-associated stroke

Table 2 analysed the impact of various factors on cancer-associated stroke and compared the predictive performance of different models using ROC curves in figure 4A. In model 4, we observed that BG-EPVS had a significant influence on cancer-associated stroke (OR=1.85 (95% CI 1.29, 2.71), p=0.001) after adjusting for gender, age, clinical risk factors, other imaging changes and laboratory findings. Figure 4A demonstrates that the AUC of model 4 was 0.848 (95% CI 0.787, 0.896), significantly higher than the other three models (compared with model 3, model 2 and model 1; p=0.049, p=0.001, p<0.001, respectively). Online supplemental figure 2 illustrates an improvement in sensitivity with the incorporation of BG-EPVS into our diagnostic model (sensitivity in model 4 vs model 3=85.87% vs 82.61%), indicating an enhanced ability of the model to accurately screen for patients with cancer-associated stroke.

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Figure 4

Receiver operating characteristic (ROC) curves. (A) Comaparision of ROC curves for four different models (models 1–4). Model 1 includes variables related to age, gender and clinical risk factors. Model 2 additionally includes variables related to other imaging changes on top of model 1. Model 3 further includes variables related to laboratory findings on top of model 2. Model 4 evaluates the impact of BG-EPVS on cancer-associated stroke on top of model 3. A higher AUC represents better predictive performance. (B) Comparison of ROC curves based on different classifications of BG-EPVS. Logistic regression models were constructed based on model 4. AUC, area under the curve; BG-EPVS, enlarged basal ganglion perivascular space.

Table 2

The logistic regression models for cancer-associated stroke

Figure 4B compares the performance of model 4 in diagnosing cancer-associated stroke using different classifications of BG-EPVS, namely binary (high-grade and low-grade) and four categories. Notably, there was no statistically significant difference observed between the binary and four-category levels of BG-EPVS, further highlighting their comparable performance in diagnosing cancer-associated stroke.

Exploratory subgroup analysis of cancer treatment

Online supplemental file 3 presents a descriptive analysis of the basic clinical characteristics of cancer-associated stroke patients based on their cancer treatment status. No statistically significant differences were observed between cancer patients who received treatment and those who did not in terms of gender, age, clinical risk factors, other imaging changes, laboratory findings, and the distribution of BG-EPVS levels.

The findings of the exploratory subgroup analysis are presented in figure 5. The analysis indicated that the impact of BG-EPVS on cancer-associated stroke was more pronounced in the group receiving cancer treatment (ORs 2.35, 95% CI 1.58, 3.66) compared with the group not receiving cancer treatment (ORs 1.46, 95% CI 0.97, 2.24). In the subgroup analysis of cancer-treated patients, a consistent effect direction was observed among the subgroups, with higher levels of BG-EPVS indicating higher ORs of cancer-associated stroke among patients who had a stroke receiving various types of cancer treatment (surgical treatment, radiotherapy and chemotherapy), across different age groups (<60 and ≥60), and sexes, although there was no significant difference observed in the chemotherapy and age <60 subgroups. A comparable trend was observed in cancer-untreated patients after stratification by age group and sex, with no statistically significant differences noted in the age <60 and female subgroups.

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Figure 5

The relationship of enlarged perivascular spaces in basal ganglion (BG-EPVS) and cancer-associated stroke in each subgroup of cancer treatment.^*To prevent model underfitting, we selectively adjusted for four key variables (hypertension, ischaemic lesions regions, platelet count, D-dimer) which significantly associated with cancer-associated stroke in model 4 when assessing the impact of BG-EPVS on cancer-associated stroke across cancer treatment subgroups. The wide CI depicted by the green line may be attributed to the small sample size. n=sample size.

Discussion

From the neurologist’s perspective, there is an urgent need to improve the diagnostic rate for patients with cancer-associated IS and to further elucidate the pathogenesis of the disease. Although similar assessments have been previously performed, our study is the first to assess the association between BG-EPVS and cancer-associated stroke. We found a significant correlation between cancer-associated stroke and BG-EPVS levels, and combining BG-EPVS levels and other indicators can improve the accuracy of diagnosing cancer-associated stroke.

We enrolled a total of 92 patients with cancer-associated stroke, who were diagnosed with cancer within 12 months before or after their stroke hospitalisation. Previous research has demonstrated that lung, gastrointestinal and genitourinary cancers are the most common types of cancer associated with stroke.4 23 Consistent with these findings, lung and gastrointestinal cancers were the most prevalent types among the cancer-associated patients who had a stroke in our cohort. It is worth noting that lung and gastrointestinal cancers are among the most prevalent and deadliest types of cancer worldwide.24 25 The incidence of cancer-associated stroke is consistent with the epidemiological burden of cancer itself. As the population continues to age, the burden of cancer is escalating annually, underscoring the growing importance of addressing cancer-associated stroke in affected patients.

In the present study, patients with cancer-associated stroke had more severe BG-EPVS than patients with general stroke. To ensure the reliability of our analytical results, we controlled for confounding factors such as gender, age, clinical risk factors, other imaging changes and laboratory findings when analysing the effects of BG-EPVS on cancer-associated stroke. Our research confirms the significant effects of factors such as hypertension, ischaemic regions, platelet count and D-dimer in the diagnosis of cancer associated stroke. However, further inclusion of BG-EPVS in the model can still significantly increase the accuracy of diagnosis, suggesting that IS occurring in the context of cancer presence may involve other mechanisms.

The EPVS can be attributed to increased vessel wall permeability, vascular deformation, perivascular tissue ischaemia and cortical atrophy.26 As reported previously, there was a robust association between BG-EPVS level and the markers of inflammation, suggesting that alterations to the endothelium and BBB may be a driving force behind vascular gap dilation.11 And, malignant cells and immune interactions induce vascular endothelial cell injury.27 Therefore, in patients with cancer associated stroke, the more severe BG-EPVS appearance may be caused by chronic inflammation and endothelial damage from cancer-related mechanisms; our findings may indirectly support this hypothesis.

In light of the influence of cancer treatment on brain structure and function, we proceeded to conduct a subgroup analysis of cancer treatment. Our findings suggest a heightened association between BG-EPVS and cancer-associated stroke within the cancer treatment group. This heightened association may be attributed to the combined impact of cancer and its treatment, which not only facilitates the progression of BG-EPVS but also collaborates with the pathogenesis of cancer-associated stroke, thereby increasing the likelihood of its occurrence. BG-EPVS consistently affects cancer-associated stroke in patients undergoing surgery and radiation therapy, but not in those undergoing chemotherapy. Previous research indicates that chemotherapy itself does not increase the risk of stroke in patients with cancer, especially those not in advanced stages.28 On the other hand, the potential correlation between BG-EPVS and stroke in patients with cancer remains evident in individuals with cryptogenic tumours who have not undergone cancer treatment, particularly among male patients and those aged 60 years or older. Female patients with cancer exhibit heightened levels of psychological distress, including symptoms of depression and anxiety, as well as behavioural changes such as sleep disturbances, which may contribute significantly to the onset of cancer-associated stroke and warrant additional investigation.29 30 Furthermore, our findings suggest that the impact of BG-EPVS on cancer-associated stroke did not reach statistical significance among individuals under the age of 60. This lack of significance may be attributed to limited statistical power resulting from a small sample size, as well as the increased heterogeneity of stroke in younger populations compared with older individuals, which may be influenced by a variety of potential risk factors and aetiologies.31

The present study has certain limitations. First, this is a retrospective, single centre study. Therefore, selection bias is possible. Second, the patients included in this study were all sourced from the neurology outpatient department, and patients from other departments such as the oncology department were not included. This may limit the generalisability of the study results. Third, the small sample size limited the detection of effect sizes for subgroup analysis. Conducting larger sample size studies will help us further validate the role of cancer treatment and BG-EPVS in stroke occurrence. Besides, given the previous findings,32 BG-EPVS may share pathophysiological mechanisms with other MRI markers such as lacunes, WMH and deep MB, all of which may also play a role in the diagnosis of cancer associated stroke, and further research is warranted.

Conclusion

In conclusion, this is the first study to assess the association between BG-EPVS and cancer-associated stroke. Combining BG-EPVS levels and other indicators can improve the accuracy of diagnosing cancer-associated stroke, and will help to identify IS patients who may carry underlying cancer. This research advances our comprehension of the pathogenesis of cancer-associated stroke, and promotes the development and application of clinical diagnostic tools.

JW, GH and LZ are joint first authors.
QM and JF contributed equally.
Contributors: Conceptualisation: JW, GH, QM, JF. Study design: JW, GH. Data collection: all authors. Data quality control: all authors. Data analysis: JW. Draft manuscript writing: JW, GH. Editing: JW. Review and approving: all authors. The author(s) read and approved the final manuscript. JW and JF are the guarantors.
Funding: This study was supported by the Natural Science Foundation of China (Grants nos. 82072777); Key Project of Natural Science Foundation of Fujian Province (Grants nos. 2021J02057); Natural Science Foundation of Fujian Province (Grants nos. 2022J011358, 2022J011365); Medical and Health Key Project of Xiamen (Grants nos. 3502Z20204006); Xiamen Municipal Health Commission, Xiamen Municipal Bureau of Science and Technology (Grants nos. 3502Z20209005); The Project of Xiamen Cell Therapy Research (Grants nos. 3502Z20214001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: None declared.
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Data are available upon reasonable request.

Ethics statements

Patient consent for publication:

Ethics approval:

This study involves human participants and this study conformed to the standards set by the latest revision of the Declaration of Helsinki and was approved by the Ethics Committee of The First Affiliated Hospital of Xiamen University (ID: XMYY-2021KYSB074). All persons gave their informed consent prior to their inclusion in the study.

Acknowledgements

We thank all the participants in the study.

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doi:10.1212/WNL.0000000000200910•Google Scholar

Overview

Abstract
Introduction
Methods
Results
Discussion
Conclusion
References

Supplementary files
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Publication history
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