Among central nervous system neoplasms, meningioma is one of the most common tumors, with surgical resection considered the curative therapy, however preoperative adequate assessment and planning is important in cases where meningioma present at difficult location for access and surgical resection [12].
Among neurosurgeons who are dealing with meningioma, its consistency is considered a critical concern, as soft tumors can be easily removed with suction and/or a low intensity cavitron ultrasonic surgical aspirator (CUSA), with adequate debulking in easy quick surgery. On the other hand, firm meningiomas are usually difficult to be dissected from the adjacent critical structures that will affect the post-operative morbidity and mortality, so it is important for the surgeon to be aware about its consistency preoperatively for better selection of suitable equipment and proper access for better therapeutic outcome [12, 13].
Currently no single reproducible and objective method to predict meningioma consistency is available. Different imaging techniques and sequences were discussed in previous studies such as MR Elastography, MR spectroscopy, and fractional anisotropy with no clear consensus and till now there are conflicting results. Also, these recent techniques are usually expensive, not available everywhere in addition to complex interpretation [13].
In order to reach the aim of the current study we tried to answer the following questions: 1) Can conventional MRI detect meningioma consistency? and 2) Can MRI detect meningioma vascularity without angiography?
Can conventional MRI detect meningioma consistency?
In our study, 40 adult patients were analyzed for MRI characteristics and intraoperative tumor consistency. Although T2 signal showed significant correlation with consistency as most of hyperintense lesions were soft and most of hypointense lesions were firm, yet still this was based on visual assessment that is completely subjective.
On the other hand, DWI with ADC which is more quantitative method gave some subjective data where restricted diffusion with mean ADC values 0.8 × 10−5 cm2/s were detected in either soft or intermediate consistency meningiomas, yet no significant difference or cut off values was noted to differentiate soft from intermediate meningiomas.
Using routine conventional MRI, many studies were done on the correlation of MR signal intensity and meningioma consistency. The majority of these studies reached that no correlation between meningioma consistency and signal intensity in T1 or post contrast enhancement [7]. However, the T2WI showed the highest degree of correlation in different studies with results similar to the current one. Hoover et al. [14], and Sitthinamsuwan et al. [12], demonstrated a relationship between meningioma consistency and T2 signal intensity. In a relatively older study, Maiuri et al. [15], compared the T2 intensity of meningioma to cortex and found that T2 hyperintensity relative to cortex probably reflects soft consistency and syncytial or angioblastic subtypes.
Nevertheless, this correlation with imaging intensity has not been established. Kasoff et al. [16], and Kashimura et al. [17], concluded that there is no statistically significant correlation between consistency of meningioma and T2 intensity. They attributed this to the possibility of using different MRI machines, sequences, and protocols. Also, in all of these studies, signal intensity was not assessed quantitatively. Additionally, in most of these studies there was no specific definition of hypo or hyperintensity.
For better reproducibility, an objective method to predict tumor consistency preoperatively was defined using intraoperative tumor consistency grading. This initial idea was tested by Smith K et al. [11], in 20 patients as a pilot study to define values and investigate possible correlations. In the current study, we pursued validation of this method in a larger number of patients, using tumor/cerebellar peduncle T2-weighted imaging intensity (TCTI) ratios in correlation with intraoperative classification with suction and/or ultrasonic aspiration.
Intraoperative tumor consistency best correlated with TCTI ratios with 100% sensitivity, specificity, positive and negative predictive values. Soft tumors correlated with TCTI ratio > 1.6 and were removed with suction alone or a CUSA amplitude < 40. Soft tumors showed TCTI ranging from 1.75 to 2.87 with mean of 1.99 and median of 1.90. Firm tumors were correlated with TCTI ratios ≤ 1.2 and necessitated a CUSA amplitude > 70. Firm meningiomas showed TCTI ranging from 0.9 to 1.20 with mean of 1.09 and median of 1.10. Lastly intermediate consistency meningiomas best correlated with TCTI ratios > 1.2 and ≤ 1.6. Such intermediate consistency lesions showed TCTI ranging from 1.3 to 1.60 with mean of 1.50 and median of 1.53.
These results matched with the previous two studies of Smith et al. [11, 13], the first of which was in 2015 on 20 meningioma patients where soft tumors correlated with TCTI ratio > 1.8 with mean of 2.11 and median of 1.96. Firm tumors best correlated with TCTI ratios ≤ 1.0 with mean of 0.6 and median of 0.62. On the other hand, intermediate consistency meningiomas best correlated with TCTI ratios between 1.0 and 1.8 with mean of 1.51 and median of 1.6 [11].
Even more robust results were noted in the later study of Smith et al. [13], which was done in 2017 on 100 patients with intracranial meningiomas showing soft lesions to be correlated with TCTI ratio > 1.63 with mean of 1.91 and median of 1.84. Firm tumors best correlated with TCTI ratios ≤ 1.27 with mean of 1.01 and median of 0.88. Lastly,intermediate consistency meningiomas correlated with TCTI ratios between 1.33 and 1.63 with mean of 1.49 and median of 1.5 [13].
The current study agrees with the previous two studies of Smith et al. [11, 13], regarding the objectivity of TCTI ratio which required no special equipment or interpretation. Such objectivity is likely to allow the general use of the TCTI ratios by radiologists to guide neurosurgeons complementing previous trials to assess the meningioma consistency quantitatively.
Can MRI detect meningioma vascularity without angiography?
A subset of meningiomas exhibit hypervascular features, which increase their operative risk profile and can complicate operative planning. Excision of hypervascular symptomatic meningiomas may be preceded by preoperative embolization to reduce intraoperative bleeding, blood loss and facilitate surgical excision [18].
Historically, digital subtraction angiography (DSA) has been the standard method for identification of hypervascular meningiomas before operative intervention [19]. The radiologist typically depends on the presence of tumor blush on DSA; absence of this blush suggesting reduced or normal vascularity of the tumor [20]. Catheter angiography is generally considered safer than surgery and less invasive however, this technique is not without risk and inconvenience to the patient. Complications of DSA include risk of anesthesia, blood loss, groin hematoma, intracranial dissection, and ischemic stroke, in addition to exposure to radiation risk to the patient [21].
Perfusion imaging is a technique to assess the flow of blood at the tissue level and is widely used in imaging of brain tumors, mainly in the assessment of gliomas. Previous studies assessing meningiomas with dynamic susceptibility contrast MR perfusion detected a correlation between cerebral blood volume (CBV) and tumor vascularity histologically, including microvessel density and area [22, 23].
During the past decade, there has been growing interest in ASL-PWI, as it can quantify tissue perfusion without intravenous contrast injection, unlike the routine dynamic susceptibility contrast-enhanced PWI. The lack of IV contrast agents is considered beneficial particularly for patients with impaired renal function, in whom there is higher risk of developing nephrogenic systemic fibrosis after contrast administration [9].
The use of ASL-PWI in the assessment of brain tumors has been studied in different recent researches. For meningiomas, the relative CBF calculated from continuous ASL imaging was found to have a significant positive correlation with micro vessel area and thus to be easier to differentiate angiomatous subtype from others [24]. Similarly, in a recent study by Koizumi et al. [25], the absolute CBF from pseudo continuous ASL-PWI, rather than the relative CBF, was used to reflect the tumor vascularity. In their study, both the mean and maximum absolute CBF values were shown to have significant positive correlations with micro vascular density [25].
The current study tried to investigate whether ASL derived nCBF could serve as a reliable imaging biomarker for angiographic vascularity of meningiomas, in correlation with intraoperative tumor vascularity based on degree of intraoperative blood loss.
ASL showed significant correlation with operative vascularity of meningiomas, with hypervascular lesions showing nCBF ranging from 2.10 to 14.20 ml/100gm/min, intermediately vascular lesions ranging from 1.50 to 1.60 ml/100gm/min, and hypo vascular lesions ranging from 0.70 to 0.90 ml/100gm/min. ROC analysis showed 100% sensitivity, specificity, PPV and NPV for nCBF > 1.6 in prediction of hyper vascular meningiomas, nCBF ≤ 0.9 for hypo vascular meningiomas with intermediately vascular lesions having nCBF between > 0.9 and ≤ 1.6.
The results of the current study were matching with the previous study of Yoo et al. [9], who studied ASL in 27 cases with meningioma in comparison with DSA to predict angiographic vascularity. In their study, angiographic vascularity was measured by DSA using a 4-point grading scale and meningiomas were divided into 2 groups: low vascularity (Grades 0 and 1; n = 11) and high vascularity (Grades 2 and 3; n = 16). Authors concluded that, there is significant positive correlation between nCBF and angiographic vascularity (ρ = 0.718; p < 0.001) where the high-vascularity group showed significantly higher nCBF than the low-vascularity group (3.334 ± 2.768 and 0.909 ± 0.468, respectively; p = 0.003). At the optimal nCBF cutoff value of 1.73, sensitivity and specificity for the differential diagnosis of the 2 groups were 69% and 100% respectively.
The authors of the same study also found that patients with high nCBF values from ASL maps would have high vascularity meningiomas and so they are considered as potential candidates for preoperative DSA and embolization. Given the high specificity (100%) for differentiating between the high and low-vascularity groups at the optimal nCBF cutoff value, they concluded that the unnecessary DSA could have been avoided in the first place had the ASL findings been taken into consideration. Not only that, but the use of ASL-PWI as an initial screening tool is of benefit to reduce the length of hospital that will be reflected upon hospital resources. They concluded that ASL-PWI might predict angiographic vascularity, which will help to determine if there is need for preoperative embolization; thus, it may aid in selecting potential candidates for preoperative DSA and embolization.
Points of strength
All lesions in the current study had surgically and histologically proven diagnoses, and all MR imaging examinations were done at a high magnetic field of 3 T with higher signal to noise ratio (SNR) thus leading to decreased motion artifacts compared to 1.5 T. The use of T2 sequence which required no special equipment or interpretation for detection of TCTI ratio allows generalization of the TCTI ratios to neuroradiologists and neurosurgeons. As there are differences in CBF values, so to use the quantitative measurement of CBF accurately, in the present study we measured a ratio of CBF values of white matter from the contralateral side as quantitative indicators to attain the nCBF.
Limitations of the study
Our study had some limitations. First, is small sample size, and validation of the optimal TCTI ratios and ASL derived nCBF cutoff values is needed in large case series. Second, no DSA nor dynamic susceptibility contrast perfusion studies were done for comparison with ASL in any of the included patients thus another objective imaging-based grading of meningiomas vascularity was missing. Third, in contrast to consistency, intraoperative assessment of meningioma vascularity was mainly based on degree on intraoperative blood loss, still with no definite grading system or scoring. Lastly, no correlation was done between MRI parameters of interest namely consistency and perfusion-based vascularity and the histopathological variants of meningiomas or their WHO grading; such correlation needs to be verified in future studies to identify the role of these MRI parameters in grading of meningiomas.
