Peritumoral vessel characteristics on magnetic resonance venography reflect the sinus invasion status of para-sinus meningioma

Introduction

Meningioma is one of the most common intracranial tumors that typically has sufficient blood supply (1). Due to its peculiar origins and growth patterns, it is not unusual for meningioma to grow onto or into the dural venous sinus, also known as the para-sinus meningioma (2). Prof. Sindou initially proposed the sinus invasion classification of meningiomas, which aligns with different ways of resection (3,4).

Early recognition of sinus invasion is crucial, because inappropriate surgical decisions may increase the risk of postoperative recurrence or venous hemodynamic disorders (5). Consensus has been reached on the analysis of venous systems for surgical practice for paranasal sinus meningiomas that present with inadequate or established venous drainage (6-8).

Preoperative images play an important role in the auxiliary diagnosis of sinus invasion for highly suspicious para-sinus meningioma. Our previous research revealed that T1 black-blood magnetic resonance imaging (MRI) technique could perform well in displaying the sinus invasion of meningioma (2). However, this technique may be difficult to be widely applied considering the usage of the contrast injection (9).

Magnetic resonance venography (MRV) based on various techniques, including time of flight (TOF), phase contrast (PC) and contrast-enhanced MRV (CE-MRV), is commonly applied for preoperative evaluation on sinus invasion (10,11). Additionally, Barth et al. (12) pointed out that blood oxygen level-dependent (BOLD) MRV could reveal the growth pattern of meningioma, which is fundamentally linked to neoplasm invasiveness, angiogenesis and other vascular compensation (13,14). Therefore, vascular details on MRV are anticipated to aid in conveniently recognizing sinus invasion. However, to our best knowledge, no reports have ever analyzed the relationship between peri-meningioma vascular features and the sinus invasion by MRV techniques so far. Therefore, we aimed to evaluate the potential predictive performance of peritumoral vascular features on MRV for assessing sinus invasion. Our research may enlighten physicians to utilize MRV image information to predict sinus invasion and prescribe better treatment plans for meningioma. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-278/rc).

Methods

The study was implemented in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional ethics committee of Huashan Hospital, Fudan University (No. KY2022-691), and individual informed consent was waived because of its retrospective nature.

Study participants

A total of 85 consecutive meningioma patients in Huashan Hospital of Fudan University were enrolled in this study from January, 2018 to May, 2020. The participants in this study should meet the following criteria and details (Appendix 1): (I) preoperative identification of meningiomas adjacent to the superior sagittal sinus (SSS) or the transverse sinus, confirmed by operational record and postoperative pathology; (II) underwent routine brain MRI scan and MRV scan (either PC MRV or CE-MRV) before surgery; (III) complete clinical and radiological data were available; (IV) underwent neurosurgical intervention.

Imaging processing

All MRI studies were performed using a 3.0 T MRI machine (DISCOVERY MR750W; G.E., Milwaukee, MI, USA), armed with head array coils (Multichannel Receiver =32). Detailed information of PC MRV, CE-MRV of Time-of-flight (TOF) technique, and routine brain MR scan, including T1-weighted, T2-weighted, T2-FLAIR and enhanced T1-weighted sequences is provided in Appendix 2.

The MRV sequences were transferred into a workstation (Advantage Workstation 4.6, General Electric, Milwaukee, WI, USA) for image reconstruction. Maximum intensity projection (MIP) images derived from phase contrast MRV (PC-MRV) and CE-MRV were reformatted in sagittal, coronal, and axial planes.

Radiological analysis

Two senior neuroradiologists (Prof. Y.L. and Prof. B.Y.), each with over 10 years of experience, both blind to surgical results, retrospectively and independently analyzed the comprehensive radiological data of routine MRI, MRV images on 3 reformatted planes.

They initially assessed the sinus status (non-invasion or invasion) via MRV and other accessible MRI images. The status of sinus invasion was documented when a flow gap or hypo-intensity was detected inside the sinus and when the sinus structure appeared to be destructed on original slices, MIP or other reconstruction models. These findings corresponded to the surgical results of the sinus invasion (3,4). If these conditions were not met, the sinus status was recorded as ‘non-invasion’. Notably, a special condition was considered as ‘non-invasion’ when the flow gap on MRV images was resulted from tumor compression or severe edema. According to previous reports, this condition could be interpreted by the distinct recognition of the sinus structure, or the presence of enlarged bridging veins on enhanced T1 and MRV images, as well as edema on T2 flair or T2 sequence (15).

Peritumoral vessel analysis

Peritumoral vessels, closely associated with meningioma growth, were recognized as linear hyper-intensified structures located within a 2 cm range around the tumor on MRV (11,16). The peritumoral vessel counts were recorded by Prof. Y.L. and Prof. B.Y. Another two radiologists (Dr. X.L. and Dr. D.W.), each possessing over 8 years of experience, recorded MRV-based vascular features later. They first checked the section of the maximum tumor area axially, and then slid the images before and after the central section until the whole tumor disappeared. During this process, the maximum tumor diameter was measured and recorded for the entirety of the tumor as viewed in the axial plane. Parameters linked to the peritumoral vessels, including the sum vascular diameter, mean vascular diameter, maximum vascular vessel and minimum vascular vessel were carefully measured and recorded.

The intra/inter-observer consistency regarding the vessel count was assessed using a two-way random ICC (Intraclass/Interclass correlation coefficient), with a value greater than 0.75 regarded as good. Then, those diameter-associated variables, respectively filtered by the paired Mann-Whitney-U test (significant criterion of P<0.05), were all calculated into mean values for further analyses.

Specially, these variables were measured again after one month and this consistency was also calculated. The qualified variables were used in the subsequent analyses. In order to ensure that these vessel characteristics and the corresponding cutoff (rounded to nearest integer value) could be interpreted, the results of the first measurement were used. The final evaluation of sinus invasion status by radiologists was achieved after negotiation between 2 readers and the averaged values of diameter-related parameters were used for further analysis. Furthermore, the details of MR interpretation consensus for the evaluation of sinus invasion are shown in Figure 1, as well as Appendix 3 and Appendix 4.

Figure 1 The performance of PC-MRV and CE-MRV in interpreting sinus invasion. The radar map was used to evaluate the performance of PC-MRV (orange) and CE-MRV (blue) in the interpretation of sinus invasion. Details were provided in Appendix 4. AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value; CE-MRV, contrast-enhanced MRV; PC-MRV, phase contrast MRV; MRV, magnetic resonance venography; CI, confidence interval.

Gold standard of sinus invasion

The surgical classification of sinus invasion recorded was used as the gold standard. Sinus invasion was initially classified intraoperatively based on Sindou’s criteria (3) by a neurosurgeon with 15 years of experience. In our study, the categorization was simplified into 2 distinct categories: sinus non-invasion (Sindou Class I) group and sinus invasion (Sindou Class II–VI) group. The latter was further sub-classified into partial-invasion (Sindou Class II–IV) and complete-invasion groups (Sindou Class V–VI).

Statistical analysis

The data obtained from clinical, surgical and radiological profiles were analyzed, and visualized by R language, with statistical significance of a two-sided P value <0.05. For continuous variables with normal distribution (as determined by Shapiro-Wilk test) and homoscedasticity, independent t-tests were conducted. For non-normal continuous variables, two-tailed non-parametric Mann-Whitney-U tests were conducted. Categorical variables were compared, using Chi-squared or Fisher exact tests. More detailed information is shown below.

Results

Patient baseline

Eighty-five consecutive meningioma patients who underwent neurosurgical intervention, confirmed by surgery and pathology, were enrolled in this study (Table 1). Forty-six patients underwent PC-MRV (non-invasion vs. invasion confirmed by surgery =21:25) and 39 underwent CE-MRV (non-invasion vs. invasion confirmed by surgery =16:23).

Spearman correlation tests were performed to examine the relationship between clinical, radiological features, as well as surgical details (Figure 2). Significant positive associations were observed between higher, the World Health Organization (WHO) grade and the presence of male, irregular tumor shape and bone changes (all P<0.05). Sinus invasion showed positive relationships with bone changes, and unclear tumor brain interface, with correlation coefficients of 0.24 and 0.29 (all P<0.05), respectively.

Figure 2 The correlation matrix for baseline details of 85 patients. The correlation matrix calculated correlations and relevantly significant P values of several baseline details by two-tailed Spearman correlation test. The correlation coefficients in the lower panel were expressed by values (100 times) in the center of each square as well as the corresponding color scale. Statistically significant P values in the upper panel were signaled as “*” (*, P<0.05), those of no statistical significance were not signaled. WHO, World Health Organization.

The assessment of sinus invasion status

Good inter-reader consistency on the MR evaluation of sinus invasion was achieved with the kappa score of 0.783 for CE-MRV and 0.77 for PC-MRV. However, when compared to the gold standard of the surgical results, a combination of MRV and routine MR series did not aid radiologists to make accurate differentiation between 2 groups with the kappa score of 0.46 for the CE-MRV group and 0.247 for PC-MRV group (Appendix 4).

The vessel count and features of peri-tumoral vessels

The intra/inter-reader agreement for the peri-tumoral vessel count based on PC-MRV and CE-MRV were 0.763 and 0.886, respectively, indicating good consistency. Moreover, filtered by the paired Mann-Whitney-U test for both types of MRV, four diameter-associated variables were all involved in further analyses (all P>0.05).

Table 2 revealed the results of the comparative analysis of vessel characteristics between two groups with different sinus statuses, including the vessel count and 4 diameter-related parameters. Overall, the number of peritumoral vessels was significantly higher in the invasion group than that in the non-invasion group detected by either PC-MRV (3.34±0.79 vs. 2.48±0.86, P=0.002) or CE-MRV (6.5±0.9 vs. 4.94±0.87, P<0.001, Figure 3). Additionally, in sub-group analyses, the vessel count appeared to correlate significantly with the extent of sinus invasion (Table 3).

Figure 3 Boxplots of peritumoral vessel characteristics for PC-MRV (the upper) and CE-MRV (the lower). For the PC-MRV (the upper, pink panel) and the CE-MRV (the lower, blue panel) group (between the sinus invasion group and the non-invasion group), the vessel count and the 4 peritumoral vascular variables of the sum vessel diameter, mean vessel diameter, maximum vessel diameter, and minimum vessel diameter were tested by independent Mann-Whitney-U and plotted respectively. Statistically significant comparisons were signaled as “*” (*, P<0.05; **, P<0.01, ***, P<0.001), and those of no statistical significance were signaled as numeric P values. All diameter-related parameters were expressed in centimeters (mm). PC-MRV, phase contrast MRV; CE-MRV, contrast-enhanced MRV; MRV, magnetic resonance venography.

Three features of diameter-related parameters, namely the sum vascular diameter, the mean vascular diameter, the maximum vascular diameter, showed significant differences between two groups detected by both types of MRV (the non-invasion vs. invasion group for PC-MRV, with P<0.001, P=0.04 and P=0.01 respectively; the non-invasion vs. invasion group for CE-MRV, with P<0.001, P<0.01 and p≤0.001, respectively in Table 2). However, no statistical difference was observed in the minimum vascular diameter between two groups for both PC-MRV and CE-MRV. The comparisons of peritumoral vessel characteristics between the sinus non-invasion group and invasion group are also plotted in Figure 3. Additionally, in sub-group analyses between the partial invasion and complete invasion group, the sum vascular diameter and the max vascular diameter exhibited significant differences, while no significant differences were observed in the other two vascular diameter variables (Table 3).

The receiver operating characteristic (ROC) curve (Figure 4), illustrated the diagnostic performance of the vessel count in the evaluation of sinus invasion, with respective area under the curve (AUC) of 0.766 for PC-MRV [95% confidence interval (CI): 0.632–0.900] and 0.88 for CE-MRV (95% CI: 0.772–0.989). The potential diagnostic performance of 4 vascular diameter variables in both PC-MRV(A) and CE-MRV(B) were presented in Figure 4 as well, with respective AUC value of 0.787, 0.676, 0.717, 0.646 for PC-MRV (95% CI: 0.657–0.917; 0.517–0.836; 0.563–0.871; 0.478–0.814) and 0.899, 0.766, 0.812, 0.565 for CE-MRV (95% CI: 0.803–0.996; 0.603–0.929; 0.672–0.953; 0.381–0.749). Moreover, when applying generalized linear model (GLM) model with the vessel count and three differentially expressed vascular diameter parameters (the sum vascular diameter, the mean vascular diameter, the maximum vascular diameter), the diagnostic accuracy for the evaluation of sinus invasion could be further elevated with AUC values of 0.859 for PC-MRV (95% CI: 0.751–0.967) and 0.913 for CE-MRV (95% CI: 0.825–1). The comprehensive GLM model based on PC-MRV exhibited a significantly improved performance compared to the simpler model made solely by the vessel count (simple model: comprehensive model, AUC =0.766: 0.859, P=0.02), while the difference between models based on CE-MRV was not statistically significant (simple model: comprehensive model, AUC =0.88: 0.913, P=0.24>0.05) (Table 4).

Figure 4 Receiver operating characteristic curve of vessel characteristics between the sinus invasion group and the non-invasion group for (A) PC-MRV and (B) CE-MRV. The ROC curve of the vessel count, 4 vascular diameter-associated features and the comprehensive GLM model which was constructed by the sum vascular diameter, vessel mean diameter, vessel max diameter, to distinguish the sinus invasion group and the non-invasion group). ROC, receiver operating characteristic; PC-MRV, phase contrast MRV; CE-MRV, contrast-enhanced MRV; MRV, magnetic resonance venography; GLM, generalized linear model.

Further assessment of sinus invasion status by the vessel count

All samples were respectively divided into the non-invasion group and the invasion group, corresponded to the threshold vessel count of 3 for PC-MRV (n=46), as well as 5.5 for CE-MRV (n=39). According to the cut-off values of 3 and 5.5 respectively for PC-MRV and CE-MRV, the predicting performance of sinus invasion via the vessel count was interpreted and then visualized in Figure 5: PC-MRV had a sensitivity of 60.0%, a specificity of 81.0% and an accuracy of 69.6%; while CE-MRV had a sensitivity of 87.0%, a specificity of 81.3%, and an accuracy of 84.6%.

Figure 5 The performance of the peritumoral vessel counts on PC-MRV and CE-MRV in interpreting sinus invasion. The radar map was used to evaluate the performance of PC-MRV (orange) and CE-MRV (blue) in the interpretation of sinus invasion, under the cutoff of 3 and 5.5 respectively for PC-MRV and CE-MRV. Details were provided in Appendix 8. AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value; CE-MRV, contrast-enhanced MRV; PC-MRV, phase contrast MRV; MRV, magnetic resonance venography; CI, confidence interval.

Discussion

The surgical management of para-sinus meningioma poses challenges. Those neoplasms, situated adjacent to the venous sinus, create obstacles for tumor resections and increase risks of bleeding and postoperative disorders (17). As a result, preoperative evaluation is essential for the management of para-sinus meningioma patients, in case of potential hazards such as postoperative venous infarctions (18). In this study, we found that the peritumoral vessel count and vascular features detected by PC-MRV and CE-MRV could help to discriminate the status of sinus invasion preoperatively.

Although a prior study (11) reported that MRV could provide some valuable information on tumor invasiveness, that evaluation of sinus invasion status underperformed with a higher false positive rate due to neoplasm compression and limited vessel visibility. Although advanced sequences such as T1 black-blood MRI and susceptibility weighted imaging (SWI) exhibit superior visualization, long scanning time and the requirement for advanced equipment constrain their general clinical applications (2). In our study, compared with the gold standard of surgery, the low/moderate agreements were achieved respectively for PC-MRV and CE-MRV. As previously reported, factors such as inaccurate estimation of intra-cavitary flow and tumor volume/edema-induced occupying effect might be responsible for the poor MRV interpretations of sinus invasion (15,19).

Some radiological features on contrast-enhanced T1WI and T2WI could help to recognize sinus invasion roughly (20-22). In our study, bone destruction and unclear meningeal interface were found to be significantly associated with sinus invasion in meningioma patients, while they respectively achieved the accuracy of 62.2% and 63.6%. These two indications were reported to be associated with the invasiveness of meningiomas. So far, it remains difficult to obtain the accurate evaluation of sinus invasion in para-sinus meningioma preoperatively.

Based on the cut-off values of the vessel count of 3 and 5.5, the status of sinus invasion in para-sinus meningioma could be predicted with an accuracy of 69.6% for PC-MRV and 84.6% for CE-MRV. We purport that the number of peri-tumoral vessels plays an important role in tumor blood supply and drainage. Two main mechanisms might explain the pathobiological process. On one hand, cerebral tumors with the higher malignancy degree and rapid proliferation yield a decrease of oxygen content in the peritumoral environment (23,24). On the other hand, sinus invasion of meningioma breaking hub cerebral flow structures, can affect blood flow and the corresponding decrease in blood-oxygen levels. Both factors promote angiogenesis, increasing the blood supply and drainage as well (25,26). Since no significant correlation was found between sinus invasion and the maximum tumor diameter, we supposed that tumor growth in size might exert little influence on this vascular proliferation of para-sinus meningioma. Moreover, we further compared the vessel characteristics of three types of sinus status including sinus invasion, non-invasion and compression, on MRI images, and no statistical difference was found in vessel characteristics between sinus compression and non-invasion groups, while significant differences did exist between sinus compression and invasion groups (Appendix 5). Therefore, it is suggested that invasion of the sinus by tumor, rather than compression, is responsible for the promotion of peri-tumoral vascular proliferation.

In this study, we did not find a significant difference in the peritumoral vessel count and vascular features among para-sinus meningiomas of different WHO grades or pathological types (Appendix 6). This lack of a relationship may be due to the complexity of tumor-host interactions and the potential influence of sinus invasion on the local microenvironment. Specifically, sinus invasion by meningiomas could disrupt normal cerebral flow circulation, leading to blood-oxygen level changes and angiogenesis in the tumor microenvironment (12). These changes may override the influence of tumor grade or type on vessel count and vascular features. In our previous work (20,27), we observed a significant correlation between certain radiologic findings of meningiomas and WHO grade, suggesting that higher-grade meningiomas tend to have more blood vessels to support their growth and metabolism. However, the current study suggests that sinus invasion may be an important confounding factor that needs to be considered when evaluating vascular changes in meningiomas. Future studies may need to further investigate the relationship between sinus invasion, vascular changes, tumor grade and WHO type, as well as the underlying mechanisms that drive these changes.

According to Sayhan et al. (28), the diameter of the SSS varies at different locations: the anterior and middle one seem larger than the posterior one. When the SSS is invaded, collaterals often develop to preserve venous drainage. It could be speculated the meningioma of middle SSS is associated with more bridging veins, while the posterior 1/3 has fewer. Despite previous studies using computed tomography venography (CTV) and cerebral digital subtraction angiography (DSA) detected more collaterals in meningiomas of posterior SSS (11,29), in our study, para-SSS meningiomas were classified into 3 groups of the anterior, middle, and posterior. Kruskal-Wallis’s rank sum tests among the three groups and the further Mann-Whitney U tests between each one and another were conducted for comparison, and no significant differences were found in vessel characteristics (Appendix 7). The insignificant results could be attributed to the combined effect from the establishment of collateral circulation and native drainage patterns.

Overall, our study revealed that the distinction of sinus invasion could be aided by analyzing the vessel count and several diameter-associated features of peritumoral veins by MRV. By identifying the peritumoral vessels on PC-MRV and CE-MRV, we provided an effective method to make up for shortcomings in venography: for PC-MRV, overestimated velocity code settings could suppress the display of flow signal, or otherwise extend scanning time; for CE-MRV, heterogeneously distributed contrast agent at the early stage or slow passage of contrast agent resulted into blood flow signal loss. These conditions brought a higher false positive preoperative estimation, as well as a lower specificity (11). In our study, the specificity was greatly improved, providing a better perspective of predicting sinus invasion status than routine MR or previous studies. Based on the cut-off values of 3 on PC-MRV and 5.5 on CE-MRV, the specificity, accuracy and AUC generated from MRV reinterpretations were improved by 28.6%, 6.6%, 8.3% for PC-MRV and 18.8%, 10.2%, 11.5% for CE-MRV (Figure 5, Appendix 8). Appendix 9 provides two representative cases with MRV images, in which the peritumoral vessel count correctly classified their sinus status. Thus, the identification of the number of peritumoral vessels could practically evaluate the probability of sinus invasion. Additionally, for further sub-group analyses, the vessel count remained closely linked with the extent of sinus invasion, as well as the sum vascular diameter and the max vascular diameter (Table 3). This suggests that, as the extent of sinus invasion increases, cerebral blood flow is compensated by the increased vessel density and vessel diameter. Further studies with more participants or long-term follow-ups are anticipated to validate these findings.

Furthermore, a comprehensive binomial logistic GLM model was constructed and its AUC performance was higher than that of the single model built by the vessel count, indicating the usefulness of diameter-related parameters in the evaluation of sinus invasion status in para-sinus meningioma patients (Table 4). Hence, we proposed that peritumoral small veins could be considered as one of promising radiological indicators for preoperatively predicting sinus invasion. Besides, these vascular markers indicate that peritumoral components may assist in the accurate prediction of sinus invasion by para-sinus meningiomas. Researchers incorporated peritumoral information into a deep learning model for train-test-validation and ultimately achieved good performance (30). Our findings could provide some clinical explanations for research concerning the microenvironment of meningioma and show promise for related clinical evaluation.

This study is the first to correlate the aggressiveness of meningioma with vessel characteristics on MR venography, and there are several limitations. Firstly, the small sample size of enrolled patients limited statistical power, necessitating larger multi-center data for validation. Secondly, only 5 vessel characteristics were examined in our study, highlighting the need for high-throughput data to provide additional insights into angiogenesis in meningiomas. Thirdly, although vascular features on MRV showed potential in detecting sinus invasion, a comprehensive preoperative evaluation requires multiple imaging modalities, such as CTV and DSA. Nevertheless, MRV is in principle susceptible to venous structures, having the edge in distinguishing veins from arteries, as well as having no risk of medication or allergy. Moreover, radiosurgery is an effective adjuvant surgical procedure (31). Previous studies considered the tumor location of para-sinus as a risk factor for post-radiotherapy (32,33). However, to control for confounders, our study cohort was only confined to patients who underwent resections. Therefore, patients with various therapies and imaging techniques would be expected to be involved in future research.

Conclusions

In our study, the peritumoral vessel count and diameter parameters surrounding para-sinus meningioma on MRV were significantly correlated with the invasion status of sinus. With the higher grade of sinus invasion, the vessel characteristics remained significantly different in subgroup analyses. The comprehensive model of the vessel count and diameter parameters performed well and might be closely related with the tumor microenvironment. Using the cutoff value of vessel count, MRV can be a promising tool to predict the sinus invasion status in peri-sinus meningiomas preoperatively.

Acknowledgments

Funding: This project was supported by “Science and Technology Innovation Action Plan” of Shanghai Science and Technology Commission (Grant No. 22S31905900), Shanghai Sailing Program (Grant No. 21YF1404800), Youth Program of Special Project for Clinical Research of Shanghai Municipal Health Commission Health Industry (Grant No. 20204Y0423), and “Fudan University Comprehensive Discipline Prosperity Program: AI for Science Special Project” (Grant No. IDF151057).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-278/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-278/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was implemented in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional ethics committee of Huashan Hospital, Fudan University (No. KY2022-691), and individual informed consent was waived because of its retrospective nature.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Goldbrunner R, Stavrinou P, Jenkinson MD, Sahm F, Mawrin C, Weber DC, Preusser M, Minniti G, Lund-Johansen M, Lefranc F, Houdart E, Sallabanda K, Le Rhun E, Nieuwenhuizen D, Tabatabai G, Soffietti R, Weller M. EANO guideline on the diagnosis and management of meningiomas. Neuro Oncol 2021;23:1821-34. [Crossref] [PubMed]
2. Wang D, Lu Y, Yin B, Chen M, Geng D, Liu L, Wen J, Zhong P, Li Y. 3D Fast Spin-Echo T1 Black-Blood Imaging for the Preoperative Detection of Venous Sinus Invasion by Meningioma : Comparison with Contrast-Enhanced MRV. Clin Neuroradiol 2019;29:65-73. [Crossref] [PubMed]
3. Sindou M. Meningiomas invading the sagittal or transverse sinuses, resection with venous reconstruction. J Clin Neurosci 2001;8:8-11.
4. Ji C, Zhao J, Xing W, Liu J. Analysis of the efficacy of microdissection of paravebous sinus meningiomas invading large venous sinuses. BMC Surg 2023;23:112. [Crossref] [PubMed]
5. Cai Q, Wang S, Wang J, Tian Q, Huang T, Qin H, Feng D. Classification of Peritumoral Veins in Convexity and Parasagittal Meningiomas and Its Significance in Preventing Cerebral Venous Infarction. World Neurosurg 2021;149:e261-8. [Crossref] [PubMed]
6. Elhammady MS, Heros RC. Cerebral veins: to sacrifice or not to sacrifice, that is the question. World Neurosurg 2015;83:320-4. [Crossref] [PubMed]
7. Yin T, Gu J, Huang Y, Wei L, Gao J, Wang S. Assessment and Treatment of Peritumoral Cortical Veins in Parasagittal Meningiomas with Application of 3-Dimensional Imaging Fusion Model. World Neurosurg 2017;104:220-8. [Crossref] [PubMed]
8. Raza SM, Gallia GL, Brem H, Weingart JD, Long DM, Olivi A. Perioperative and long-term outcomes from the management of parasagittal meningiomas invading the superior sagittal sinus. Neurosurgery 2010;67:885-93; discussion 893. [Crossref] [PubMed]
9. Wamelink IJHG, Azizova A, Booth TC, Mutsaerts HJMM, Ogunleye A, Mankad K, Petr J, Barkhof F, Keil VC. Brain Tumor Imaging without Gadolinium-based Contrast Agents: Feasible or Fantasy? Radiology 2024;310:e230793. [Crossref] [PubMed]
10. Mattle HP, Wentz KU, Edelman RR, Wallner B, Finn JP, Barnes P, Atkinson DJ, Kleefield J, Hoogewoud HM. Cerebral venography with MR. Radiology 1991;178:453-8. [Crossref] [PubMed]
11. Bozzao A, Finocchi V, Romano A, Ferrante M, Fasoli F, Trillò G, Ferrante L, Fantozzi LM. Role of contrast-enhanced MR venography in the preoperative evaluation of parasagittal meningiomas. Eur Radiol 2005;15:1790-6. [Crossref] [PubMed]
12. Barth M, Nöbauer-Huhmann IM, Reichenbach JR, Mlynárik V, Schöggl A, Matula C, Trattnig S. High-resolution three-dimensional contrast-enhanced blood oxygenation level-dependent magnetic resonance venography of brain tumors at 3 Tesla: first clinical experience and comparison with 1.5 Tesla. Invest Radiol 2003;38:409-14. [Crossref] [PubMed]
13. Jirawisan P, Nunta-Aree S, Sitthinamsuwan B, Chankaew E. Route patterns of the collateral venous pathway in patients with tumors invading the superior sagittal sinus: an angiographic study and clinical applications. Neurosurg Rev 2024;47:415. [Crossref] [PubMed]
14. Berhouma M, Picart T, Dumot C, Pelissou-Guyotat I, Meyronet D, Ducray F, Honnorat J, Eker O, Guyotat J, Lukaszewicz AC, Cotton F. Alterations of cerebral microcirculation in peritumoral edema: feasibility of in vivo sidestream dark-field imaging in intracranial meningiomas. Neurooncol Adv 2020;2:vdaa108. [Crossref] [PubMed]
15. Yu J, Chen FF, Zhang HW, Zhang H, Luo SP, Huang GD, Lin F, Lei Y, Luo L. Comparative Analysis of the MRI Characteristics of Meningiomas According to the 2016 WHO Pathological Classification. Technol Cancer Res Treat 2020;19:1533033820983287. [Crossref] [PubMed]
16. Jensen CA, Chan MD, Mccoy TP, Shaw EG, Mcmullen KP, Ellis TL, Munley MT, Bourland JD, De Guzman A, Tatter SB. Adjuvant Radiosurgery after Resection of a Brain Metastasis Allows for the Delay or Elimination of Whole Brain Radiotherapy. Int J Radiat Oncol 2009;75:S226.
17. Balik V, Kourilova P, Sulla I, Vrbkova J, Srovnal J, Hajduch M, Takizawa K. Recurrence of surgically treated parasagittal meningiomas: a meta-analysis of risk factors. Acta Neurochir (Wien) 2020;162:2165-76. [Crossref] [PubMed]
18. Cai Q, Wang S, Zheng M, Wang X, Liu R, Liu L, Qin H, Feng D. Risk factors influencing cerebral venous infarction after meningioma resection. BMC Neurol 2022;22:259. [Crossref] [PubMed]
19. Niu J, Zhang S, Ma S, Diao J, Zhou W, Tian J, Zang Y, Jia W. Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images. Eur Radiol 2019;29:1625-34. [Crossref] [PubMed]
20. Lu Y, Liu L, Luan S, Xiong J, Geng D, Yin B. The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest. Eur Radiol 2019;29:1318-28. [Crossref] [PubMed]
21. Zhang J, Yao K, Liu P, Liu Z, Han T, Zhao Z, Cao Y, Zhang G, Zhang J, Tian J, Zhou J. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study. EBioMedicine 2020;58:102933. [Crossref] [PubMed]
22. Li N, Mo Y, Huang C, Han K, He M, Wang X, Wen J, Yang S, Wu H, Dong F, Sun F, Li Y, Yu Y, Zhang M, Guan X, Xu X. A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features. Front Oncol 2021;11:752158. [Crossref] [PubMed]
23. Mittal S, Wu Z, Neelavalli J, Haacke EM. Susceptibility-weighted imaging: technical aspects and clinical applications, part 2. AJNR Am J Neuroradiol 2009;30:232-52. [Crossref] [PubMed]
24. Guevara P, Escobar-Arriaga E, Saavedra-Perez D, Martinez-Rumayor A, Flores-Estrada D, Rembao D, Calderon A, Sotelo J, Arrieta O. Angiogenesis and expression of estrogen and progesterone receptors as predictive factors for recurrence of meningioma. J Neurooncol 2010;98:379-84. [Crossref] [PubMed]
25. Wu PB, Chow DS, Petridis PD, Sisti MB, Bruce JN, Canoll PD, Grinband J. Asynchrony in Peritumoral Resting-State Blood Oxygen Level-Dependent fMRI Predicts Meningioma Grade and Invasion. AJNR Am J Neuroradiol 2021;42:1293-8. [Crossref] [PubMed]
26. Arzanforoosh F, Van der Velden M, Berman AJL, Van der Voort SR, Bos EM, Schouten JW, Vincent AJPE, Kros JM, Smits M, Warnert EAH. MRI-Based Assessment of Brain Tumor Hypoxia: Correlation with Histology. Cancers (Basel) 2023;16:138. [Crossref] [PubMed]
27. Li X, Lu Y, Xiong J, Wang D, She D, Kuai X, Geng D, Yin B. Presurgical differentiation between malignant haemangiopericytoma and angiomatous meningioma by a radiomics approach based on texture analysis. J Neuroradiol 2019;46:281-7. [Crossref] [PubMed]
28. Sayhan S, Guvencer M, Ozer E, Arda MN. Morphometric evaluation of parasagittal venous anatomy for intracranial approaches: a cadaveric study. Turk Neurosurg 2012;22:540-6. [Crossref] [PubMed]
29. Zhen J, Liu C, Jiang B, He J, Pang Q, Wang G. Preoperative evaluation of venous systems with computed tomography venography in parasagittal meningiomas. J Comput Assist Tomogr 2008;32:293-7. [Crossref] [PubMed]
30. Sun K, Zhang J, Liu Z, Qiu Q, Gao H, Liu P, Chen K, Wei W, Wang L, Zhang J, Zhou J, Tian J. A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions. Eur J Radiol 2022;149:110187. [Crossref] [PubMed]
31. Mair MJ, Tabouret E, Johnson DR, Sulman EP, Wen PY, Preusser M, Albert NL. Radioligand therapies in meningioma - evidence and future directions. Neuro Oncol 2024; Epub ahead of print. [Crossref]
32. Li X, Lu Y, Liu L, Wang D, Zhao Y, Mei N, Geng D, Ma X, Zheng W, Duan S, Wu PY, Wen H, Tan Y, Sun X, Sun S, Li Z, Yu T, Yin B. Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study. Eur Radiol 2023;33:8912-24. [Crossref] [PubMed]
33. Hoe Y, Choi YJ, Kim JH, Kwon DH, Kim CJ, Cho YH. Peritumoral Brain Edema after Stereotactic Radiosurgery for Asymptomatic Intracranial Meningiomas: Risks and Pattern of Evolution. J Korean Neurosurg Soc 2015;58:379-84. [Crossref] [PubMed]