Keywords MRI - T1 mapping - liver fibrosis - chronic liver disease - nonfunctional liver volume
Introduction
Chronic liver disease (CLD) affects approximately 1.5 billion people worldwide [1 ]. Complications of CLD such as liver cirrhosis associated adverse outcomes and hepatocellular
carcinoma are responsible for 3.5% of the deaths worldwide [2 ]. Because the early stages of CLD can be reversible, it is crucial to have a method
that can detect the disease before it becomes permanent and complications develop
[3 ].
The current reference standard for CLD diagnosis is liver biopsy [4 ]. However, liver biopsy is not suitable for early detection due to its invasiveness
[5 ]. Non-invasive diagnostic approaches include serum biological markers, ultrasound
(US) elastography, magnetic resonance elastography (MRE), and morphological assessment
of the liver [3 ]. Biological markers can be easily measured in clinical practice by a laboratory
test [6 ], but are not specific to hepatic fibrosis [3 ]. US elastography is widely available, well reproducible, and allows an accurate
diagnosis of liver cirrhosis [7 ]. However, the diagnostic performance is not optimal for early fibrosis, and there
is an overlap between adjacent stages and some methodological limitations [7 ]. MRE is more accurate and reliable than US elastography [7 ]. Still, it is a method that is not widely available, as it requires special and
expensive hardware [6 ].
Changes in liver morphology can be evaluated by ultrasonography (US), computer tomography
(CT), or magnetic resonance imaging (MRI), widely available modalities [3 ]. Typical morphological features of liver cirrhosis encompass nodularity of the hepatic
surface, right hepatic posterior notch, narrowing of the hepatic veins, enlargement
of the gallbladder fossa, and expansion of the hilar periportal space [3 ]. Liver fibrosis also causes a volume increase in the hepatic left lateral section
and caudate lobe (segments I–III) and decrease in the right and medial left lobe (segments
IV–VIII) [8 ]. Various measures, such as the liver segmental volume ratio (LSVR) and caudate to
right lobe ratio (CRL-R), have been introduced to quantify the volume changes associated
with fibrosis [9 ]
[10 ]
[11 ]
[12 ].
In addition, parameters capable of estimating liver tissue composition were identified:
T1 relaxation time has been shown to be increased in liver fibrosis [4 ]
[13 ]
[14 ]
[15 ]
[16 ]
[17 ]
[18 ]
[19 ], steatosis, and inflammation [13 ]
[19 ], and decreased in case of iron overload [13 ], while T2 relaxation time rather correlates with inflammation [20 ]
[21 ]. It has also been proven that the extracellular volume (ECV), calculated using the
T1 relaxation time of the liver and the blood pool before and after injection of a
contrast agent that only distributes in the extracellular compartment of the liver,
is increased in case of fibrosis [4 ]
[14 ]
[16 ]
[22 ], and is independent of the MRI field strength, in contrast to T1 and T2 relaxation
times [14 ].
We assume that the use of a contrast medium that enters the hepatocytes artificially
increases the ECV and could be used to estimate the hepatic function. For this reason,
we defined the functional liver fraction (FLF) for contrast media that enters the
hepatocytes. We hypothesize that the combination of volumetry and FLF could be a marker
of liver fibrosis. Therefore, we created a new parameter, the NFLV, calculated as
(1 – FLF ) × Lvol . The aim of this study was to investigate whether FLF and NFLV are associated with
CLD.
Material and Methods
Study Population
This retrospective study was approved by the Institutional Review Board (Cantonal
ethics committee, Bern, Switzerland, Project ID 2019-01333). All patients involved
provided written informed consent.
Our Radiological Information System (RIS) was screened for multiparametric 3T abdominal
MRI enhanced with gadobenate dimeglumine (Gd-BOPTA, Multihance, Bracco Imaging, Milan, Italy) and performed between September 4, 2018 and May 20, 2020. Out of 532 exams, 382 were
excluded because they did not feature a complete parametric T1 mapping before and
20 minutes after intravenous contrast injection. In addition, 19 scans were excluded
due to lack of hematocrit value, 11 because they were repeated examinations performed
on the same patient, and 66 due to conditions, other than liver fibrosis, influencing
the T1 relaxation time or the liver volume such as: pathologies of the biliary tract
(n=32); large or multiple focal liver lesions (n=11); status post liver transplantation
(n=10); status post liver ablation surgery (n=8); iron overload (n=3); portal vein
thrombosis (n=1); and cerebral death with unclear hepatopathy (n=1). Finally, three
scans were excluded due to significant image artifacts. The final study population
consisted of 51 scans.
The patients were divided into two groups depending on whether they had a diagnosis
of chronic liver disease in their medical history (CLD group), defined as a disease
of the liver leading to a progressive deterioration of the liver function lasting
over six months [23 ], or not (noCLD group). The CLD group was composed of 20 patients, whereas the noCLD
group consisted of 31 patients. [Fig. 1 ] represents a flowchart of the selection of the study population and the repartition
of the patients in the groups.
Fig. 1 Flowchart of the selection of the study population and the repartition of the patients
in the two groups.
Clinical data
All information regarding the patient’s clinical status was retrieved from their medical
electronic records. The following parameters were gathered: CLD etiology, sex, age,
size, weight, body mass index (BMI), hypertension status, diabetes status, alcohol
consumption, creatinine level, albumin level, bilirubin level, aspartate aminotransferase
(AST) level, alanine aminotransferase (ALT) level, alkaline phosphatase (ALP) level,
gamma-glutamyl transferase (GGT) level, thrombocytes count, hematocrit, Quick, Fibrosis-4
Index (FIB-4) and AST to Platelet Ratio Index (APRI). The Child-Pugh score was also
calculated for all patients with chronic liver disease. Laboratory values from 3 months
before or after the MRI were considered to reflect the patient’s state of health at
the time of the examination.
Image acquisition
All images were acquired on 3T MRI scanners (Magnetom Skyra or Prisma, Siemens Healthineers, Erlangen, Germany) . The images were obtained following a standard liver imaging protocol including morphological
T1w, T2w, diffusion-weighted images, and VIBE Dixon sequences, as well as parametric
T1 mapping before and 20 minutes after intravenous injection of 0.1 mmol/kg bodyweight
of gadobenate dimeglumine (Gd-BOPTA, Multihance, Bracco Imaging, Milan, Italy) .
The T1 mapping was obtained using a modified look-locker inversion recovery (MOLLI)
variant sequence with a 3–3-5 design and consisted of four single breath-hold (11
s) axial slices placed over the liver with a spacing of 300% (24 mm). The following
specifications were used: echo time (TE) of 1.01 ms, repetition time (TR) of 740 ms,
inversion time (TI) of 225 ms, flip angle (FA) 35°, slice thickness of 8 mm, field-of-view
(FOV) of 306 × 360 mm, and matrix of 154 × 192 pixels.
The native T1-VIBE Dixon in phase acquisition was used as sequence for the liver segmentation
and consisted of 60 to 80 axial slices of 3 mm thickness with axial dimensions between
210 × 320 and 270 × 320 pixels, and pixel spacing between 1.09375 × 1.09375 and 1.5625 × 1.5625
mm2 .
The LiverLab (Siemens Healthineers, Erlangen, Germany) with a multi-echo Dixon VIBE sequence resulting in a fat fraction map, a R2* map
and a Goodness of Fit map was also acquired before contrast injection und used for
the proton density fat fraction (PDFF) measurement.
Images analysis
The image analysis was performed by a trained medical student and validated by a board-certified
radiologist (10 years of experience in hepatobiliary imaging), using a picture archiving
and communication system (Sectra IDS7, Sectra, Linköping, Sweden) .
If the Liverlab image quality was good enough (Goodness of fit <50‰), the PDFF was
measured directly by drawing circular regions of interest (ROIs) on the fat fraction
screening map of the Liverlab. In case of poor image quality, PDFF was calculated
using the T1w VIBE Dixon sequences.
Pre- and postcontrast T1 relaxation times of the liver were measured before and after
contrast injection by delineating and averaging nine polygonal regions of interest
(ROIs) corresponding to the hepatic segments in a slice above and a slice under the
portal vein bifurcation, avoiding focal liver lesions, vessels, and artifacts. ROIs
were drawn with at least 1 cm separation from the edges of the liver to prevent partial
volume effects. Additionally, efforts were made to place the ROIs consistently across
different maps. T1 relaxation time of the aorta was obtained by placing a circular
ROI in the center of the aorta on the four slices of the T1 mapping and averaging
the values. Elements that could distort the measurement, such as image artifacts and
calcifications, were avoided. [Fig. 2 ] shows an example of the T1 relaxation time measurements.
Fig. 2 Example of the T1 relaxation time measurements of the liver and aorta. The measurements
were performed on the liver of a 22-year-old female patient without CLD and with a
hematocrit of 0.36. a Precontrast T1map, slice above the portal vein bifurcation. b Precontrast T1map, slice under the portal vein bifurcation. c Postcontrast T1map, slice above the portal vein bifurcation. d Postcontrast T1map, slice under the portal vein bifurcation. Total precontrast T1
relaxation time of the liver: 808ms. Precontrast T1 relaxation time of the liver segments
I–III: 810ms. Precontrast T1 relaxation time of the liver segments IV–VIII: 807ms.
Precontrast T1 relaxation time of the aorta: 1993ms. Total postcontrast T1 relaxation
time of the liver: 330ms. Postcontrast T1 relaxation time of the liver segments I–III:
328ms. Postcontrast T1 relaxation time of the liver segments IV–VIII: 332ms. Mean
post-contrast T1 relaxation time of the aorta: 381ms.
Total and segmental liver volume (Lvol) were determined with the help of a convolutional
neural network with a U-net architecture (nnU-Net) trained to delineate the segments
of the liver and its vessels on the native T1 Dixon sequence [24 ]
[25 ]. The segmentations performed by the artificial intelligence (AI) model were then
imported into a 3D segmentation software (ITK-SNAP) and were visually checked for
inaccuracies. If inaccurate, the liver segmentation was manually adjusted. The software
then calculated the liver volumes by multiplying the number of voxels by the sequence-specific
volume of one voxel. [Fig. 3 ] shows an example of 3D segmentation of the hepatic segments and vessels delineated
by the AI model.
Fig. 3 Segmentation of the hepatic segments and vessels delineated by the convolutional neuronal
network. The 3D segmentation was performed on the same 22-year-old female patient
without CLD as in Figure 2. The bile ducts are represented in green and the hepatic
veins in dark blue. a Segmentation in the coronal plane. b Segmentation in the axial plane. c Segmentation in the sagittal plane. d 3D visualization. Total predicted Lvol: 1286ml. Predicted Lvol of the segments I–III:
269ml. Predicted Lvol of the segments IV–VIII: 1017ml. LSVR: 0.26. Total FLF: 0.54.
FLF of the segments I–III: 0.55. FLF of the segments IV–VIII: 0.54. Total NFLV: 592ml.
NFLV of the segments I–III: 122ml. NFLV of the segments IV–VIII: 472ml.
Liver segmental volume ratio (LSVR) was calculated using the formula available in
the literature [9 ]:
Functional liver fraction (FLF) was calculated using the following formula [14 ] for the whole liver, segments I–III, and IV–VIII:
Finally, nonfunctional liver Volume (NFLV) was calculated as Lvol × (1 – FLF ) for the whole liver, segments I–III, and IV–VII.
Statistical analysis
All statistical analyses were performed using GraphPad Prism software (GraphPad Software, San Diego, USA) . To compare the characteristics of the groups, the Mann-Whitney U test was used for
the continuous variables and Fisher’s exact test for the discrete variables. Mann-Whitney
U tests and ROC analyses were performed to compare the T1 relaxation time, Lvol, LSVR,
FLF, and NFLF between the groups. The optimal cutoff values were determined using
Youden’s index. The significance level was set as α = 5%.
Results
Characteristics of the study population
The study population consisted of 51 patients (25 men and 26 women) of which 20 had
CLD and 31 had no CLD. The mean age was 55 years with a standard deviation (SD) of
15 years. The etiologies of the liver disease in the CLD group were steatosis (defined
as PDFF>10%) (n = 6), metabolic dysfunction-associated steatotic liver disease (MASLD)
(n = 6), alcoholic fatty liver disease (AFLD) or alcoholic steatohepatitis (ASH) (n
= 2), drug induced liver injury (DILI) (n = 2), hepatitis B (n = 1), and hepatopathy
of unknown etiology (n = 3). The distribution of CLD patients in Child-Pugh classes
was as follows: 18 Child-Pugh A (Child-Pugh score 5–6), 1 Child-Pugh B (Child-Pugh
score 7–9), and 1 Child-Pugh C (Child-Pugh score 10–15). [Table 1 ] shows further characteristics of the study population.
Table 1
Characteristics of the groups. Discrete values are written as the number of patients (n) with the percentage in parentheses
and continuous data as mean ± standard deviation (SD). Data are rounded to the nearest
unit except for APRI and FIB-4 index, which are rounded to the nearest tenth. A Mann-Whitney
U test was used to calculate the P-values for the continuous variables and Fisher’s
exact test was used to calculate the P-values for the discrete values. Non-significant
P-values are rounded to the nearest hundredth, and significant P-values are written
as <0.05, <0.01, or <0.001.
noCLD (n = 31)
CLD (n = 20)
P-value
BMI: body mass index; PDFF: proton density fat fraction; AST: aspartate aminotransferase;
ALT: alanine aminotransferase; ALP: alkaline phosphatase; GGT: gamma-glutamyl transferase;
APRI: aspartate aminotransferase-to-platelet ratio index; FIB-4: Fibrosis-4 Index.
Male (n, %)
11 (35%)
14 (70%)
P < 0.05
Diabetes (n, %)
0 (0%)
3 (15%)
P = 0.05
Hypertension (n, %)
12 (39%)
10 (50%)
P = 0.56
Alcohol consumption (n, %)
7 (23%)
11 (55%)
P = 0.03
Age [years]
55 ± 15
55 ± 15
P = 0.94
Size [cm]
170 ± 9
171 ± 8
P = 0.82
Weight [kg]
75 ± 19
82 ± 19
P = 0.23
BMI [kg/m2 ]
25 ± 5
28 ± 7
P = 0.13
PDDF [%]
3 ± 4
12 ± 12
P < 0.01
Creatine [µmol/L]
70 ± 14
74 ± 15
P = 0.36
Albumin [g/L]
36 ± 6
37 ± 6
P = 0.35
Bilirubin [µmol/L]
9 ± 4
37 ± 112
P = 0.10
AST [U/L]
27 ± 17
69 ± 86
P < 0.001
ALT [U/L]
45 ± 102
100 ± 165
P < 0.001
ALP [U/L]
75 ± 56
130 ± 167
P < 0.05
GGT [U/L]
65 ± 163
162 ± 227
P < 0.001
Thrombocytes [G/L]
249 ± 80
243 ± 78
P = 0.86
Hematocrit [%]
38 ± 5
41 ± 4
P < 0.05
Quick [%]
97 ± 13
96 ± 21
P = 0.68
FIB-4
1 ± 0.4
1.7 ± 1.1
P < 0.05
APRI
0.3 ± 0.2
0.7 ± 0.6
P < 0.001
Descriptive statistics
Native T1 relaxation time was significantly higher in patients with CLD for the whole
liver (897ms [832, 976ms] vs. 818ms [762, 866ms]; p<0.01), for segments I–III (902ms
[828, 987ms vs. 814ms [773, 879ms]; p<0.01), and for segments IV–VIII (893ms [835,
975ms] vs 825ms [752, 867ms]; p>.01).
Total Lvol was significantly higher in patients with CLD than in patients without
CLD (1532ml [1313, 2015ml] vs. 1280ml [1074, 1523ml]; p<0.01). Lvol of the segments
I–III was also significantly higher in patients with CLD than in patients without
CLD (356ml [288, 422ml] vs. 255ml [216, 297ml]; p<0.001), as well as Lvol of the segments
IV–VIII (1167ml [993, 1546ml] vs. 997 [849, 1259ml]; p<0.01. LSVR was not significantly
different in patients with CLD than without (0.27 [0.23, 0.37) vs. 0.25 [0.22, 0.29];
p=0.25).
There was no significant difference in total FLF between the groups (0.45 [0.38, 0.53]
vs. 0.49 [0.41, 0.55]; p=0.23). The same applied for FLF of segments I–III (0.47 [0.38,
0.54] vs. 0.49 [0.41, 0.56]; p=0.31) and segments IV–VIII (0.44 [0.37, 0.52] vs. 0.48
[0.41, 0.55]; p=0.20).
Total NFLV was increased in patients with CLD compared to patients without CLD (894ml
[691, 1035ml] vs. 643ml [543, 733ml]; p<0.01). NFLV of segments I–III (185ml [157,
240ml] vs. 125ml [101, 162ml]; p<0.001) and segments IV–VIII (658ml [556, 824ml] vs.
899ml [442, 572ml]; p<0.01) were also significantly different between the groups.
The descriptive statistics are available in [Table 2 ].
Table 2
Descriptive statistics of the total and segmental Lvol, FLF, NFLV, and LSVR. Data are written as median [Q25; Q75]. Non-significant P-values are rounded to the
nearest hundredth, and significant P-values are written as <0.05, <0.01, or <0.001.
The Lvol predicted by the AI model was used without adjustment for 17 patients from
the group with CLD and 30 patients from the group without CLD. The segmentation was
manually adjusted by 3 patients from the group with CLD and 1 patient from the group
without CLD.
noCLD (n = 31)
CLD (n = 20)
P-value
Native T1 relaxation time
818ms [762, 866ms]
897ms [832, 976ms]
P < 0.01
814ms [773, 879ms]
902ms [828, 987ms]
P < 0.01
825ms [752, 867ms]
893ms [835, 975ms]
P < 0.01
Postcontrast T1 relaxation time
346ms [327, 375ms]
352ms [314, 393ms]
P = 0.98
346ms [324, 374ms]
347ms [288 387ms]
P = 0.66
346ms [324, 373ms]
358ms [315, 396ms]
P = 0.73
Liver volume (Lvol)
1280ml [1074, 1523ml]
1532ml [1313, 2015ml]
P < 0.01
255ml [216, 297ml]
356ml [288, 422ml]
P < 0.001
997 [849, 1259ml]
1167ml [993, 1546ml]
P < 0.01
Liver segmental volume ratio (LSVR)
0.25 [0.22, 0.29]
0.27 [0.23, 0.37]
P = 0.25
Functional liver fraction (FLF)
0.50 [0.41, 0.55]
0.45 [0.38, 0.53]
P = 0.23
0.49 [0.41, 0.56]
0.47 [0.38, 0.54]
P = 0.32
0.49 [0.41, 0.55]
0.44 [0.37, 0.52]
P = 0.20
1 – functional liver fraction (1 – FLF)
0.50 [0.45, 0.59]
0.55 [0.47, 0.62]
P = 0.23
0.51 [0.44, 0.59]
0.53 [0.46, 0.62]
P = 0.32
0.51 [0.46, 0.59]
0.56 [0.48, 0.63]
P = 0.20
Non-functional liver volume (NFLV)
643ml [543, 733ml]
894ml [692, 1035ml]
P < 0.01
125ml [101, 162ml]
185ml [157, 240ml]
P < 0.001
499ml [442; 572ml]
658 [556; 824ml]
P < 0.01
ROC analysis
The AUC of native T1 relaxation time was 0.75 (p<0.01) for the whole liver and 0.74
(p<0.01) for the native T1 relaxation time of segments I–III, as well as IV–VIII.
ROC analysis depicted an AUC=0.75 (p<0.01) for total Lvol, an AUC=0.80 (p<0.001) for
Lvol of the segments I–III, an AUC=0.73 (p <0.01) for Lvol of the segments IV–VIII,
and an AUC=0.60 (p=0.24) for LSVR. The AUC was 0.60 (p=0.23) for total FLF. NFLV allowed
differentiating between CLD and no CLD with an AUC=0.77 (p<0.01) when the total NFLV
was measured and even a higher AUC=0.78 (p<0.001), when only the NFLV of the segments
I–III was measured. AUC was only 0.75 (p<0.01) when the NFLV of only the segments
IV–VIII was measured. The optimal cutoff value was 745 ml for total NFLV and 573 ml
for NFLV of segments IV–VII, both having a sensitivity of 77% and a specificity of
75%. A sensitivity of 87% and a specificity of 70% were reached by a cutoff of 174ml
for NFLV of segment I–III. [Fig. 4 ] shows the ROC curves and [Table 3 ] resumes the AUC and P-values.
Fig. 4 ROC curves of native T1 relaxation time, Lvol, LSVR, FLF, and NFLV for discriminating
patients with and without CLD. The figure presents the native T1 relaxation time,
Lvol, FLF, and NFLV results for the whole liver (green), segments I–III only (red),
and segments IV–VIII only (blue).
Table 3
Results of the ROC analysis of native T1 relaxation time, Lvol, LSVR, FLF, and NFLV
for discriminating patients with and without CLD. AUC is rounded to the nearest hundredth. Non-significant P-values are rounded to
the nearest hundredth, and significant P-values are written as <0.05, <0.01, or <0.001.
The optimal cutoff value was determined using Youden’s index.
AUC
P-value
Youden’s index
Cutoff
Sensitivity
Specificity
Native T1 relaxation time
0.75
p < 0.01
0.47
867 ms
60%
87%
0.74
p < 0.01
0.45
891 ms
55%
90%
0.74
p < 0.01
0.47
879 ms
60%
87%
Liver volume (Lvol)
0.75
p < 0.01
0.39
1642 ml
94%
45%
0.80
p < 0.001
0.55
334 ml
90%
65%
0.73
p < 0.01
0.38
1079 ml
68%
70%
Liver segmental volume ratio (LSVR)
0.60
p = 0.24
0.24
0.30
84%
40%
Functional liver fraction (FLF)
0.60
p = 0.23
0.23
0.46
68%
55%
0.58
p = 0.31
0.18
0.51
48%
70%
0.61
p = 0.20
0.23
0.48
58%
65%
Non-functional liver volume (NFLV)
0.77
p < 0.01
0.52
745 ml
77%
75%
0.78
p < 0.001
0.57
174 ml
87%
70%
0.75
p < 0.01
0.52
573 ml
77%
75%
Discussion
This study shows that our introduced hepatic fibrosis biomarker, the nonfunctional
liver volume (NFLV) is increased in patients with CLD, especially when measured in
segments I–III. Since NFLV is non-invasive and does not require specific hardware,
it could be relatively easily incorporated in routine MRI examination evaluations
without unreasonably extending the examination time. Furthermore, besides time-consuming
manual segmentation, a delineation of the segments and vessels of the liver can be
achieved in less than one minute by a convolutional neural network [24 ].
FLF and NFLV are based on the property of Gd-BOPTA to enter the hepatocytes. Although
not statistically significant, our findings demonstrate a reduction in contrast medium
uptake in CLD patients, consistent with our initial assumption. Further corroborating
this hypothesis, the NFLV was significantly higher in the CLD group compared to the
noCLD group and its AUC is higher than the one of the Lvol for the whole liver and
segments IV–VIII. Another commonly used contrast medium in clinical practice that
also enters hepatocytes is gadoxetic acid (Gd-EOB-DTPA) [26 ]. Both contrast agents distribute into the extracellular space and are then taken
up by hepatocytes to be excreted into the biliary tract at 5% for Gd-BOPTA and 50%
for Gd-EOB-DTPA [26 ]. Due to this property, Gd-EOB-DTPA is presumably more suitable for assessing liver
function than Gd-BOPTA, and it would be interesting to use it to calculate the FLF
and NFLV.
The result concerning the liver volume showed an increase of the total Lvol in patients
with CLD compared with patients without CLD. However, several studies have shown that
the total liver volume does not adequately estimate liver fibrosis [12 ]
[27 ]. For example, Liu et al. found no significant difference in liver volume between
the different stages of liver fibrosis although it tended to increase form stage 1
to stage 3 and to decrease in stage 4 [27 ]. Nevertheless, our results are corroborated by Tarao et al. in a study showing an
increase in liver volume between patients with alcoholic steatosis compared to healthy
volunteers [28 ]. Furthermore, in a study by Kromrey et al., the liver volume estimated by ultrasonography
was shown to be affected by parenchymal liver disease, with hepatic steatosis increasing
the volume more than fibrosis or cirrhosis [29 ]. Because our CLD-group includes a majority of patients with steatosis, AFLD/ASH
or MASLD, and the mean PDFF was significantly higher than in the noCLD group, one
can postulate that the increase in Lvol is due to hepatocytes ballooning [28 ]. BMI, size, weight, gender, and age also influence the liver volume [29 ]
[30 ]. Our CLD group includes a higher proportion of male patients than the noCLD group;
however, the BMI, size, weight, and age are not significantly different between the
groups. It is therefore unlikely that these parameters have influenced the Lvol.
Despite the deep learning algorithm used to carry out the liver segmentation and delineation
of the vessel demonstrating high accuracy and strong correlation with manual calculation
[25 ], our LSVR results deviate from the literature [9 ]
[10 ]
[12 ]. This study revealed a significant augmentation of volume in segments I–III and
IV–VIII in patients with CLD compared to patients without CLD, and no significant
increase of LSVR, whereas fibrosis typically leads to a volume decrease in volume
in segments IV–VIII, an increase in segments I–III, and consequently an augmentation
of LSVR [10 ].
The limitations of this study include its retrospective design and the lack of use
of liver biopsy as the pathological reference standard. Furthermore, in the CLD group
encompassed many different etiologies. Patients were classified into the groups according
to the information present in the medical reports. For many of them, the diagnosis
of CLD was made without performing a biopsy. Another possible point of criticism is
the small group size, the relative underrepresentation of patients with CLD and the
predominance of males within the CLD group. Furthermore, the CLD group predominantly
consisted of patients classified as Child-Pugh A. This lack of diversity within the
cohort restricted the possibility of conducting meaningful stratified analyses. Further
validation of our results is now necessary on a larger patient cohort with Child-Pugh
stratification and histologically graded CLD.
Conclusion
This study suggests that MRI-derived nonfunctional liver volume (NFLV) is conducive
to early detection of imaging changes in CLD. NFLV is highly associated with CLD,
notably when measured in the liver segments I–III.
Clinical relevance
This study suggests that MRI-derived NFLV is conducive to early detection of imaging
changes in CLD.
Since it does not require specific hardware, the measurement of this parameter could
be relatively easily incorporated in routine MRI examination evaluations without unreasonably
extending the examination time.
