Shimada et al.
Egyptian Pediatric Association Gazette(2023) 71:85
https://doi.org/10.1186/s43054-023-00232-4

Egyptian Pediatric
Association Gazette

Open Access

RESEARCH

Proposal for diagnosis using FLAIR image
aimed for pediatric MELAS with recurrent
stroke‑like episodes on MRI system cannot take
ASL imaging
Makoto Shimada1,2* , Tae Ikeda3, Ryohei Fukui4, Katsuhiro Kida4, Ryutaro Matsuura4, Takuya Akagawa5 andSachiko Goto4

Abstract
Background Arterial spin-labeling (ASL) imaging is currently the most useful method for diagnosing mitochondrial
encephalomyopathy, lactic acidosis, and stroke-like attack syndrome (MELAS). However, ASL is often an optionalfeature of standard MRI systems. Therefore, not all MRI systems can perform ASL imaging. In contrast, fluid-attenuatedinversion recovery (FLAIR) imaging is one of the common sequences in brain MRI because FLAIR imaging can be performed regardless of the specifications of the equipment. This study aimed to compare the diagnostic performanceof quantitative analysis of signal intensity obtained from fluid-attenuated inversion recovery (FLAIR) images with ASL
images for MELAS with recurrent stroke-like episodes (SLEs). A total of 68 cases with normal magnetic resonanceimaging findings and 25 cases diagnosed MELAS with recurrent SLEs were included. We evaluated the frontal lobe
and cuneus as target areas and compared the regional cerebral blood flow (rCBF) values obtained from ASL images
with the normalized signal intensity (nSI) obtained from FLAIR images.
Results The sensitivity and specificity for diagnosing MELAS from linear discriminant analysis (LDA) obtained
from the rCBF values were 0.84 and 0.941, respectively, and those of nSI were 0.8 and 0.897, respectively. The area
under the ROC curves (AUC) calculated from the receiver operating characteristic (ROC) curve analysis using rCBF
values and nSI were 0.889 and 0.804, respectively.
Conclusion Quantitative analysis using the signal intensity of the FLAIR image could have a diagnostic performanceequivalent to that of rCBF values obtained from ASL images.
Keywords Mitochondrial encephalomyopathy lactic acidosis and stroke-like attack syndrome, Stroke-like episodes,
Fluid-attenuated inversion recovery, Arterial spin labeling

Institution where the study was conducted: Osaka Women’s and Children’s
Hospital*Correspondence:
Makoto Shimada
itami1309@gmail.com
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Shimada et al. Egyptian Pediatric Association Gazette(2023) 71:85

Background
Mitochondrial encephalomyopathy, lactic acidosis, andstroke-like attack syndrome (MELAS) are common clinical forms of mitochondrial disease, a multisystem disorder that occurs in childhood in approximately 70% ofcases [1–3]. MELAS is characterized by stroke-like episodes (SLEs), such as headache, vomiting, convulsions,
impaired consciousness, and motor paralysis of theextremities [4]. A typical MELAS lesion is an edematous
lesion centered in the cerebral cortex that is not consistent with vascular control areas [5]. SLE lesions often
appear from the unilateral cuneus to the temporal lobe,
and distinguishing MELAS from cerebral infarction isimportant. SLE lesions appear as high-signal areas on diffusion-weighted images; however, the apparent diffusion
coefficient values of SLE lesions do not show a strongly
decreased intensity area compared with cerebral infarctions. MELAS is often associated with dilated vessels due
to vascular endothelial dysfunction, resulting in foci of
predominantly vasogenic edema due to extravasation [6];however, in the subacute phase, there may also be a mixture of cytotoxic edema in the foci. Arterial spin labeling(ASL) imaging is currently the most useful imaging fordifferentiating MELAS from cerebral infarction because
it can easily capture increased blood flow at SLE lesions[7, 8]. ASL uses an inversion pulse to magnetically label
the arterial blood flowing into the imaging cross section,
which is then used as a tracer [9], and a labeling pulse isapplied. It is a non-invasive imaging method that measures brain perfusion by collecting data after a delay time
between the application of the labeling pulse, called postlabeling delay, and data collection. Recently, magnetic
resonance imaging (MRI) has seen rapid technologicaladvances in pulse sequencing and the proliferation of
hardware, such as high-field devices and high-sensitivity
phased array coils. However, ASL is often an optionalfeature of standard MRI systems. Therefore, not all MRI
systems can perform ASL imaging. In contrast, fluidattenuated inversion recovery (FLAIR) imaging is oneof the common sequences in brain MRI because FLAIR
imaging can be performed regardless of the specificationsof the equipment [10, 11]. FLAIR is an imaging techniquethat suppresses the cerebrospinal fluid signal by scanningat a time when the longitudinal magnetization of the cerebrospinal fluid becomes negative when a 180° inversion
pulse is applied and then returns to zero. FLAIR is characterized by lesions in contact with cerebrospinal fluid,
such as the cortex and periventricular walls, which are
more easily visualized than T2-weighted imaging. This
study aimed to compare the diagnostic performance ofquantitative analysis of signal intensity obtained fromFLAIR images with ASL images for MELAS with recurrent SLEs.

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Methods
Subjects

Sixty-eight cases were included as the normal group, as
they were diagnosed with normal findings and without
developmental delay or organic abnormalities in thebrain parenchyma on MRI. Additionally, 25 cases diagnosed MELAS with recurrent SLEs were included as the
MELAS group. The normal group included 28 males
and 40 females with a median age of 8.5 years (range
5 months to 14 years). The MELAS group included 25
females with a median age of 11.17 years (range 9–14
years). All cases underwent ASL and FLAIR imaging
on brain MRI scans from May 2015 to May 2021 in our
hospital. All cases underwent MRI examination withclosed eyes.
Image acquisition

MRI images were obtained using an Optima 450w 1.5T
scanner (GE Healthcare, Milwaukee, WI, USA). A
16-channel GEM Head Neck Unit coil was used as thereceiving coil. The imaging parameters of the ASL and
FLAIR images are listed in Table 1.
Regional cerebral blood flow calculation

The frontal lobe and cuneus were evaluated as target areas at the basal ganglia level on transverse ASL
images. As aforementioned, we chose the cuneus as thetarget area because of the increased blood flow in the
cuneus in MELAS cases. The frontal lobe was selected
because it is the region furthest from the cuneus. The
console attached to the MRI scanner was used to locate
three circular regions of interest (ROIs), each 50 ­mm2
in size, on both sides of the frontal lobe and cuneus.
Regional cerebral blood flow (rCBF) was measured inTable 1 Imaging parameters of ASL and FLAIR images

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the ROIs, and the mean value of rCBF obtained fromthe six ROIs was used in the analysis (Fig. 1). This procedure was performed in all cases in the normal and
MELAS groups.

of cerebrospinal fluid on signal intensity, and the signal intensity in the ROI was averaged for normalization(Fig. 2b).

Signal intensity of FLAIR images

Spearman’s rank correlation coefficient was calculatedusing the rCBF values and nSI obtained from the frontal lobe and cuneus in all cases [12]. To distinguishbetween the normal and MELAS groups, linear discriminant analysis (LDA) was performed to obtain thelinear decision boundary, which was estimated from ascatter plot of the rCBF values and nSI. Fisher’s method
was employed to analyze LDA results [13]. The sensitivity and specificity were determined using these decisionboundaries. Additionally, receiver operating characteristic (ROC) curves and the area under the ROC curve(AUC) were estimated using the rCBF values and nSI
in the frontal lobe and cuneus. The DeLong method
was employed for statistical analysis of the two AUCs

The frontal lobe and cuneus on transverse FLAIR images
were also evaluated as well as ASL images. A circular
ROI of 200 m­ m2 and oval ROI of 200 ­mm2 were locatedon each side of the frontal lobe and cuneus, respectively (Fig. 2a). The signal intensity in the ROIs wasmeasured, and the mean signal intensity was calculated.
However, the signal intensities of the frontal lobe andcuneus obtained from the FLAIR images were relativevalues, unlike rCBF, which is an absolute value. Therefore, the mean signal intensity was normalized to thesignal intensity in the brainstem. This is referred to as
the normalized signal intensity (nSI). A 180-mm2 circular ROI was located on the brainstem to avoid the effectStatistical analysis

Fig. 1 Locations of 12 ROIs in the frontal lobe and cuneus for measuring rCBF values using ASL images. Each circular ROI has an area of 50 m
­ m2

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Fig. 2 Location of the ROI of the frontal lobe, cuneus, and brainstem for measuring normalized signal intensity using FLAIR images. a The circle ROIof the frontal lobe has an area of 200 ­mm2. The oval ROI of the cuneus has an area of 200 ­mm2. b The circle ROI of the brainstem has an area of 180­mm2

to compare the diagnostic performance of the imaging
modalities for MELAS [14]. We used R Studio (version
2023.06.1+524, posit, Boston, USA) to perform the statistical analysis, with a significance level of 0.05.

Results
The correlation coefficients of rCBF values for the frontal lobe and cuneus in the normal and MELAS groups
were 0.47 and 0.46, respectively. Those of nSI were 0.871
and 0.746, respectively. Scatter plots of the rCBF values
and nSI for the normal and MELAS groups are shownin Figs. 3 and 4. We also show the decision boundaries
estimated from LDA, distinguishing between the normal and MELAS groups. From the decision boundaries of rCBF values, the sensitivity and specificity forthe diagnosis of MELAS were 0.84 and 0.941, respectively, and those of nSI were 0.8 and 0.897, respectively(Table 2).
The four clinical images diagnosed MELAS are shownin Fig. 5. In the positive case diagnosed with MELAS,
based on the decision boundaries obtained from the
LDA of the rCBF values, there was a clear increase inblood flow from the right cuneus to the temporal lobe
(Fig. 5a). In the positive case diagnosed with MELAS
from the LDA of nSI, there was a clear T2 prolongation
area in the right occipital lobe and from the left cuneus
to the left temporal lobe, and increased blood flow on
ASL images consistent with the same area (Fig. 5b). In
the case that was not distinguished as MELAS using
the decision boundaries obtained from the rCBF values

and nSI, the right cuneus to the right occipital lobe
was the site of the lesion; however, there was no obvious increase in blood flow and area of T2 prolongation(Fig. 5c). In the case of MELAS diagnosed on the border of the decision boundary obtained from the LDA
of nSI, the right cuneus to the right temporal lobe was
the site of the lesion, but there was no obvious T2 prolongation area, while ASL images showed an obvious
increase in blood flow in the same area (Fig. 5d). The
AUCs for the ASL and FLAIR images were 0.889 and0.804, respectively, as estimated from the ROC curves
using the rCBF values and nSI (Fig. 6). There was no
significant difference between the two AUC values (p =0.173).

Discussion
In this study, we examined the clinical utility of FLAIR
imaging for the diagnosis of MELAS with recurrent
SLEs compared to ASL imaging. The results showedthat the rCBF values obtained from ASL images andthe nSI obtained from FLAIR images were equivalent
to their diagnostic performance in MELAS cases. We
calculated Spearman’s rank correlation coefficient using
the rCBF values and found no correlation between thenormal and MELAS groups. We considered that thedegree of increased blood flow in recurrent SLEs varied
by case. However, as shown in Fig. 3, the overall rCBF
values were higher in the MELAS cases than in the normal cases, especially in the cuneus. Therefore, only theShimada et al. Egyptian Pediatric Association Gazette(2023) 71:85

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Fig. 3 Results of rCBF values obtained from ASL images and the decision boundary calculated using LDA

degree of increased blood flow can easily indicate anabnormality of the brain; this is consistent with findingsof previous reports [7, 8]. In contrast to the rCBF values, there was a strong positive correlation in the nSI
results. The high correlation of the nSI obtained fromFLAIR images is due to the smaller signal intensity in
MELAS cases and normalization of the signal intensity by dividing it by the signal intensity of the brainstem. We considered that the correlation coefficientof nSI was also higher because normalization allowed
the variation in signal values to be compared between
cases. Based on the decision boundaries obtained from
LDA, both the sensitivity and specificity for diagnosing MELAS using the rCBF value were higher thanthose of the nSI; however, there were few differences.
Regarding sensitivity, in the positive cases diagnosed
with MELAS on the border of the decision boundary
obtained from the LDA of nSI, one MELAS-positive
case could be distinguished by the ASL image, which

was the cause for the slight difference. We consideredthat the difference in specificity was due to the larger
rCBF values of cuneus in MELAS cases than in normalcases, and the smaller signal intensity in MELAS cases
and the smaller difference between MELAS and normalcases. Care must be taken in cases of MELAS that cannot be distinguished using either rCBF values or nSI.
In this study, MRI examination of recurrent SLEs was
performed; however, there were no abnormalities on
the ASL or FLAIR images (Fig. 5c). Li et al. reported
that persistent cellular injury due to impaired adenosine triphosphate production may give rise to excessive loss of neurons, resulting in decreased rCBF in
the affected brain regions during the chronic phase of
SLEs [7]. Therefore, our method is not appropriate forthese cases, and we need to perform a comprehensive
diagnosis in conjunction with physical findings andother examination results. We calculated AUC values
from ROC analysis using rCBF values and the nSI, andShimada et al. Egyptian Pediatric Association Gazette(2023) 71:85

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Fig. 4 Results of the normalized signal intensity obtained from FLAIR images and the decision boundary

Table 2 Sensitivity and specificity for MELAS cases obtained
from the LDA

recognized that ASL images have better diagnostic performance than FLAIR images.
However, there was no statistically significant difference between the AUC values obtained from the ASL and
FLAIR images. On the basis of the results, we concluded
that the diagnostic performances of ASL and FLAIR
images for MELAS with recurrent SLEs were equivalentusing our method.

Edematous lesions with cortical predominance may
persist long after symptom resolution, although FLAIR
high-signal lesions may shrink in size. Using thismethod, it is possible to determine whether SLEs haverecurred in residual FLAIR high-signal lesions that are
difficult to assess visually. In addition, although the casesin this study were obtained at the time of recurrent
SLEs, a case was reported in which ASL image over time
captured increased blood flow in the preictal lesion, andit was also reported that ASL imaging was useful in predicting seizures [15, 16]. If our method has a diagnostic
performance equivalent to that of ASL imaging, we suggest that FLAIR imaging may also be useful in predicting
seizures.
A limitation of this study was the small number of
MELAS cases; however, we considered that a population
trend was obtained when considering the distribution of
the scatter plots. In addition, both angioedema and cellular edema show high signal intensity on FLAIR images

Shimada et al. Egyptian Pediatric Association Gazette(2023) 71:85

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Fig. 5 MELAS cases analyzed in this study. Positive cases were determined by the decision boundary obtained from the a rCBF value and b nSI. c
Cases in which MELAS could not be diagnosed using rCBF or nSI. d Case located on the decision boundary obtained from nSI

Conclusions
Quantitative analysis of the signal intensity in FLAIR
images had a diagnostic performance equivalent to thatof the rCBF values obtained from ASL images when diagnosing MELAS with recurrent SLEs. Using our method,
MELAS can be diagnosed even in facilities with MRI system that cannot perform ASL imaging.

Fig. 6 ROC curves for diagnosing MELAS by using the ASL and FLAIR
imagesand may not be discriminated based solely on the signal
intensity of the FLAIR image. Thus, we consider that thediagnosis of such cases should be proceeded with othersequences.

Abbreviations
MELAS	Mitochondrial encephalomyopathy lactic acidosis and stroke-like
attack syndrome
SLEs	Stroke-like episodes
ASL	Arterial spin labeling
MRI	Magnetic resonance imaging
FLAIR	Fluid-attenuated inversion recovery
ROI	Regions of interest
rCBF	Regional cerebral blood flownSI	Normalized signal intensity
LDA	Linear discriminant analysis
AUC​	Area under the ROC curve
Authors’ contributions
MS designed the study, collected and analyzed the data, and wrote this manuscript. TI performed data collection, analysis, and paper review. RF provided
data analysis methodology. KK, RM, TA, and SG discussed the results of theanalysis and checked the text of this paper.
Availability of data and materials
The datasets generated and/or analyzed during the current study are notpublicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available
from the corresponding author on reasonable request.

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Declarations
Ethics approval and consent to participate
The study protocol and use of clinical images were approved by the Ethical
Review Committee of Osaka Women’s and Children’s Hospital (approval
number: 1356).
Consent for publication
Not applicable.

12.
13.
14.
15.

Competing interests
The authors declare that they have no competing interests.
Author details
1
Faculty of Health Sciences Graduate School of Health Sciences, Okayama
University, Okayama Daigaku Igakubu Hoken Gakka Daigakuin Hokengaku
Kenkyuka, 2‑5‑1, Shikata‑cho, Kita‑ku, Okayama 700‑8558, Japan. 2 Departmentof Radiology, Osaka International Cancer Institute, 3‑1‑69Chuo‑ku, OtemaeOsaka 541‑8567, Japan. 3 Department of Pediatric Neurology, Kitakyushu City
Yahata Hospital, 2‑6‑2Yahatahigashi‑ku, OguraKitakyushu, Fukuoka 805‑8534,
Japan. 4 Department of Radiological Technology, Faculty of Health Sciences,
Okayama University, 2‑5‑1, Shikata‑cho, Kita‑ku, Okayama 700‑8558, Japan.
5
Department of Radiological Technology, Tokushima Red Cross Hospital, 103,
Irinokuchi, Komatsushima‑cho, Komatsushima, Tokushima 541‑8567, Japan.
Received: 16 October 2023 Accepted: 23 October 2023

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