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Computed Tomography-Based Texture Features for the Risk Stratification of Portal Hypertension and Prediction of Survival in Patients With Cirrhosis: A Preliminary Study

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Objective Clinical evidence suggests that the risk stratification of portal hypertension (PH) plays a vital role in disease progression and patient outcomes. However, the gold standard for stratifying PH [portal vein pressure (PVP) measurement] is invasive and therefore not suitable for routine clinical practice. This study is aimed to stratify PH and predict patient outcomes using liver or spleen texture features based on computed tomography (CT) images non-invasively. Methods A total of 114 patients with PH were included in this retrospective study and divided into high-risk PH (PVP ≥ 20 mm Hg, n = 57) or low-risk PH (PVP < 20 mm Hg, n = 57), a progression-free survival (PFS) group ( n = 14), or a non-PFS group ( n = 51) based on patients with rebleeding or death after the transjugular intrahepatic portosystemic shunt (TIPS) procedure. All patients underwent contrast-enhanced CT, and the laboratory data were recorded. Texture features of the liver or spleen were obtained by a manual drawing of the region of interest (ROI) and were performed in the portal venous phase. Logistic regression analysis was applied to select the significant features related to high-risk PH, and PFS-related features were determined by the Cox proportional hazards model and Kaplan-Meier analysis. Receiver operating characteristic (ROC) curves were used to test the diagnostic capacity of each feature. Results Five texture features (one first-order feature from the liver and four wavelet features from the spleen) and the international normalized ratio (INR) were identified as statistically significant for stratifying PH ( p < 0.05). The best performance was achieved by the spleen-derived feature of wavelet.LLH_ngtdm_Busyness, with an AUC of 0.72. The only log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation feature from the liver was associated with PFS with a C-index of 0.72 (95% CI 0.566–0.885), which could stratify patients with PH into high- or low-risk groups. The 1-, 2-, and 3-year survival probabilities were 66.7, 50, and 33.3% for the high-risk group and 93.2, 91.5, and 84.4% for the low-risk group, respectively ( p < 0.05). Conclusion CT-based texture features from the liver or spleen may have the potential to stratify PH and predict patient survival.
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fmed-09-863596 March 28, 2022 Time: 15:48 # 1
ORIGINAL RESEARCH
published: 01 April 2022
doi: 10.3389/fmed.2022.863596
Edited by:
Xiaolong Qi,
First Hospital of Lanzhou University,
China
Reviewed by:
Jiacheng Liu,
Huazhong University of Science
and Technology, China
Christian Jansen,
University of Bonn, Germany
*Correspondence:
Fubi Hu
yingxianghu_cmc@163.com
Bin Song
songlab_radiology@163.com
Specialty section:
This article was submitted to
Gastroenterology,
a section of the journal
Frontiers in Medicine
Received: 27 January 2022
Accepted: 22 February 2022
Published: 01 April 2022
Citation:
Wan S, Wei Y, Zhang X, Yang C,
Hu F and Song B (2022) Computed
Tomography-Based Texture Features
for the Risk Stratification of Portal
Hypertension and Prediction
of Survival in Patients With Cirrhosis:
A Preliminary Study.
Front. Med. 9:863596.
doi: 10.3389/fmed.2022.863596
Computed Tomography-Based
Texture Features for the Risk
Stratification of Portal Hypertension
and Prediction of Survival in Patients
With Cirrhosis: A Preliminary Study
Shang Wan1, Yi Wei1, Xin Zhang2, Caiwei Yang1, Fubi Hu3*and Bin Song1,4*
1Department of Radiology, West China Hospital, Sichuan University, Chengdu, China, 2Pharmaceutical Diagnostics, GE
Healthcare, Beijing, China, 3Department of Radiology, First Affiliated Hospital of Chengdu Medical College, Chengdu, China,
4Department of Radiology, Sanya People’s Hospital, Sanya, China
Objective: Clinical evidence suggests that the risk stratification of portal hypertension
(PH) plays a vital role in disease progression and patient outcomes. However, the gold
standard for stratifying PH [portal vein pressure (PVP) measurement] is invasive and
therefore not suitable for routine clinical practice. This study is aimed to stratify PH
and predict patient outcomes using liver or spleen texture features based on computed
tomography (CT) images non-invasively.
Methods: A total of 114 patients with PH were included in this retrospective study and
divided into high-risk PH (PVP 20 mm Hg, n= 57) or low-risk PH (PVP <20 mm
Hg, n= 57), a progression-free survival (PFS) group (n= 14), or a non-PFS group
(n= 51) based on patients with rebleeding or death after the transjugular intrahepatic
portosystemic shunt (TIPS) procedure. All patients underwent contrast-enhanced CT,
and the laboratory data were recorded. Texture features of the liver or spleen were
obtained by a manual drawing of the region of interest (ROI) and were performed
in the portal venous phase. Logistic regression analysis was applied to select the
significant features related to high-risk PH, and PFS-related features were determined
by the Cox proportional hazards model and Kaplan-Meier analysis. Receiver operating
characteristic (ROC) curves were used to test the diagnostic capacity of each feature.
Results: Five texture features (one first-order feature from the liver and four wavelet
features from the spleen) and the international normalized ratio (INR) were identified as
statistically significant for stratifying PH (p<0.05). The best performance was achieved
by the spleen-derived feature of wavelet.LLH_ngtdm_Busyness, with an AUC of 0.72.
The only log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation feature from
the liver was associated with PFS with a C-index of 0.72 (95% CI 0.566–0.885), which
could stratify patients with PH into high- or low-risk groups. The 1-, 2-, and 3-year
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Wan et al. Texture Features of Portal Hypertension
survival probabilities were 66.7, 50, and 33.3% for the high-risk group and 93.2, 91.5,
and 84.4% for the low-risk group, respectively (p<0.05).
Conclusion: CT-based texture features from the liver or spleen may have the potential
to stratify PH and predict patient survival.
Keywords: risk stratification, survival, computed tomography, texture features, portal hypertension
INTRODUCTION
Portal hypertension (PH) is the initial and main consequence of
cirrhosis and is responsible for the majority of its complications
(1), which, by definition, is an increase in the pressure in the
portal vein and its territory (2). The direct measurement of portal
vein pressure (PVP) is the most accurate technique for reflexing
PH, but it is extremely invasive (2). Thus, the indirect and less
invasive measurement of the hepatic venous pressure gradient
(HVPG), widely accepted as the PVP equivalent, has been applied
in clinical practice (24).
In recent years, clinically significant portal hypertension
(CSPH) has been recognized in patients with liver cirrhosis
and is defined by an HVPG of at least 10 mm Hg, which is
associated with an increased risk of variceal bleeding, hepatic
encephalopathy (HE), post-surgical decompensation (5), and
hepatocellular carcinoma (HCC) (6). Patients at this stage may
have different prognoses based on the level of HVPG (7);
notably, an HVPG of at least 20 mm Hg is considered a
strong predictor of early rebleeding and death (8,9), which
would put patients at higher risk of decompensation and poor
clinical outcome. The findings of these studies revealed the
clinical significance of identifying severe PH. Previous studies
also indicated that recurrent variceal bleeding occurs in 60% of
patients after variceal rupture, if untreated, usually within 1–
2 years of index hemorrhage (1,10). Herein, the risk stratification
of PH and individualizing care for patients are warranted in
clinical decision-making.
Despite the crucial role of PVP or HVPG measurements
for the assessment and prognostic evaluation of PH (11), the
invasive nature and high-cost effectiveness of these techniques
have limited their clinical application as ideal surveillance tools
for monitoring disease progression (12). Currently, liver stiffness
(LS) by transient elastography (TE; Fibro-Scan) is recognized
as the backbone of the non-invasive diagnosis of PH (1,13);
however, controversy still exists regarding its application in
patients with obesity, non-alcoholic fatty liver disease, or severe
Abbreviations: PH, portal hypertension; PVP, portal vein pressure; HVPG,
hepatic venous pressure gradient; CSPH, clinically significant portal hypertension;
HE, hepatic encephalopathy; HCC, hepatocellular carcinoma; LS, liver stiffness;
TE, transient elastography; CT, computed tomography; TIPS, transjugular
intrahepatic portosystemic shunt; PFS, progression-free survival; FHVP, free
hepatic venous pressure; WHVP, wedged hepatic venous pressure; ROIs, regions
of interest; GLCM, gray-level cooccurrence matrix; GLSZM, gray-level size zone
matrix; GLRLM, gray-level run length matrix; NGTDM, neighboring gray-
tone difference matrix; GLDM, gray-level dependence matrix; ICC, intraclass
correlation coefficient; ROC, receiver operating characteristic; AUC, curve and the
area under the curve; INR, international normalized ratio; OR, odds ratio; SD,
standard deviation; IQR, interquartile range; HR, hazard ratio; PT, prothrombin
time; CTP, Child-Turcotte-Pugh.
ascites (1). In the past few years, imaging modalities have shown
potential in the assessment of PH as non-invasive and effective
procedures (12). The literature has demonstrated that computed
tomography (CT) has shown promising results for diagnosing
PH based on morphological measurements or computational
algorithms (9,14,15), however, non-invasive stratification of PH
on images has not been specified and remains challenging.
Most patients with PH asking for medical help present overt
clinical manifestations, such as varices or variceal hemorrhage
(12), which, by definition, with CSPH. Abraldes et al. indicated
that an HVPG 20 mm Hg is an independent factor that predicts
failure to control bleeding in patients with PH (16), and another
study demonstrated that HVPG is the only variable associated
with patient outcome and that an HVPG 20 mm Hg predicts
poor evolution when compared with HVPG <20 mm Hg,
specifically, longer intensive care unit stay, longer hospital stay,
and greater transfusion requirements. Thus, stratifying PH and
further predicting patients’ clinical outcomes with non-invasive
tests are urgently needed for patient management (1,7).
Texture analysis can non-invasively extract digital
information from images that naked eyes cannot with a
high throughput and can thus explore more characteristics
and provide more quantitative information from images (17).
This imaging-based technique has been applied to tumor
characterization, differential diagnosis, and prediction of
prognosis (1720). A landmark report indicated that the
radiomics signature extracted from CT could achieve significant
clinical benefits in the detection of CSPH (14). Another study
found that CT-based radiomics features may predict PVP (21);
however, the specified stratification of PH has not yet been
investigated. To the best of our knowledge, there is still a lack of
reports on CT-based texture features for the stratification of PH
and the prediction of survival conditions in patients with PH.
In this study, we aimed to assess whether CT-based liver or
spleen texture signatures could be used to predict high-risk PH
and patients’ long-term clinical outcomes.
MATERIALS AND METHODS
Patients
This retrospective study was approved by the West China
Hospital Ethics Committee and had a waiver of patients’
written informed consent. This study was conducted following
the Declaration of Helsinki. From January 2016 to October
2020, patients with PH admitted to our medical center
for a transjugular intrahepatic portosystemic shunt (TIPS)
procedure were eligible for study participation. The inclusion
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FIGURE 1 | The flowchart of patient enrollment.
criteria were as follows: (1) patients who were diagnosed
with liver cirrhosis; (2) patients with available intraoperative
direct measurements of PVP and abdominal contrast-enhanced
CT scans; and (3) adult patients (age 18 years). The
exclusion criteria were as follows: (1) patients who previously
underwent one of the following surgical procedures: TIPS,
splenectomy, partial splenic embolization, balloon-occluded
retrograde, transvenous obliteration, or liver transplantation;
(2) patients with portal thrombosis or histologically confirmed
HCC; and (3) patients with non-sinusoidal PH (e.g., hepatic
cavernoma, Budd-Chiari syndrome). All patients received the
TIPS procedure with direct PVP measurement during this
hospitalization and underwent contrast-enhanced CT within
4 weeks prior to the TIPS procedure. The patients’ laboratory
assessments were also recorded, and the patients were divided
into a high-risk PH group (PVP 20 mm Hg) and a low-risk
PH group (PVP <20 mm Hg) according to the PVP levels. The
flowchart of patient enrollment is shown in Figure 1.
Transjugular Intrahepatic Portosystemic
Shunt Procedure
The TIPS procedure was performed using a previously described
standard process (22). The jugular vein was accessed and a TIPS
set (Cook Medical Co., Bloomington, IN, United States) was
introduced into the right hepatic vein. The metal cannula was
bent by the operator according to the anatomical relationship
between the hepatic vein and the targeted puncture site along
the portal vein branch. A 3D roadmap was used for portal vein
puncture guidance, and access to the portal vein was confirmed
by injecting the contrast using a 5-ml syringe under fluoroscopy.
Subsequently, direct portography was performed, and PVP
measurements were made. The intrahepatic parenchymal tract
was then dilated with an 8-mm balloon (Powerflex; Cordis,
Roden, Netherlands) and an 8-mm stent graft (Fluency; C.R.
Bard, Murray Hill, NJ, United States) was placed. The direct
PVP was measured again, and the targeted threshold after stent
deployment was 12 mm Hg (23,24).
Computed Tomography Image
Acquisition
The investigated individuals underwent contrast-enhanced CT
imaging with one of the following systems: Sensation 64 CT
(Siemens), Sensation 16 CT (Siemens), or 64 LightSpeed VCT
(GE Healthcare). Triple-phase CT examinations were conducted,
i.e., non-enhanced, arterial, and portal vein phases. Abdominal
scouts were acquired from the dome of the diaphragm to the
iliac crests. The arterial phase of the same region was started at
approximately 20–30 s after contrast agent administration and
was followed by the portal phase (30–40 s). The reconstructions
were conducted on a GE Advantage Windows 3D workstation
(GE Healthcare, Waukesha, WI, United States), and the
reconstitution thickness was set at 1–2 mm. The detailed
scanning parameters are listed as follows: tube voltage, 120 or
100 kVp; tube current, 150–600 mA; slice thickness, 1.25 mm;
and pitch, 1.375. All patients received an intravenous, non-ionic
contrast agent (iodine concentration, 370 mg/ml; volume, 1.5–
2.0 ml/kg of body weight; contrast type, Omnipaque 300, GE
Healthcare, Ireland) at a rate of 3–5 ml/s. A volume of 20 ml saline
was injected after the injection of the contrast.
Follow-Up
Patients were consistently followed up after the TIPS procedure
by periodic re-examinations of CT scans in the outpatient clinics
at intervals of 3–6 months or by telephone verification. The
time of disease-specific progression (rebleeding) or death was
recorded, and patients were censored on October 30, 2021.
Patients for follow-up were divided into a progression-free
survival (PFS) group or a non-PFS group based on patients with
rebleeding or death after the TIPS procedure.
Texture Feature Extraction
Portal venous phase CT images were used for texture feature
extraction (14,25). Regions of interest (ROIs) were drawn
around the liver at the porta hepatis level and around the
spleen at the splenic hilum level using ITK-SNAP 3·6 (ITK-
SNAP 3·X TEAM) (14). Then, Artificial Intelligence Kit software
(A.K. software; GE Healthcare, Life Sciences, Beijing, China)
was used to extract feature parameters for each ROI, which was
based on the image biomarker standardization initiative (IBSI).
Figure 2 shows the delineation of the ROI of the liver and spleen.
Before feature extraction, image normalization was performed by
remapping the histogram to fit µ±3σ: (µ, average grayscale
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FIGURE 2 | The delineation of the region of interest of the liver (A) and spleen (B) on CT images.
within ROI; σ, grayscale SD) (26). Texture features were also
extracted from images conducted with the Laplacian of Gaussian
filter (Log) and wavelet filter. All scans were analyzed by two
senior residents independently (CWY, 5 years of experience in
abdominal imaging analysis, and YW, 8 years of experience in
abdominal imaging analysis) and were supervised by a senior
radiologist (FY, 13 years of experience) to handle the non-
consensus.
Texture Analysis and Statistical Analysis
A total of 1,037 radiomics features were calculated for original
images and filtered images from liver or spleen segmentation
that include first-order features of 18 intensity statistics and 14
3D shape features, 24 gray-level co-occurrence matrix (GLCM),
16 gray-level size zone matrix (GLSZM), 16 gray-level run
length matrix (GLRLM), 5 neighboring gray-tone difference
matrix (NGTDM), and 14 gray-level dependence matrix (GLDM)
features and features with two filters that include 744 wavelet
features and 186 LoG filtered features.
The intraclass correlation coefficient (ICC) was considered to
evaluate the interobserver agreement, and ICC values of >0.85
represent an almost perfect agreement between observers. The
Mann-Whitney U-test was used to compare continuous variables,
and the chi-square test was used to compare categorical variables.
Univariate and multivariate logistic regression analyses were
used to screen the independent risk factors for discriminating
the high-risk or low-risk PH group. Univariate analyses
were performed first, and only parameters found to have
statistical significance were used for further stepwise multivariate
logistic regression.
The diagnostic performance of each texture feature for
discriminating the high-risk or low-risk PH group was quantified
by the receiver operating characteristic (ROC) curve and the
area under the curve (AUC), and the accuracies, sensitivities,
and specificities were also calculated. Additionally, univariate
analyses with Cox proportional hazards regression identified the
predictors of disease progression of variceal bleeding recurrence
and death. The Cox proportional hazards model was used to
assess the PFS-associated texture features that predicted the
probabilities of 1-, 2-, and 3-year PFS in the followed up patients.
The risk probability of followed up patients was stratified
into high-risk and low-risk groups using the optimal cutoff
point determined by X-tile software (27). Survival curves were
generated with the Kaplan-Meier method and compared by a
2-sided log-rank test. The C-index was used to determine the
diagnostic capabilities of risk factors associated with PFS.
Categorical variables are reported as frequencies and
proportions. Continuous variables are reported as the means
(SD) and medians (interquartile ranges, IQR). Statistical analysis
was performed using R software (version 3.5.3). A values of pof
less than 0.05 was defined as significant in two-tailed analyses.
RESULTS
Patient Characteristics
The characteristics of the patients are summarized in Table 1. Out
of 114 patients included, there were 57 cases in the high-risk PH
group (PVP 20 mm Hg) and 57 cases in the low-risk PH group
(PVP <20 mm Hg). A total of 65 patients were finally followed
that include 14 cases in the PFS group and 51 cases in the non-
PFS group. In the low- and high-risk PH groups, the value of the
international normalized ratio (INR) was found to be statistically
significant between these two groups (p<0.05), except that the
remaining clinical parameters were not statistically significant
between the two groups (Table 1).
Clinical Variables and Texture Features
for Portal Hypertension Stratification
Texture features that had greater ICCs considering a threshold
of 0.85 were robust and adopted for later analysis. Of
all the clinical factors or CT-based texture features, six
significant features, i.e., 1 clinical variable [INR, odds ratio
(OR) 7.76, 95% confidence interval (CI) 1.14–52.88], and 5
texture features, were identified as independent predictors
by univariate analysis. Out of the 5 CT-based texture features,
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation
was identified from the liver (OR: 0.71, 95% CI: 0.54–0.94), and
wavelet.LLH_ngtdm_Busyness (OR 3.74, 95% CI 1.28–10.9),
wavelet.HLL_glrlm_RunLengthNonUniformity (OR 2.08,
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TABLE 1 | Demographics and clinical characteristics of the study population.
Variables NLow-risk PH High-risk PH Statistics P
Age, yearsa114 48.64 ±11.45 52.04 ±8.87 1.711 0.091
Genderb1.671 0.196
Male 78 29 (61.70%) 49 (73.13%)
Female 36 18 (38.30%) 18 (26.87%)
Etiologyb0.066 0.947
Post-hepatic cirrhosis 73 32 (68.09%) 41 (61.19%)
Alcoholic cirrhosis 14 2 (4.26%) 12 (17.91%)
Combined cirrhosis 12 4 (8.51%) 8 (11.94%)
Primary biliary cirrhosis 7 3 (6.38%) 4 (5.97%)
Others 8 6 (12.77%) 2 (2.99%)
ChildPugh classb0.253 0.8
Child–Pugh class A 35 15 (31.91%) 20 (29.85%)
Child–Pugh class B 61 25 (53.19%) 36 (53.73%)
Child–Pugh class C 18 7 (14.89%) 11 (16.42%)
PVP (mm Hg)c114 17.00 (15.20, 18.00) 22.00 (21.00, 27.00) 8.805 <0.001
EVB historyb1.131 0.288
Absent 17 9 (19.15%) 8 (11.94%)
Present 97 38 (80.85%) 59 (88.06%)
Ascitesb0.77 0.38
Absent 20 10 (21.28%) 10 (14.93%)
Present 94 37 (78.72%) 57 (85.07%)
Hypersplenismb0.337 0.561
Absent 88 35 (74.47%) 53 (79.10%)
Present 26 12 (25.53%) 14 (20.90%)
Hepatic encephalopathyb2.433 0.119
Absent 105 46 (97.87%) 59 (88.06%)
Present 9 1 (2.13%) 8 (11.94%)
Total bilirubinc114 21.50 (13.30, 30.72) 26.30 (16.34, 32.46) 1.361 0.173
Albumina114 34.40 ±6.50 33.09 ±6.12 1.096 0.275
Globulinc114 27.60 (23.68, 31.82) 26.90(22.62, 32.06) 0.653 0.514
ALTc114 21.00 (13.20, 31.80) 21.00 (14.00, 43.40) 0.458 0.647
ASTc114 32.00 (22.00, 43.80) 31.00 (21.20, 55.60) 0.622 0.534
INRc114 1.24 (1.15, 1.39) 1.35 (1.23, 1.48) 2.331 0.02
PLTc114 62.00 (51.20, 89.20) 63.00 (40.20, 100.40) 0.636 0.525
PH, portal hypertension; PVP, portal vein pressure; EVB, esophageal variceal bleeding; ALT, alanine aminotransferase; AST, aspartate aminotransferase; INR, international
normalized ratio; PLT, platelet count.
aData were compared by using Student’s t-test and are presented as the means ±deviation.
bData were compared using chi-square test and are presented as numbers (%).
cData were compared using the Mann-Whitney test and are presented as medians (interquartile range).
95% CI 1.1–3.95), avelet.HLH_glcm_MCC (OR 0.57, 95% CI
0.34–0.95), and wavelet.LLL_glrlm_RunLengthNonUniformity
(OR 2.1, 95% CI 1.09–4.05) were identified in the spleen
(Table 2). Clinical variable of INR and spleen-derived feature
of wavelet.LLH_ngtdm_Busyness showed the most significant
association with the high-risk PH group (OR 7.76, 95% CI
1.14–52.88, p= 0.036 vs. OR 3.74, 95% CI 1.28–10.9, p= 0.016).
Receiver operating characteristic analysis showed that the
above texture features had moderate capabilities to distinguish
between the high- and low-risk PH groups, of which the best
performance was achieved by the spleen-derived feature of
wavelet.LLH_ngtdm_Busyness, with an AUC of 0.72, an accuracy
of 0.746, a specificity of 0.681, and a sensitivity of 0.791 when
using a cutoff value of 0.517 (Table 3 and Figure 3). The
clinical feature of INR also showed a moderate performance for
stratifying PH, with an AUC of 0.629, an accuracy of 0.649, a
specificity of 0.468, and a sensitivity of 0.776 when using a cutoff
value of 0.528 (Figure 3).
Texture Features for Progression-Free
Survival
As of October 30, 2021, a total of 65 of 114 (57.0%) patients
had completed the PFS follow-up, the overall recurrence
rate of bleeding was 12.3% (8/65), and the overall death rate
was 9.2% (6/65). Table 4 shows the results of univariate Cox
proportional hazard regression analysis for PFS, of which only
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation
had a statistically significant difference for the PFS stratification
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TABLE 2 | Univariate analysis for stratifying portal hypertension.
Variables Prediction of high-risk PH
OR (95% CI) P
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation 0.71 (0.54–0.94) 0.017
wavelet.LLH_ngtdm_Busyness 3.74 (1.28–10.9) 0.016
wavelet.HLL_glrlm_RunLengthNonUniformity 2.08 (1.1–3.95) 0.025
wavelet.HLH_glcm_MCC 0.57 (0.34–0.95) 0.03
wavelet.LLL_glrlm_RunLengthNonUniformity 2.1 (1.09–4.05)
INR 7.76 (1.14–52.88) 0.036
PH, portal hypertension; OR, odds ratio; CI: confidence interval; INR, international normalized ratio.
TABLE 3 | The performance of texture features and INR for stratifying portal hypertension.
Variables AUC (95% CI) Accuracy Specificity Sensitivity Cutoff
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation 0.605 (0.49–0.710) 0.658 0.333 0.899 0.493
wavelet.LLH_ngtdm_Busyness 0.72 (0.622–0.817) 0.746 0.681 0.791 0.517
wavelet.HLL_glrlm_RunLengthNonUniformity 0.594 (0.509–0.717) 0.55 0.843 0.333 0.618
wavelet.HLH_glcm_MCC 0.593 (0.489–0.705) 0.65 0.275 0.928 0.5
wavelet.LLL_glrlm_RunLengthNonUniformity 0.604 (0.518–0.726) 0.592 0.588 0.594 0.553
INR 0.629 (0.525–0.733) 0.649 0.468 0.776 0.528
AUC, the area under the ROC curve; CI, confidence interval; INR, international normalized ratio.
FIGURE 3 | Receiver operating characteristic (ROC) analysis of selected texture .features and international normalized ratio (INR) for predicting high-risk portal
hypertension (PH). (A) The ROC curve calculated by texture features of log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation,
wavelet.LLH_ngtdm_Busyness, wavelet.HLL_glrlm_RunLengthNonUniformity, wavelet.HLH_glcm_MC, and wavelet.LLL_glrlm_RunLengthNonUniformity. (B) The
ROC curve of INR for predicting high-risk PH.
and could divide the followed up patients into high- or
low-risk groups (log-rank test, p<0.05; Figure 4). It was
lower in the high-risk group (medium 3.839; IQR 3.465–
4.027) than in the low-risk group (medium 5.868; IQR
5.166–6.942) [hazard ratio (HR) 0.529, 95% CI 0.322–
0.869, p= 0.012], and the remaining texture features
were not found to be associated with PFS. The feature of
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation
presented a moderate prognostic performance for predicting the
high-risk group with a C-index of 0.72 (95% CI 0.566–0.885)
when using a cutoff value of 4.15. We also evaluated clinical
characteristics for survival using univariate Cox proportional
hazard regression. We found that the variables of hypersplenism
and HE had statistical significance for survival analysis for PFS
(p<0.05; Supplementary Table 1), with HRs of 3.80 (95%
CI 1.31–10.99) and 4.27 (95% CI 1.33–13.64), respectively.
Supplementary Figures 1,2show the survival curves of
hypersplenism and HE, respectively. Clinical manifestations
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Wan et al. Texture Features of Portal Hypertension
of hypersplenism presented a C-index of 0.643 (95% CI
0.514–0.772), and HE presented a C-index of 0.614 (95%
CI 0.494–0.734).
The median survival time was 20.5 (IQR 10.25–39.75) months
for high-risk patients and 37 (IQR 32.5–41.5) months for low-risk
patients. The 1-, 2-, and 3-year survival probabilities were 66.7,
50, and 33.3% for the high-risk group and 93.2, 91.5, and 84.4%
for the low-risk group, respectively (log-rank test, p= 0.0014).
Representative cases were given to show the discriminative
performance of the features for stratifying PH and predicting
PFS (Figure 5).
DISCUSSION
Non-invasive stratification of PH and prediction of high-risk
PH in patients with cirrhosis have been highlighted in recent
years due to the lack of widespread application of invasive
PVP or HVPG measurements. In this study, we assessed the
texture features based on CT and clinical data non-invasively
for predicting high-risk PH patients. In addition, we evaluated
patient outcomes using the extracted features, with the aim of
aiding clinical decision-making.
Our results suggested that texture features from the liver
or spleen were significantly different between the high-risk
PH and low-risk PH groups in cirrhotic patients that include
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation
from the liver, wavelet.LLH_ngtdm_Busyness,
wavelet.HLL_glrlm_RunLengthNonUniformity,
wavelet.HLH_glcm_ MCC, and
wavelet.LLL_glrlm_RunLengthNonUniformity from
the spleen. Out of these features, the feature of
wavelet.LLH_ngtdm_Busyness from the spleen demonstrated
the best diagnostic performance, with an AUC of
0.72. Furthermore, we found that only the feature of
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation
was associated with patient outcomes, and it also showed a
moderate prognostic capability for discriminating the high-risk
group from the low-risk group, with a C-index of 0.726 based on
a cutoff value of 0.415.
A previous study demonstrated that the non-invasive
radiomics signature based on a machine-learning method,
which they termed rHVPG, could accurately facilitate the
diagnosis of PH in patients with cirrhosis (14). Their findings
underlined the significance of the detection of CSPH in
clinical treatment and inspired more investigation using
the advanced machine-learning algorithm for the evaluation
of PH. However, current guidelines indicate that different
levels of portal pressure are a strong predictor for patient
outcomes (1). As mentioned previously, an HVPG 20 mm
Hg predicts poor patient long-term survival and a higher
incidence of rebleeding (8,16); thus, stratification of PH
and identification of severe PH should be more emphasized.
Therefore, we conducted a further investigation based on a
previous report (14), and we evaluated the performance of the
texture signature from the machine-learning method for the
stratification of PH.
We found that out of 5 texture features associated with high-
risk PH, four were derived from the spleen, which might refer
to previous literature. They found that non-invasive spleen-
related parameters have the potential to predict the grade of
PH and the presence of varices (2830). For example, spleen
stiffness measurement by Fibro-Scan has been found to be
more closely related to PH than LS measurement (31,32). The
following reason might explain the spleen-related finding of
the present study. We all know that patients with severe PH
generally present spleen enlargement in the natural history of
disease progression. It is relevant to note that splenomegaly in
cirrhosis is characterized by enlargement and hyperactivation
of the splenic lymphoid tissue and increased angiogenesis and
fibrogenesis, in addition to passive congestion due to increased
portal pressure (33,34). Briefly, the pathogenetic changes leading
to spleen enlargement can be reflected in the spleen tissue
that includes the outer splenic morphological features and the
inner compartment; thus, the measurement of spleen stiffness
could reveal the physical property of spleen tissue consequent
to the hyperactivation condition of PH, by which satisfactory
results were obtained to be closely correlated with the degree of
PH, at least not inferior to that of LS, particularly, in a more
advanced stage of PH (32); therefore, spleen stiffness showed
a close relationship with PH. Likely, as an advanced imaging-
based technique, texture analysis can extract more valuable data
of the tissue component that traditional methods cannot detect,
partially, such as spleen stiffness measurement (17), it might be
able to reveal more inner pathologic characteristics of the spleen
and can thus have the ability to correlate with PH, especially in a
more advanced stage of PH as mentioned previously, for example,
the stage of the high-risk PH.
Tseng et al. indicated that a radiomics model based on the
spleen signature can yield superior performance for predicting
portal pressure when compared with the model of the liver
signature (AUC 0.832 vs. 0.789, respectively) (21), which
highlighted the spleen-derived signature on images. However,
they only evaluated the association between portal pressure and
the radiomics model and failed to further investigate the risk
stratification of PH. In this study, similar results were observed
in the diagnostic performance of spleen-derived texture features
(AUC, wavelet.LLH_ngtdm_Busyness of 0.72 from spleen vs.
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation
of 0.605 from liver). The spleen-derived texture outperformed
that of the liver and seemed more suitable to evaluate PH. We
speculated that the splenic-dominated result may be associated
with the complex vascular branch, particularly, the opening of
portosystemic shunts in the late stage of PH, which may have
significant implications on the liver tissue (35,36). As a result,
texture features from the liver might not be able to reflect the
complex hemodynamic changes of severe PH and may not
correlate well with severe PH. However, as a relatively isolated
organ, the spleen may not be influenced as much as the liver by
the collateral circulation in the late stage of PH (37,38); thus,
the spleen-derived features seem more stable and reliable. The
findings of this study suggest the potential of splenic texture
features for the prediction of high-risk PH; however, due to
the lack of relevant literature regarding the stratification of
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Wan et al. Texture Features of Portal Hypertension
TABLE 4 | Univariate Cox proportional hazard regression for survival.
Variables Survival analysis for PFS
HR (95% CI) P
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation 0.529 (0.322–0.869) 0.012
wavelet.LLH_ngtdm_Busyness 0.727 (0.266–1.991) 0.535
wavelet.HLL_glrlm_RunLengthNonUniformity 1.053 (0.671–1.653) 0.821
wavelet.HLH_glcm_MCC 1.352 (0.775–2.361) 0.288
wavelet.LLL_glrlm_RunLengthNonUniformity 1.03 (0.651–1.63) 0.9
HR, hazard ratio; CI, confidence interval; PFS, progression-free survival.
FIGURE 4 | Texture features for the evaluation of patient survival. As the picture depicts, only the liver-derived feature of log.sigma.3.0.mm.
3D_firstorder_RobustMeanAbsoluteDeviation could stratify patients with portal hypertension into high- or low-risk group (log-rank test, p<0.05).
PH and the relatively limited diagnostic capability, the results
of this study should be interpreted cautiously and need to be
corroborated in further prospective studies.
The clinical value of INR in the evaluation of liver
cirrhosis and the prognostic condition of patients still remains
controversial (1,39). Malinchoc et al. have previously reported
that INR for prothrombin time (PT) could be used as a predictor
for survival conditions in patients with liver cirrhosis undergoing
the TIPS procedure (40). Zhang et al. reported that the INR and
PT in the bleeding group were higher than those in the non-
bleeding group in patients with cirrhosis (41). They indicated
that the liver is an important site for coagulation factor synthesis,
and INR represents the deficiency in procoagulant proteins in
cirrhosis (42). The changes in PT and INR can accurately reflect
the degree of liver function impairment, and a longer PT or
INR usually suggests a worse liver function (41,43). However,
the association between INR and portal pressure has not been
described, and our results suggested a higher level of INR in the
high-risk PH group than in the low-risk PH group (medium 1.35
vs. 1.24), with an OR of 7.76. As we mentioned above, patients
with a higher INR are often accompanied by poor liver function;
theoretically, patients with poor liver function [Child-Turcotte-
Pugh (CTP) B or C] are usually decompensated or at an advanced
stage of PH (1), given that a higher INR value may be associated
with severe PH, such as high-risk PH. Regarding this, the findings
of this study with INR may provide valuable information for
clinicians for the stratification of PH.
To the best of our knowledge, this is the first
report that predicts patient outcomes consequent to
PH using texture signatures from the liver or spleen.
Based on the significant features for diagnosing high-
risk PH patients, we found that only the feature of
log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation
was associated with patient outcomes. A previous study applied
the radiomics technique in the assessment of portal pressure
along with the outcome (21); however, they only evaluated
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Wan et al. Texture Features of Portal Hypertension
FIGURE 5 | Representative cases for stratifying portal hypertension (PH) and patients’ survival condition. (A,B) Visualized spleen-derived texture feature of
wavelet.LLH_ngtdm_Busyness in high-risk PH patients [portal vein pressure (PVP) 20 mm Hg] and low-risk PH patients (PVP <20 mm Hg) respectively. (C,D)
Visualized liver-derived texture feature of log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation in the high-risk patients and low-risk patients, respectively.
patients’ outcome of variceal recurrence after initial endoscopic
therapy (suggesting a high portal pressure) (44), by which the
association between the portal pressure and the radiomics
was obtained. Unlike the previous study, in this study,
we evaluated patients’ survival condition more directly by
collecting the data of recurrence of bleeding or death, which
can be more clinically relevant and may provide more helpful
information for disease progression, and we found that the
feature can yield a moderate capability for discriminating
the high-risk group from the low-risk group when using a
cutoff value of 4.15 in this preliminary study, obtaining a
C-index of 0.726. We know that texture signatures can quantify
image features by extracting the distribution and relation
of pixel or voxel grayscale in images (17), and the 3D feature
(log.sigma.3.0.mm.3D_firstorder_RobustMeanAbsoluteDeviation)
indicates the rate of intensity change of the images. In this study,
we found a lower value of that in the high-risk group than that in
the low-risk group (medium 3.839 vs. 5.868), suggesting a higher
homogeneity of images in the high-risk group. Furthermore, the
only significant feature was derived from the liver; we assumed
that the difference between the high- and low-risk groups may
correlate with the inner alteration of liver tissue consequent
to cirrhosis. Additionally, we found that clinical variables of
hypersplenism and HE were also associated with PFS, and this
finding is consistent with the evolution of cirrhosis and PH (1).
Since PH is not an isolated complication, it should be considered
in the context of advances in the staging of cirrhosis and in the
context of other complications of cirrhosis (1,45). Whether these
variables can be used as independent prognostic factors for PFS
should be validated with more studies.
Several limitations of our study should be noted. First, due
to the retrospective nature of this study, a large number of
patients with portal thrombosis were excluded for its high
prevalence in patients with PH, which might lead to potential
selection bias and may impair the reproducibility of the results.
Second, the sample size of patients for follow-up and those
with disease progression was limited, and the interpretation of
the results should be carried out with caution and still needs
further validation with a large-scale sample size. Third, the
findings of this preliminary study are less than ideal, which is
the main limitation of this research. We are now collecting more
eligible patients, and we plan to conduct consecutive research
with a large sample size to improve the predictive performance.
We hope that the preliminary results could be suggestive
for researchers. Finally, this study is retrospective and was
carried out in a single-center institution, and more prospective
multicenter investigations are needed to better validate the results
in clinical practice.
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fmed-09-863596 March 28, 2022 Time: 15:48 # 10
Wan et al. Texture Features of Portal Hypertension
In conclusion, our study demonstrated that CT-based texture
signatures from the liver or spleen may have the potential to
stratify PH and predict patient survival. The results still need to
be corroborated by further multicenter prospective studies.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included
in the article/Supplementary Material, further inquiries can be
directed to the corresponding author/s.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the West China Hospital Ethics Committee. The
patients/participants provided their written informed consent to
participate in this study.
AUTHOR CONTRIBUTIONS
SW and YW contributed to the conception and design of the
study. SW and CY organized the database. XZ carried out data
statistics and analysis. SW wrote the manuscript. FH and BS
revised the manuscript. All authors have read and approved the
final manuscript.
FUNDING
This study was supported by the China Postdoctoral Science
Foundation (No. 2021M692289) and the Key R&D Projects of
Department of Science and Technology of Sichuan Province
(No. 2021YFS0144).
ACKNOWLEDGMENTS
We thank the patients who were included in this research project
and the staff who developed the ITK-SNAP and sklearn package.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmed.
2022.863596/full#supplementary-material
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Conflict of Interest: XZ was employed by the company Pharmaceutical
Diagnostics, GE Healthcare.
The remaining authors declare that the research was conducted in the absence of
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conflict of interest.
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Purpose To develop and validate a computational model for estimating hepatic venous pressure gradient (HVPG) based on CT angiographic images, termed virtual HVPG, to enable the noninvasive diagnosis of portal hypertension in patients with cirrhosis. Materials and Methods In this prospective multicenter diagnostic trial (ClinicalTrials.gov identifier: NCT02842697), 102 consecutive eligible participants (mean age, 47 years [range, 21-75 years]; 68 men with a mean age of 44 years [range, 21-73 years] and 34 women with a mean age of 52 years [range, 24-75 years]) were recruited from three high-volume liver centers between August 2016 and April 2017. All participants with cirrhosis of various causes underwent transjugular HVPG measurement, Doppler US, and CT angiography. Virtual HVPG was developed with a three-dimensional reconstructed model and computational fluid dynamics. Results In the training cohort (n = 29), the area under the receiver operating characteristic curve (AUC) of virtual HVPG in the prediction of clinically significant portal hypertension (CSPH) was 0.83 (95% confidence interval [CI]: 0.58, 1.00). The diagnostic performance was prospectively confirmed in the validation cohort (n = 73), with an AUC of 0.89 (95% CI: 0.81, 0.96). Inter- and intraobserver agreement was 0.88 and 0.96, respectively, suggesting the good reproducibility of virtual HVPG measurements. There was good correlation between virtual HVPG and invasive HVPG (R = 0.61, P < .001), with a satisfactory performance to rule out (7.3 mm Hg) and rule in (13.0 mm Hg) CSPH. Conclusion The accuracy of a computational model of virtual hepatic venous pressure gradient (HVPG) shows significant correlation with invasive HVPG. The virtual HVPG also showed a good performance in the noninvasive diagnosis of clinically significant portal hypertension in cirrhosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Malayeri in this issue.
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Purpose Portal venous pressure (PVP) measurement is of clinical significance, especially in patients with portal hypertension. However, the invasive nature and associated complications limits its application. The aim of the study is to propose a noninvasive predictive model of PVP values based on CT-extracted radiomic features. Methods Radiomics PVP (rPVP) models based on liver, spleen and combined features were established on an experimental cohort of 169 subjects. Radiomics features were extracted from each ROI and reduced via the LASSO regression to achieve an optimal predictive formula. A validation cohort of 62 patients treated for gastroesophageal varices (GOV) was used to confirm the utility of rPVP in predicting variceal recurrence. The association between rPVP and response to treatment was observed. Results Three separate predictive formula for PVP were derived from radiomics features. rPVP was significantly correlated to patient response to endoscopic treatment for GOV. Among which, the model containing both liver and spleen features has the highest predictability of variceal recurrence, with an optimal cut-off value at 29.102 mmHg (AUC 0.866). A Kaplan Meier analysis further confirmed the difference between patients with varying rPVP values. Conclusion PVP values can be accurately predicted by a non-invasive, CT derived radiomics model. rPVP serves as a non-invasive and precise reference for predicting treatment outcome for GOV secondary to portal hypertension.
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Clinically significant portal hypertension is associated with an increased risk of developing gastro-oesophageal varices and hepatic decompensation. Hepatic venous pressure gradient measurement and oesophagogastroduodenoscopy are the gold-standard methods for assessing clinically significant portal hypertension (hepatic venous pressure gradient ≥10 mm Hg) and gastro-oesophageal varices, respectively. However, invasiveness, cost, and feasibility limit their widespread use, especially if repeated and serial evaluations are required to assess the efficacy of pharmacotherapy. Although new techniques for non-invasive portal pressure measurement have been pursued for many decades, only recently have new tools been assessed and validated for larger clinical application. This Review focuses on the recent advances in non-invasive approaches for the diagnosis and serial monitoring of portal hypertension and varices for clinical practice.