Abstract

Introduction

The vein of Galen aneurysmal malformation (VGAM) is a rare congenital intra-arachnoidal and extra-cerebral vascular malformation, representing 30% of pediatric vascular and 1% of all pediatric congenital anomalies [1, 2]. VGAM is believed to arise at the choroid stage (8th–11th week of gestation) when choroidal arteries and their feeders become prominent in the cerebral vascular system. This malformation is the result of an abnormal embryonic development that leads to the shunting of arterial blood into the median prosencephalic vein of Markowski, a precursor of the vein of Galen. In these patients, the median prosencephalic vein of Markowski persists instead of undergoing a normal regression [3].

The clinical presentation of VGAM reflects its angio-architecture and shunt complexity as well as patient age. In neonates, the high-flow low-resistance lesion with direct arteriovenous connections leads to cardiac failure and respiratory insufficiency [4–6]. Later in infancy or childhood, patients may present with seizures, developmental delay, macrocephaly, and hydrocephalus [6, 7]. In adolescence and adulthood, symptoms comprise headaches, cognitive dysfunction, and, rarely, subarachnoid hemorrhage [6, 7].

Specialized intensive perinatal care and endovascular techniques are now available and have made VGAM a manageable condition [4, 8–10]. Research in the last few years has been focused on identifying early clinical and radiological prognostic features that might guide the clinical decision-making process. Several brain magnetic resonance imaging (MRI) findings have been evaluated, including (i) VGAM angioarchitectural features, (ii) arterial steal and/or pseudo-feeders, (iii) superior sagittal sinus stenosis, (iv) straight sinus dilatation, (v) VGAM pouch volume, (vi) posterior fossa dural sinus occlusion, and/or (vii) presence of brain lesions [11–18, 12, 17]. Among these, the most debated and controversial neuroimaging predictor is the volume of the venous pouch [12, 17, 19]. Historically, a large venous pouch accompanied by numerous feeders was associated with a worse prognosis [20, 21]. In 2017, Paladini et al. found that VGAM aneurysm volume>20 cm³ was predictive of poor outcome and parenchymal injury [19]. However, according to recent studies, VGAM volumetry should no longer be considered a prognostic factor [12, 17].

The volume of the pouch is generally calculated from the three diameters (craniocaudal, laterolateral, and anteroposterior) using the ellipsoid formula:

where A represents the height, B the width, and C the length of the pouch [12, 19].

Despite its wide use, we believe this application fails to represent the true morphology of most VGAM pouches, as it assumes a regular and edge-free shape.

Many segmentation algorithms and platforms are now easily accessible, even as open source, in the radiology field, and they are regularly used for evaluating other diseases. Studies on tumors have demonstrated that advanced segmentation techniques are highly performing and can automatically learn complex features representative of the heterogeneity of the tumor entity [22, 23]. Similarly, we hypothesize that segmentation techniques might reproduce more accurately the VGAM pouch, thus shedding light on the prognostic role of the VGAM pouch volume.

In the present study, we implemented a semiautomatic segmentation algorithm to define and calculate the VGAM pouch volume, and we compared the results of this method with those obtained using the ellipsoid formulas. We then assessed the relationship between the VGAM volume obtained with the new method and other neuroimaging and clinical features and explored the role of VGAM volume in the prediction of poor clinical outcome. As primary outcomes, we considered heart failure requiring endovascular treatment in the neonatal period. Secondary short-term outcomes included the presence of brain lesions at first MRI, the occurrence and severity of periprocedural neurologic complications, and mortality. Finally, the relationship between VGAM volume and neurological findings at last follow-up was explored.

Methods

This single-center retrospective observational study has been approved by the Regional Ethical Committee (CER Liguria: 804/2021). Parental written informed consent was obtained for each exam. The study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [24].

Semiautomated segmentation of the VGAM pouch

Figure 1 recapitulates the main steps of the semiautomated segmentation analysis. In detail, we first performed an exploratory analysis of multi-sequence MRI data in a subgroup of 10 subjects with VGAM (5 performed on a 1.5 T and 5 on a 3 T magnet) to select the MRI sequence that allowed the most accurate segmentation of the pouch. Images from 4 different MRI sequences, including 2D T2-weighted, 2D or 3D T1-weighted sequences, TOF arterial MRA, and phase-contrast venous MRA were converted with the open-source software MRIcron into a Neuroimaging Informatics Technology Initiative (NIfTI) format. The NIfTI images were then imported into the open-source 3D Slicer software by Brigham and Women’s Hospital (v. 4.10.2) that allows semiautomated segmentation and volume calculations [25]. Briefly, the process consisted of the following: (i) adding two segments in the Segment Editor module, (ii) drawing the seed regions within (segment 1) and outside (segment 2) the VGAM pouch on the three planes with the function “paint,” (iii) initializing the “Grow from seeds” algorithm for segment 2 which would automatically discriminate the pouch from other brain structures (Fig. 2), and (iv) manual refinement to verify that the segmentation followed the anatomical compartment boundaries properly. The VGAM volume was then extrapolated using the Segment Statistics function, and the three-dimensional (3D) segmentations were exported into a.nnrd file. The semiautomated segmentation method was implemented and validated by a bioinformatician (CP) with a 4-year experience in pediatric neuroimaging post-processing and analysis.

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Fig. 1

Study and segmentation workflow. After case selection (Sect. Introduction), the MRI images were retrieved from our PACS system and imported on the 3D Slicer software. A preliminary analysis on the sequence providing the highest quality 3D representation of the VGAM pouch was performed (Sect. Methods). The phase contrast venous MRA images obtained the highest scoring and were used to perform the VGAM pouch segmentation on the three planes in the entire cohort (Sect. Results). Figure created with BioRender.com

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Fig. 2

VGAM segmentation using the “Grow from seeds” algorithm from the Segment editor module of the 3D Slicer software. a The seed regions are drawn within (segment 1) and outside (segment 2) the VGAM pouch on the three planes with the function “paint.” b The “Grow from seeds” algorithm is initialized with automatic discrimination of the pouch from other brain structures

The VGAM segmentations obtained from the 4 sequences were independently assessed by two neuroradiologists (MS and DT, with 15 and 10 years’ expertise in pediatric neuroimaging, respectively) prior to any manual polishing, and assigned a score from 1 to 5, with 1=Poor, 2=Fair, 3=Good, 4=Very Good, and 5=Excellent segmentation, as shown in the supplementary Fig. 1 (Online Resource 2).

All VGAM pouches were then independently segmented by the same readers who performed the linear manual measurements, i.e., reader 1 (MAV) and reader 2 (MS), using the sequence that rated the highest-quality scores. To avoid bias, a random patient was selected, and calculations for the volume were repeated 15 times for each approach by one reader.

Results

Clinical and neuroradiological data

Between 2009 and 2022, 49 patients with VGAM were admitted to the Gaslini Children’s Hospital. One neonate died before undergoing a brain MRI, three subjects had an incomplete brain MRI protocol, and 2 patients had a poor-quality MR scan. In total, 43 patients with VGAM were included in this study (22 males, mean age at first MRI 6.56 days, range 0–99 days). Of these, 39 subjects were enrolled during the neonatal period. Patients’ characteristics are summarized in Table 1. Twenty-two neonates (51%) underwent the first MRI examination on a 1.5 Tesla scanner and 21 (49%) on a 3 Tesla scanner. Table ​Table22 reports the results of the qualitative analysis of the brain MRI studies of the patients.

Table 1

Demographic and general features of the patients

Patients’ characteristics
Sex, M (%)	22 (51.1)
Mean GA at birth (range)	38 w (30–41)
Mean age at first MRI (range)	6.56 d (1–99 d)
Mean age at embolization (range)	38.74 d (2–198 d)
Mean age at last follow-up (range)	5.49 y (2 d–14 y)
Embolization (%)	35 (81)
Neonatal embolization (%)*†	22 (63)
Embolization complications (%)†	15 (43)
Minor complications (%)†	7 (20)
Major complications (%)†	8 (23)
Palliated (%)	3 (7.6)

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d days, GA gestational age, M males, MRI magnetic resonance imaging, w weeks, y years

^*Treatment performed in 17 cases for hemodynamic reasons and in 5 cases for neuroradiological features including pseudo-feeders

^†Data available on the 35 embolized patients

Table 2

Qualitative neuroradiological findings of the patients

	Yes (%)	No (%)
Brain MRI features (n=43)
Ventriculomegaly	14 (33)	29 (67)
SSS stenosis	25 (58)	18 (42)
JB stenosis	24 (53)	19 (47)
ICV drainage into VGAM	9 (21)	34 (79)
Small thalamic feeders	16 (37)	27 (63)
Pseudo-feeders	17 (40)	26 (60)
Global brain atrophy	3 (7)	40 (93)
Ischemic/hemorrhagic lesions	13 (30)	30 (70)
WM signal alterations	18 (42)	25 (58)
Tonsillar herniation	0 (0)	43 (100)
Aqueductal stenosis	25 (58)	18 (42)

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ICV internal cerebral veins, JB jugular bulb, MRI magnetic resonance imaging, RV right ventricle, SSS superior sagittal sinus, WM white matter

VGAM volume analysis

The sequence achieving the best scoring in the preliminary analysis was the phase contrast MR venography, with a mean score of 4.5 and 4.6 for readers 1 and 2, respectively, corresponding to an excellent segmentation with a weighted Cohen K of 0.8.

Table ​Table33 reports the median VGAM volumes obtained with the manual and semiautomated method for both readers. The volumes measured by the two methods correlated well, with r_s (41)=0.86, p<0.001 for reader 1 and r_s (41)=0.82, p<0.001 for reader 2 (Fig. 3a, b). Bland–Altman analysis showed that the bias between manual and semiautomated methods was very small for both readers, i.e., 0.1 for reader 1 and 0.4 for reader 2 (Fig. 3c, d). Around 5% of the points were lying outside the limits, specifically 2/43 (4%) for reader 1 and 3/43 (6%) confirming a good agreement between the approaches.

Table 3

VGAM volume calculated with the semiautomatic method and with the ellipsoid formula for both readers

VGAM volume (cm3)	R1 med (IQR)	R1 (min–max)	R2 med (IQR)	R2 (min–max)
3D Slicer	4.7 (3.24–7.49)	0.25–17.84	5 (3.01–8.21)	0.3–18.2
Ellipsoid formula	4.2 (2.46–7.71)	0.18–18.40	5.2 (3.51–8.42)	0.7–21.33

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IQR interquartile range, Max maximal, Med median, Min minimum, R1 reader 1, R2 reader 2, VGAM vein of Galen malformation

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Fig. 3

Scattered and Bland–Altman plots showing the agreement between the two methods for evaluating the VGAM volume. a, b Scattered plots showing good correlation between the two methods both for reader 1 (a) and reader 2 (b). c, d Bland–Altman plots reveal that the bias between manual and semiautomated methods was very small both for reader 1 (c) and reader 2 (d), i.e., 0.1 and 0.4, respectively. Around 5% of the points were lying outside the limits, specifically 2/43 (4%) for reader 1 and 3/43 (6%) confirming a good agreement between the approaches. Bias is the mean difference between the volumes of the Slicer 3D and ellipsoid (black line). Red dashed lines represent the upper and lower limits of agreement ($\pm 1.96\sigma$)

Regarding reproducibility, the inter-rater ICC was 0.885 (confidence interval 0.797–0.936) for the manual method and 0.992 (confidence interval 0.996–0.998) for the semiautomated method (p<0.001).

Repeated measures on the same subject showed less variability using 3D Slicer compared to the ellipsoid formula with a standard error of 0.04 cm³ (median 11.94, IQR 11.78–11.98 cm³) and 0.40 cm³ (median 11.69, IQR 10.99–12.99 cm³), respectively, as shown in the Supplementary Fig. 2 (Online Resource 3).

Associations between VGAM volume and clinico-instrumental features

Table ​Table44 demonstrates the relationship between the VGAM volume obtained with 3D slicer and qualitative neuroimaging features. We observed a higher volume of the pouch in patients with SSS stenosis (5.20 cm³; IQR 3.60–10.62 versus 4.02 cm³; IQR 2.23–5.38), JB stenosis (5.11 cm³, IQR 3.77–10.69 versus 3.61 cm³, IQR 2.30–5.47), and aqueductal stenosis (4.79 cm³, IQR 3.43–10.62 versus 3.81 cm³, IQR 1.98–5.38) with p-values<0.05.

Table 4

Relationship between VGAM volume and other neuroradiological features

			V3D SLICER
		n (%)	Med (IQR)	p-value
Brain MRI features (n=43)
VGAM type	Choroidal	36 (84)	4.51 (3.33–7.89)	0.910
	Mural	7 (16)	5.05 (2.83–6.57)
Ventriculomegaly		14 (33)	4.70 (3.76–8.61)	0.140
SSS stenosis		25 (58)	5.20 (3.60–10.62)	0.036
JB stenosis		24 (56)	5.11 (3.77–10.69)	0.045
ICV drainage into VGAM		9 (21)	4.43 (2.24–6.44)	0.418
Small thalamic feeders		16 (37)	3.87 (2.23–6.52)	0.167
Pseudo-feeders		17 (40)	5.17 (3.60–10.62)	0.224
Aqueductal stenosis		25 (58)	4.79 (3.43–10.62)	0.044

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ICV internal cerebral vein, IQR interquartile range, JB jugular bulb, MRI magnetic resonance imaging, SSS superior sagittal sinus, V volume, VGAM vein of Galen malformation, WM white matter

p-values < 0.05

Regarding the quantitative parameters, a weak positive correlation was measured between VGAM volume and SS-MD (rho=0.331) whereas a weak negative correlation was observed for the SSS index (rho=−0.325) (Fig. 4).

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Fig. 4

Scatterplot graphs showing the correlation between the VGAM volume and quantitative neuroradiological features for both methods. a Correlation with the superior sagittal sinus (SSS) index. b Correlation with the latero-lateral diameter at the narrowest point of the straight sinus (SS-MD)

Functional and anatomical cardiac features at birth were available for 39 neonates with VGAM (Table 5). No significant differences were found regarding VGAM volume, and the null hypothesis was accepted for all parameters (p>0.05).

Table 5

Relationship between VGAM volume and cardiological features at birth

		n (%)	V3D SLICER
			Med (IQR)	p-value
Neonatal cardiac US (n=39)
RV dilation	Yes	24 (61.5)	4.70 (3.43–8.04)	0.529
Shunt in DA	L to R	11 (28.2)	4.90 (2.91–9.18)	0.137
	L=R	13 (33.3)	4.10 (2.48–5.44)
	R to L	15 (38.4)	5.20 (3.81–10.76)
Shape IVS	Normal	14 (35.8)	3.43 (2.34–6.58)	0.268
	Intermediate	19 (48.7)	4.89 (3.53–10.02)
	R to L	6 (15.3)	6.08 (3.89–8.56)
PH index	>1	14 (37)	4.95 (3.58–10.56)	0.469
	1	10 (25.6)	5.47 (3.62–10.80)
	<1	15 (38.4)	4.75 (2.74–6.01)

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DA ductus arteriosus, IQR interquartile range, IQR interquartile range, IVS interventricular septum, L left, Med median, PH pulmonary hypertension, R right, RV right ventricular, US ultrasound, V volume

The discriminant function analysis demonstrated a greater contribution of SSS stenosis (standardized coefficient of 1.33), JB stenosis (standardized coefficient of 1.02), SS-MD (standardized coefficient of 0.16), and SSS index (standardized coefficient of 0.08) in predicting NAR when compared with VGAM volume (standardized coefficient of 0.02).

VGAM volume and outcomes

Table ​Table66 reports the relationship between the VGAM volume and the short- and long-term outcome measures. Heart failure occurred in 20/39 neonates (51.2%), of whom 17 (85%) were categorized as NAR due to high-output heart failure requiring EVT in the neonatal period and 3 were palliated due to the severe brain damage at birth (these 3 patients were not included in the analysis). We did not find significant differences between the VGAM volume and presence of heart failure at birth or in the NAR subgroup. Similarly, no differences in VGAM volume emerged for death (p-value>0.05).

Table 6

Relationship between VGAM volume and outcomes and embolization complications

			V3D SLICER
		n (%)	Med (IQR)	p-value
Short-term outcomes
Heart failure (n=39)*		20 (51)	5.19 (3.81–10.69)	0.120
NAR (n=36)§		17 (44)	5.17 (3.79–9.30)	0.208
Deaths (n=43)		7 (16)	5.17 (3.81–10.49)	0.323
I/H lesions at first MRI (n=43)		13 (30)	4.70 (3.60–10.70)	0.425
WM signal alterations (n=43)		18 (42)	4.88 (3.34–10.59)	0.176
Global brain atrophy (n=43)		3 (7)	10.49 (2.66–17.84)	0.321
Long-term outcomes
POPC score (n=36)^	1	26 (60)	4.71 (2.57–7.32)	0.337
	2	3 (7)	3.94 (3.76–4.43)
	3	2 (5)	12.40 (n.a.)
	4	5 (12)	3.44 (2.38–12.01)
Epilepsy (n=36)^		8 (22)	5.36 (2.87–12.15)	0.421
Treatment
Embolization complications (n=35)		17 (48.5)	5.05 (3.81–10.49)	0.657

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EVT endovascular treatment, I/H ischemic/hemorrhagic, IQR interquartile range, n.a. not applicable, NAR neonates at risk, Med median, MRI magnetic resonance imaging, POPC Pediatric Overall Performance Category, WM white matter

^*Analysis performed in the 39 subjects presenting in the neonatal period

^§Three neonates who died after palliation for severe brain lesions were excluded from the analysis

^Analysis performed in the 36 surviving subjects

At first MRI, 13/43 (30%) of subjects had small-to-medium sized ischemic and/or hemorrhagic lesions, 18/43 (42%) had white matter signal alterations, and 3/43 (7%) showed global brain atrophy. None of these features was associated with larger VGAM volumes (p-value>0.05).

At last follow-up, 60% (26/43) of our cohort was assigned a POPC1, 7% (3/43) a POPC2, only 5% (2/43) scored a POPC3, and 12% a POPC4. A total of 36 cases had EEG data available, among whom 8 were diagnosed with epilepsy (22%). No associations were observed between the VGAM volume and epilepsy or POPC score (p-value>0.05).

Treatment and intervention strategies

Table ​Table77 reports the median and IQR values for the IS and VIS scores in the 39 neonates with VGAM. No correlation was observed between the IS and VIS scores and the VGAM volume (p-values>0.05).

Table 7

Relationship between VGAM volume and pharmacological cardiovascular support

	Median (min–max)	[IQR]	V3D SLICER p-value
Pharmacological support (n=39)
IS score	0 (0–20)	[0–10]	0.226
VIS score	4 (0–24)	[0–6]	0.187

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IQR interquartile range, IS inotrope score, Max maximal, Min minimum, V volume, VIS vasoactive inotrope score

In the whole cohort, endovascular staged embolization was performed in 35/43 subjects (81%) with a median age at first embolization of 33.2 days (range 2 days–6 months). A total of 22/35 cases underwent endovascular treatment in the neonatal period: 17 for severe heart failure and 5 for the presence of neuroradiological risk factors, such as pseudofeeders, even in the absence of severe heart failure [12]. Multiple embolization procedures were completed in 19/35 (54%) patients (range of procedures, 2 to 9) after multidisciplinary team evaluations. After embolization, 15/35 (43%) subjects developed periprocedural neurological complications (8 major and 7 minor). In four patients (4/43, 10%) who were asymptomatic at birth and clinically stable, the VGAM underwent spontaneous thrombosis and was not treated.

No association was observed between endovascular procedure complications or the IS and VIS score and the VGAM volume (Table 6 and ​and7)7) (p-value>0.05).

Discussion

In the present study, we demonstrated that quantitative semiautomated VGAM volumetry is feasible with reliable and reproducible results as compared to linear measurements. Indeed, only a few cases were lying outside of the agreement limits between the two methods, showing that the volumes calculated with the semiautomated approach do not diverge significatively from those obtained using the ellipsoid formula. Post hoc inspection of the segmented boundaries suggested that the outliers in variability were mainly due to judgment calls of the operator. These cases had large transitions between the VGAM aneurysm and the straight sinus, thus not enabling a clear separation of these anatomical structures and requiring operator judgment on where to draw the border between the two. The narrower confidence interval by the semiautomated method suggested a better reproducibility compared to the manual method. Of note, the standard error in repeated measurement was found to be lower in the V_{3D SLICER} than the V_ELLIPSOID (0.04 versus 0.40), indicating less variability results from the 3D Slicer method. Of note, phase-contrast MR venography provided the best segmentation results among all sequences. While arterial TOF angiography better depicts the VGAM arterial feeders [11], phase-contrast MR venography provides better contour delineation with high signal intensity of the VGAM pouch and is ideal to obtain VGAM volumetry. Interestingly, this sequence has also been used to obtain quantitative measurements of flow inside the VGAM pouch [30, 31], thus providing interesting insights regarding the flow dynamics of the shunt. A traditional drawback associated with phase-contrast imaging is the acquisition time penalty [32]. However, the use of parallel imaging and accelerating techniques has led to significantly reduced times to acquire phase-contrast MR angiograms [32].

Regarding the relationship with other neuroradiological features, we observed larger VGAM volumes in subjects with SSS narrowing, JB stenosis, and aqueductal stenosis. While the latter feature is explained by the VGAM mass effect, the relation with SSS and JB stenosis is more complex and likely relies on hemodynamic mechanisms. SSS narrowing is associated with poor clinical outcome in patients with VGAM [14] but its etiology is not well understood. The main draining route of the VGAM is toward the straight sinus or through a persistent falcine sinus when the straight sinus is absent or small [20]. Saliou et al. proposed that a decrease in the SSS diameter could reflect a reduction of venous flow through the cortical veins caused by the shunt, associated with venous compression due to high intracranial pressure [14]. We thus hypothesize that the larger VGAM size might be related to increased arterial steal towards the shunt and higher intracranial pressure, with consequent venous diameter reduction of the SSS. Future studies using noninvasive MR techniques to assess hemodynamics within the VGAM pouch, such as 4-Dimensional Flow Magnetic Resonance Imaging [31], are awaited to clarify whether larger VGAM are characterized by higher blood flow and/or pressure gradients through the shunt.

JB stenosis or occlusion is another prognostic factor associated with poor prognosis due to worsening of cerebral venous hypertension [13, 14]. JB stenosis is rarely present at birth and usually develops later during the disease course, compromising venous outflow and causing complications related to chronic venous hypertension [13]. The underlying pathophysiology is still unknown, but several hypotheses have been formulated, including intrinsic “dysmaturative” vessel factors or extrinsic “hemodynamic” mechanisms [4, 14, 33, 34]. According to the latter, JB stenosis might protect the heart by decreasing the venous pressure arising in the right heart due to the high-flow shunt [14]. A recent study using phase-contrast MR sequences with arterial and venous dual velocity encoding showed a high-complexity recirculating flow pattern in the venous system of infants with VGAM, thus supporting the “hemodynamic” theory [35]. We speculate that, as for the SSS narrowing, larger VGAM are related to more complex flow patterns and consequent hemodynamic changes in the venous system. The integration of VGAM volumetry with computational fluid dynamics in prospective cohorts might shed light on this issue.

Finally, we found a positive correlation between the VGAM volume and SS-MD, a well-known neonatal prognostic factor in fetuses and neonates with VGAM [17, 19]. In 2017, Paladini et al. first reported a poor clinical prognosis, including neurologic sequelae, neonatal death, and termination of pregnancy, in fetuses with VGAM and straight sinus dilatation [19]. Subsequently, Arko et al. confirmed that dilatation of the straight sinus at the point of greatest constriction to flow return from the malformation to the systemic circulation was sharply predictive of mortality and the need for neonatal intervention [17]. In particular, they found that a SS-MD of>6.2 mm in the neonatal MRI and/or>5.2 mm in the fetal MRI strongly predicted clinical evolution to heart failure requiring early endovascular treatment [17]. Despite the underlying mechanism remaining unknown, higher venous pressure has been found in patients with an enlarged straight or falcine sinus, likely transmitting the intra-aneurysmal pressure to the general cerebral venous system [36]. Interestingly, Quisling and Mickle also showed a positive relationship between VGAM size and venous pressure, with larger aneurysms presenting higher venous pressures [36], thus supporting our findings.

Using the VGAM volumetry obtained with this new semiautomated quantitative method, we did not find any association with anatomical and functional cardiac features or outcome measures at birth. Moreover, the discriminant function analysis confirmed that VGAM volume has a marginal role in predicting the NAR outcome as compared to other well-known neuroimaging findings (i.e., JB and SSS stenosis and straight sinus dilatation). These findings further support recent evidence that median prosencephalic varix size itself is not a predictor of clinical evolution to heart failure requiring early endovascular intervention or death in the neonatal period [12, 17]. As such, the relationship between VGAM volume and the other venous predicting factors is independent from the outcome and likely due to different hemodynamic factors. Also, no relationship was noted between VGAM volumetry and either the dosage of inotropic drugs required to maintain clinical stability in the neonatal period or the occurrence of post-embolization complications. In addition, in contrast with the study of Paladini et al. [19], VGAM volume was not predictive of brain damage, including ischemic/hemorrhagic lesions, white matter signal alterations, and global brain atrophy. Finally, despite the presence of possible bias due to different intervening factors, we did not find a relationship between the VGAM volume and long-term outcome, including levels of functional ability and disability and the presence of epilepsy at last follow-up.

The major limitations in this study are the retrospective design and small sample size, in keeping with the rarity of the disorder. Regarding the volumetric analysis, in some cases, there was a discrepancy between the results obtained with the linear measurements and semiautomated method due to anatomical reasons, underlying the necessity of manual refinement to draw the lesion boundary in complex cases. However, in our opinion, the reduction of operator variability and the relative simplicity of the method outweigh this problem. Among other limitations, we did not use digital subtraction angiography to confirm the data obtained from brain MRI and MRA; indeed, angiography was performed in newborns only for therapeutic purposes, using a limited amount of contrast for diagnostic runs due to the small age and weight of the patient. As such, full replenishment of the pouch was purposedly not achieved in most cases. Moreover, the positioning of the head during angiography often leads to oblique sagittal and coronal plane acquisitions, hindering precise measurements. Conversely, our MRI protocols were performed with strict rules for the alignment of the sagittal (with midline planes always including both the cerebral aqueduct and pituitary stalk), coronal (oriented along the floor of the fourth ventricle), and axial sequences (aligned along the bi-commissural plane). Notably, most of our cases were treated with a biplanar angiography suite in which 3D rotational acquisitions were not technically available, representing a further limitation against the possibility of obtaining multiplanar reformatting.

Finally, there is a possible influence of changes in neurointerventional devices on the long-term outcome during the wide interval time of the study. Indeed, it is possible that the post-embolization complication rates have been reducing over the years with better clinical outcome, thanks to increased operator experience and skills, improved patient selection, and better devices and technology. However, as reported by Brinjiki et al. in their systematic review and metanalysis [37], there is not enough data to determine clinical and angiographic outcomes by type of embolic agent or device used, and it is thus difficult to sort out short- and long-term morbidity and mortality related to the endovascular treatment. Recently, Bathia et al. found that using a microcatheter with a distal outer diameter of more than 2.0 Fr was a significant predictor of poor neurological outcomes [38]. More studies are needed to understand the real impact of devices and technology on the long-term outcome of the patients with VGAM.

Conclusions

In summary, semiautomated segmentation and VGAM volumetry are feasible with low variability and better representation of the aneurysmal shape compared to linear measurements. This 3D tool is easy to use and may be particularly useful in research settings. We confirmed that VGAM volume is not an independent predictor of short- and long-term outcome, but rather an imaging correlate of the associated venous abnormalities, such as the SSS narrowing, JB stenosis, and straight sinus dilatation. Future prospective studies on larger cohorts are needed to validate this approach and to shed light on the relationship between the aneurysm volume and VGAM hemodynamics.

Acknowledgements

Funding

This work was supported by funds from “Ricerca Corrente sui Disordini Neurologici e Muscolari – Neuroscienze (Linea 4)” of the Italian Ministry of Health.

Declarations

Footnotes

References