Accurate computed tomography-based portal pressure assessment in patients with hepatocellular carcinoma

Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection

BACKGROUND/AIMS

The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB.

METHODS

This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses.

RESULTS

During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD.

CONCLUSION

Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.

CT Rule-in and Rule-out Criteria for Clinically Significant Portal Hypertension in Chronic Liver Disease

Background The value of CT in assessment of clinically significant portal hypertension (CSPH) has not been well determined. Purpose To evaluate the performance of CT features that have been associated with portal hypertension for diagnosing CSPH in patients with chronic liver disease (CLD). Materials and Methods This retrospective study included patients with CLD who underwent contrast-enhanced CT and subsequent hepatic venous pressure gradient (HVPG) measurement within 3 months at two tertiary institutions from January 2001 to December 2019. Two readers independently evaluated the presence of gastroesophageal varix, spontaneous portosystemic shunt (SPSS), and ascites on CT images. Splenomegaly at CT was determined using three methods, as follows: personalized or fixed volume criteria, based on spleen volume as measured by a deep learning algorithm, or manually measured spleen diameter. The diagnostic performance of these findings alone or in combination for detecting CSPH (HVPG ≥10 mm Hg) was evaluated. Results A total of 235 patients (mean age, 53.2 years ± 13.0 [SD]; 155 male patients), including 110 (46.8%) with CSPH, were included. Detection of CSPH according to the presence of both splenomegaly and at least one other CT feature (ie, gastroesophageal varix, SPSS, and ascites) achieved specificities of 94.4%-97.6%, whereas detection of CSPH according to the presence of any feature (ie, splenomegaly, gastroesophageal varix, SPSS, or ascites) achieved sensitivities of 94.5%-98.2%. When employing the former as rule-in criteria with the absence of splenomegaly, gastroesophageal varix, SPSS, and ascites as rule-out criteria for CSPH, 171-185 (range, 72.8%-78.7%) of 235 patients were correctly classified as either having CSPH or not, seven to 13 (range, 3%-5.5%) of 235 patients were incorrectly classified, and 42-54 (range, 17.9%-23%) of 235 patients were unclassified. Conclusion The presence or absence of splenomegaly, gastroesophageal varix, SPSS, and/or ascites on CT images may be useful for ruling in and ruling out CSPH in patients with CLD. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Fraum in this issue.

Portal hypertension in patients with hepatocellular carcinoma and immunotherapy: prognostic relevance of CT-morphologic estimates

BACKGROUND

Clinically significant portal hypertension (CSPH) has been identified as an important prognostic factor in patients with hepatocellular carcinoma (HCC) undergoing curative treatment. This study aimed to assess PH estimates as prognostic factors in patients with HCC treated with immunotherapy.

METHODS

All patients with HCC treated with an immunotherapeutic agent in first or subsequent lines at our tertiary care center between 2016 and 2021 were included (n = 50). CSPH was diagnosed using the established PH score for non-invasive PH estimation in pre-treatment CT data (cut-off ≥ 4). Influence of PH on overall survival (OS) and progression-free survival (PFS) was assessed in uni- and multivariable analyses.

RESULTS

Based on the PH score, 26 patients (52.0%) were considered to have CSPH. After treatment initiation, patients with CSPH had a significantly impaired median OS (4.1 vs 33.3 months, p < 0.001) and a significantly impaired median PFS (2.7 vs 5.3 months, p = 0.02). In multivariable Cox regression, CSPH remained significantly associated with survival (HR 2.9, p = 0.015) when adjusted for established risk factors.

CONCLUSIONS

Non-invasive assessment of CSPH using routine CT data yielded an independent prognostic factor in patients with HCC and immunotherapy. Therefore, it might function as an additional imaging biomarker to detect high-risk patients with poor survival and possibly for treatment decision making.

Computed tomography imaging features to evaluate the severity of portal hypertension and predict the rebleeding risk after endoscopic treatment in cirrhotic patients with variceal hemorrhage

PURPOSE

To investigate the association of computed tomography (CT) imaging features and severity of portal hypertension (PH) and develop a nomogram to predict high-risk PH in cirrhotic patients with gastroesophageal variceal hemorrhage (GVH).

METHODS

The study retrospectively enrolled 158 cirrhotic patients with a history of endoscopic treatment for GVH. Hepatic vein pressure gradient (HVPG) was measured and the patients were classified into high-risk (HVPG > 16 mmHg) or low-risk (HVPG ≤ 16 mmHg) PH group. Pre-treatment CT features, including cavernous transformation of portal vein (CTPV), hilar periportal space (a distance between right portal vein and posterior edge of segment IV of the liver), and depth of right posterior hepatic notch sign (a sharp indentation in the right medial posterior liver surface), were evaluated. Risk factors associated with high-risk PH were analyzed, and a nomogram based on the imaging features was developed.

RESULTS

High-risk PH group showed a higher rebleeding rate after treatment than that of the low-risk (P = 0.029). Multivariate analysis indicated that larger hilar periportal space (P < 0.001), less frequencies of CTPV (P = 0.044) and deeper right posterior hepatic notch (P < 0.001) were independent risk factors associated with high-risk PH. A nomogram based on the three CT imaging features was established to predict high-risk PH with an excellent discrimination (c-statistic 0.854).

CONCLUSION

The nomogram based on CT features of hilar periportal space, depth of right posterior hepatic notch and CTPV can help to distinguish cirrhotic patients with high-risk PH, who are more vulnerable of variceal rebleeding after endoscopic treatment.

Numerical prediction of portal hypertension by a hydrodynamic blood flow model combing with the fractal theory

Portal hypertension (PH) can cause a series of complications, therefore, early prediction of PH is important. Traditional diagnostic methods are harmful to the human body, while other non-invasive methods are inaccurate and lack physical meaning. Combining various fractal theories and flow laws, we establish a complete portal system blood flow model from the Computed Tomography (CT) and angiography images. The portal vein pressure (PP) is obtained by the flow rate data from the Doppler ultrasound and the pressure-velocity relationship is established by the model. Three normal participants and 12 patients with portal hypertension were divided into three groups. For the three normal participants (Group A), their mean PP calculated by the model is 1752 Pa, falling into the normal range of PP. The mean PP of three patients with portal vein thrombosis (Group B) is 2357 Pa; and for the 9 patients with cirrhosis (Group C), their mean PP is 2915 Pa. These results validate the classification performance of the model. Moreover, the blood flow model can give early warning parameters of the corresponding portal vein trunk and portal vein microtubules for thrombosis and liver cirrhosis. This model presents the complete process of blood flow from sinusoids to the portal vein, adapts to the diagnosis of portal hypertension of thrombosis and liver cirrhosis, and provides a new method for noninvasive portal vein pressure detection from the perspective of biomechanics.

CT-derived liver and spleen volume accurately diagnose clinically significant portal hypertension in patients with hepatocellular carcinoma

Background & Aims

Clinically significant portal hypertension (CSPH) is a landmark in the natural history of cirrhosis, influencing clinical decisions in patients with hepatocellular carcinoma (HCC). Previous small series suggested that splanchnic volume measurements may predict portal hypertension. We aimed to evaluate whether volumetry obtained by standard multidetector computerised tomography (MDCT) can predict CSPH in patients with HCC.

Methods

We included 175 patients with HCC, referred for hepatic venous pressure gradient (HVPG) evaluation, in whom contemporary MDCT was available. Liver volume, spleen volume (SV) and liver segmental volume ratio (LSVR: volume of the segments I-III/volume of the segments IV-VIII) were calculated semi-automatically from MDCT. Other non-invasive tests (NITs) were also employed.

Results

Volume parameters could be measured in almost 100% of cases with an excellent inter-observer agreement (intraclass correlation coefficient >0.950). SV and LSVR were independently associated with CSPH (HVPG ≥10 mmHg) and did not interact with aetiology. The volume Index (VI), calculated as the product of SV and LSVR, predicted CSPH (AUC 0.83; 95% CI 0.77-0.89). Similar results were observed in an external cohort (n = 23) (AUC 0.87; 95% CI 0.69-1.00). Setting a sensitivity and specificity of 98%, VI could have avoided 35.9% of HVPG measurements. The accuracy of VI was similar to that of other NITs. VI also accurately predicted HVPG greater than 12, 14, 16 and 18 mmHg (AUC 0.81 [95% CI 0.74-0.88], 0.84 [95% CI 0.77-0.91], 0.85 [95% CI 0.77-0.92] and 0.87 [95% CI 0.79-0.94], respectively).

Conclusions

Quantification of liver and spleen volumes by MDCT is a simple, accurate and reliable method of CSPH estimation in patients with compensated cirrhosis and HCC.

Impact and implications

An increase in portal pressure strongly impacts outcomes after surgery in patients with early hepatocellular carcinoma (HCC). Direct measurement through hepatic vein catheterization remains the reference standard for portal pressure assessment, but its invasiveness limits its application. Therefore, we evaluated the ability of CT scan-based liver and spleen volume measurements to predict portal hypertension in patients with HCC. Our results indicate that the newly described index, based on quantification of liver and spleen volume, accurately predicts portal hypertension. These results suggest that a single imaging test may be used to diagnose and stage HCC, while providing an accurate estimation of portal hypertension, thus helping to stratify surgical risks.

Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI

OBJECTIVE

This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD).

MATERIALS AND METHODS

We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters.

RESULTS

Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11-117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ΔLS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ΔLS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023).

CONCLUSION

MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.

Gadoxetic acid-enhanced MRI-derived functional liver imaging score (FLIS) and spleen diameter predict outcomes in ACLD

BACKGROUND & AIMS

Functional liver imaging score (FLIS) - derived from gadoxetic acid-enhanced MRI - correlates with liver function and independently predicts liver-related mortality in patients with chronic liver disease (CLD), while splenic craniocaudal diameter (SCCD) is a marker of portal hypertension. The aim of this study was to investigate the accuracy of a combination of FLIS and SCCD for predicting hepatic decompensation, acute-on-chronic liver failure (ACLF), and mortality in patients with advanced CLD (ACLD).

METHODS

We included 397 patients with CLD who underwent gadoxetic acid-enhanced liver MRI. The FLIS was calculated by summing the points (0-2) of 3 hepatobiliary-phase features: hepatic enhancement, biliary excretion, and portal vein signal intensity. Patients were stratified into 3 groups according to liver fibrosis severity and presence/history of hepatic decompensation: non-ACLD, compensated ACLD (cACLD), and decompensated ACLD (dACLD).

RESULTS

SCCD showed excellent intra- and inter-reader agreement. Importantly, SCCD was an independent risk factor for hepatic decompensation in patients with cACLD (per cm; adjusted hazard ratio [aHR] 1.13; 95% CI 1.04-1.23; p = 0.004). Patients with cACLD and a FLIS of 0-3 points and/or a SCCD of >13 cm were at increased risk of hepatic decompensation (aHR 3.07; 95% CI 1.43-6.59; p = 0.004). In patients with dACLD, a FLIS of 0-3 was independently associated with an increased risk of ACLF (aHR 2.81; 95% CI 1.16-6.84; p = 0.02), even after adjusting for other prognostic factors. Finally, a FLIS and SCCD-based algorithm was independently predictive of transplant-free mortality and stratified the probability of transplant-free survival (TFS) in ACLD (p <0.001): FLIS 4-6 and SCCD ≤13 cm (5-year TFS of 84%) vs. FLIS 4-6 and SCCD >13 cm (5-year TFS of 70%) vs. FLIS 0-3 (5-year TFS of 24%).

CONCLUSION

The FLIS and SCCD are simple imaging markers that provide complementary information for risk stratification in patients with compensated and decompensated ACLD.

LAY SUMMARY

Magnetic resonance imaging (MRI) can be used to assess the state of the liver. Previously the functional liver imaging score, which is based on MRI criteria, was developed as a measure of liver function and to predict the risk of liver-related complications or death. By combining this score with a measurement of spleen diameter, also using MRI, we generated an algorithm that could predict the risk of adverse liver-related outcomes in patients with advanced chronic liver disease.

Liver stiffness-based model predicts hepatic venous pressure gradient in patients with liver disease

BACKGROUND

The aim was to develop a model to predict clinically significant portal hypertension, hepatic venous pressure gradient (HVPG) ≥10 mmHg using pre-operative noninvasive makers.

METHODS

Patients who have been programmed for liver resection/transplantation were enrolled prospectively. Preoperative liver stiffness measurement (LSM), liver function test (LFT), and intraoperative HVPG were assessed. A probability score model to predict HVPG≥10 mmHg called HVPG₁₀ score was developed and validated.

RESULTS

A total of 161 patients [66% men, median age of 63 years] were recruited for the study. Median LSM, and HVPG were 9.5 kPa, and 5 mmHg respectively. HVPG₁₀ score was developed using independent predictors of HVPG≥10 mmHg in the training set were LSM, total bilirubin, alkaline phosphatase, and international normalized ratio. Area under receiver operating curve of HVPG₁₀ score in the training and validation sets were 0.91 and 0.93 respectively with a cutoff of 15. In the overall cohort, HVPG₁₀ score≥15 had 83% accuracy, 90% sensitivity, 81% specificity and 96% negative predictive value in predicting HVPG≥10 mmHg.

CONCLUSION

HVPG₁₀ score is an easy-to-use noninvasive continuous scale tool to rule out clinically significant portal hypertension in >95% patients with chronic liver disease.

Bibliometric-analysis visualization and review of non-invasive methods for monitoring and managing the portal hypertension

Background

Portal hypertension monitoring is important throughout the natural course of cirrhosis. Hepatic venous pressure gradient (HVPG), regarded as the golden standard, is limited by invasiveness and technical difficulties. Portal hypertension is increasingly being assessed non-invasively, and hematological indices, imaging data, and statistical or computational models are studied to surrogate HVPG. This paper discusses the existing non-invasive methods based on measurement principles and reviews the methodological developments in the last 20 years.

Methods

First, we used VOSviewer to learn the architecture of this field. The publications about the non-invasive assessment of portal hypertension were retrieved from the Web of Science Core Collection (WoSCC). VOSviewer 1.6.17.0 was used to analyze and visualize these publications, including the annual trend, the study hotspots, the significant articles, authors, journals, and organizations in this field. Next, according to the cluster analysis result of the keywords, we further retrieved and classified the related studies to discuss.

Results

A total of 1,088 articles or review articles about our topic were retrieved from WoSCC. From 2000 to 2022, the number of publications is generally growing. “World Journal of Gastroenterology” published the most articles (n = 43), while “Journal of Hepatology” had the highest citations. “Liver fibrosis” published in 2005 was the most influential manuscript. Among the 20,558 cited references of 1,088 retrieved manuscripts, the most cited was a study on liver stiffness measurement from 2007. The highest-yielding country was the United States, followed by China and Italy. “Berzigotti, Annalisa” was the most prolific author and had the most cooperation partners. Four study directions emerged from the keyword clustering: (1) the evaluation based on fibrosis; (2) the evaluation based on hemodynamic factors; (3) the evaluation through elastography; and (4) the evaluation of variceal bleeding.

Conclusion

The non-invasive assessment of portal hypertension is mainly based on two principles: fibrosis and hemodynamics. Liver fibrosis is the major initiator of cirrhotic PH, while hemodynamic factors reflect secondary alteration of splanchnic blood flow. Blood tests, US (including DUS and CEUS), CT, and magnetic resonance imaging (MRI) support the non-invasive assessment of PH by providing both hemodynamic and fibrotic information. Elastography, mainly USE, is the most important method of PH monitoring.

Liver and spleen volumes are associated with prognosis of compensated and decompensated cirrhosis and parallel its natural history

Objective

Cirrhosis is characterized by the complex interplay among biological, histological and haemodynamic events. Liver and spleen remodelling occur throughout its natural history, but the prognostic role of these volumetric changes is unclear. We evaluated the relationship between volumetric changes assessed by multidetector computerised tomography (MDCT) and landmark features of cirrhosis.

Methods

We included consecutive cirrhotic patients who underwent liver transplantation (LT) or hepatocellular carcinoma (HCC) resection in whom dynamic MDCT was available. Different volumetric indices were calculated. Fibrosis was evaluated by the collagen proportional area and Laennec sub‐stages. Correlation and logistic regression analysis were performed to explore associations of volumetric indexes and fibrosis with key prognostic features across the clinical stages of cirrhosis.

Results

185 patients were included (146 LT; 39 HCC); the predominant aetiology was viral hepatitis (51.35%); 65.9% had decompensated disease and 85.08% clinically significant portal hypertension (CSPH). The standardised liver volume and liver‐spleen volume ratio negatively correlated with Model for End‐stage Liver Disease (MELD), albumin and hepatic venous pressure gradient (HVPG) and were significantly lower in decompensated patients. The liver segmental volume ratio (segments I–III/segments IV–VIII) best captured the characteristic features of the compensated phase, showing a positive correlation with HVPG and a good discrimination between patients with and without CSPH and varices. Volumetric changes and fibrosis severity were independently associated with key prognostic events, with no association between these two parameters.

Conclusions

Liver and spleen volumetric indices evolve differently along the natural history of cirrhosis and are associated with key prognostic factors in each phase, regardless of fibrosis severity and portal hypertension.

Noncirrhotic Portal Hypertension after Trastuzumab Emtansine in HER2-positive Breast Cancer as Determined by Deep Learning-measured Spleen Volume at CT

Background Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate approved for use in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Case reports have suggested an association between T-DM1 and portal hypertension. Purpose To evaluate the association of T-DM1 therapy with spleen volume changes and portal hypertension on CT scans and clinical findings compared with lapatinib and capecitabine therapy. Materials and Methods Patients with HER2-positive breast cancer who were administered at least two cycles of T-DM1 or lapatinib and capecitabine (controls) in a tertiary institution from 2001 to 2020 and who underwent CT before initial treatment and at least once during treatment were retrospectively enrolled. Spleen volume changes and the signs of portal hypertension (gastroesophageal varix [GEV], spontaneous portosystemic shunt [SPSS], and ascites) were evaluated at contrast-enhanced CT. Patients were followed until treatment ended or for 2 years after the start of treatment. Spleen volume changes were measured with a deep learning algorithm and evaluated by using a linear mixed model. The incidences of splenomegaly and portal hypertension were compared between the T-DM1 and control groups by using a χ² test or Fisher exact test. Results The T-DM1 group included 111 patients (mean age, 54 years ± 11 [SD]; 111 women) and the control group included 122 patients (mean age, 50 years ± 9; 121 women). Spleen volume progressively increased with T-DM1 therapy but was constant in the control group (104% ± 5 vs -1% ± 6 at the 33rd treatment cycle, respectively; P < .001). Incidences of splenomegaly (46% [51 of 111] vs 3% [four of 122] of patients; P < .001), GEV (11% [12 of 111] vs 1% [one of 122] of patients; P < .001), and SPSS (27% [30 of 111] vs 1% [one of 122] of patients; P < .001) were higher in the T-DM1 group than in the control group. Conclusion Trastuzumab emtansine therapy was associated with noncirrhotic portal hypertension at CT, with higher incidences of splenomegaly, gastroesophageal varix, and spontaneous portosystemic shunt than those with lapatinib and capecitabine therapy. © RSNA, 2022 Online supplemental material is available for this article.

Baseline Splenic Volume Outweighs Immuno-Modulated Size Changes with Regard to Survival Outcome in Patients with Hepatocellular Carcinoma under Immunotherapy

Simple Summary

Splenic volume (SV) has been identified as a highly predictive parameter for prognosis in patients with hepatocellular carcinoma (HCC). Moreover, an association between immunotherapy and an increase in SV has been described for various types of cancer. In our cohort of patients with HCC under immunotherapy, SV was a highly predictive factor for overall survival at baseline and initial follow-up. Although a large proportion of patients (76%) showed an SV increase after the initiation of immunotherapy, this additional immuno-modulated SV change was negligible compared to long-standing changes in the splanchnic circulation in our patient cohort.

Abstract

Background: An association between immunotherapy and an increase in splenic volume (SV) has been described for various types of cancer. SV is also highly predictive of overall survival (OS) in patients with hepatocellular carcinoma (HCC). We evaluated SV and its changes with regard to their prognostic influence in patients with HCC undergoing immunotherapy. Methods: All patients with HCC who received immunotherapy in first or subsequent lines at our tertiary care center between 2016 and 2021 were screened for eligibility. SV was assessed at baseline and follow-up using an AI-based tool for spleen segmentation. Patients were dichotomized into high and low SV based on the median value. Results: Fifty patients were included in the analysis. The median SV prior to treatment was 532 mL. The median OS of patients with high and low SV was 5.1 months and 18.1 months, respectively (p = 0.01). An increase in SV between treatment initiation and the first follow-up was observed in 28/37 (75.7%) patients with follow-up imaging available. This increase in itself was not prognostic for median OS (7.0 vs. 8.5 months, p = 0.73). However, patients with high absolute SV at the first follow-up continued to have impaired survival (4.0 months vs. 30.7 months, p = 0.004). Conclusion: High SV prior to and during treatment was a significant prognostic factor for impaired outcome. Although a large proportion of patients showed an SV increase after the initiation of immunotherapy, this additional immuno-modulated SV change was negligible compared to long-standing changes in the splanchnic circulation in patients with HCC.

The Value of Liver and Spleen Stiffness for Evaluation of Portal Hypertension in Compensated Cirrhosis

Patients with compensated advanced chronic liver disease who develop clinically significant portal hypertension (CSPH) are at high risk for hepatic decompensation and mortality if left untreated. Liver biopsy and hepatic venous pressure gradient (HVPG) measurements are the current gold standard procedures for determining fibrosis severity and diagnosing CSPH, respectively; however, both are invasive, limiting their use in clinical practice and larger trials of novel agents. As such, there is an unmet clinical need for reliable, validated, noninvasive measures to detect CSPH and to further assess portal hypertension (PH) severity. Alterations in the biomechanical properties of the liver or spleen in patients with cirrhosis can be quantified by tissue elastography, which examines the elastic behavior of tissue after a force has been applied. A variety of methods are available, including magnetic resonance elastography, shear-wave elastography, and the most thoroughly investigated measure, vibration-controlled transient elastography. Liver stiffness (LS) and spleen stiffness (SS) measurements offer valuable alternatives to detect and monitor CSPH. Both LS and SS correlate well with HVPG, with thresholds of LS >20-25 kPa and SS >40-45 kPa indicating a high likelihood of CSPH. Because SS is a direct and dynamic surrogate of portal pressure, it has the potential to monitor PH severity and assess PH improvement as a surrogate marker for clinical outcomes. Importantly, SS seems to be superior to LS for monitoring treatment response in clinical trials focusing on reducing PH.

Liver surgery for colorectal metastasis: New paths and new goals with the help of artificial intelligence

Colorectal cancer is one of the most common neoplasia with an high risk to metastatic spread. Improving medical and surgical treatment is moving along with improving the precision of diagnosis and patient's assessment, the latter two aided more and more with the use of artificial intelligence (AI). The management of colorectal liver metastasis is multidisciplinary, and surgery is the main option. After the diagnosis, a surgical assessment of the patient is fundamental. Reaching a R0 resection with a proper remnant liver volume can be done using new techniques involving also artificial intelligence. Considering the recent application of artificial intelligence as a valid substitute for liver biopsy in chronic liver diseases, several authors tried to apply similar techniques to pre-operative imaging of liver metastasis. Radiomics showed good results in identifying structural changes in a unhealthy liver and in evaluating the prognosis after a liver resection. Recently deep learning has been successfully applied in estimating the remnant liver volume before surgery. Moreover AI techniques can help surgeons to perform an early diagnosis of neoplastic relapse or a better differentiation between a colorectal metastasis and a benign lesion. AI could be applied also in the histopathological diagnostic tool. Although AI implementation is still partially automatized, it appears faster and more precise than the usual diagnostic tools and, in the short future, could become the new gold standard in liver surgery.

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

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 &lt; 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 &lt; 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 &lt; 0.05).

Conclusion

CT-based texture features from the liver or spleen may have the potential to stratify PH and predict patient survival.

An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension

The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (aHVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (≥10, ≥12, ≥16, and ≥20 mm Hg) and compare the model with imaging- and serum-based tools. The final aHVPG model achieves AUCs over 0.80 and outperforms other non-invasive tools for assessing HVPG. The model shows performance improvement in identifying the severity of PHT, which may help non-invasive HVPG primary prophylaxis when transjugular HVPG measurements are not available.

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aHVPG is an automated HVPG quantitative estimation model based on CT

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aHVPG has the potential to assess HVPG and outperforms other non-invasive tools

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Non-invasive tools may help PHT monitoring when invasive HVPG is not available

Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen

The aim of this study was to develop a deep learning-based algorithm for fully automated spleen segmentation using CT images and to evaluate the performance in conditions directly or indirectly affecting the spleen (e.g., splenomegaly, ascites). For this, a 3D U-Net was trained on an in-house dataset (n = 61) including diseases with and without splenic involvement (in-house U-Net), and an open-source dataset from the Medical Segmentation Decathlon (open dataset, n = 61) without splenic abnormalities (open U-Net). Both datasets were split into a training (n = 32.52%), a validation (n = 9.15%) and a testing dataset (n = 20.33%). The segmentation performances of the two models were measured using four established metrics, including the Dice Similarity Coefficient (DSC). On the open test dataset, the in-house and open U-Net achieved a mean DSC of 0.906 and 0.897 respectively (p = 0.526). On the in-house test dataset, the in-house U-Net achieved a mean DSC of 0.941, whereas the open U-Net obtained a mean DSC of 0.648 (p < 0.001), showing very poor segmentation results in patients with abnormalities in or surrounding the spleen. Thus, for reliable, fully automated spleen segmentation in clinical routine, the training dataset of a deep learning-based algorithm should include conditions that directly or indirectly affect the spleen.

Longitudinal Analysis of the Effect of Repeated Transarterial Chemoembolization for Liver Cancer on Portal Venous Pressure

Objectives

Investigate long-term effects of repeated transarterial chemoembolization (TACE) on portal venous pressure (PVP) using non-invasive surrogate markers of portal hypertension.

Methods

Retrospective, Institutional Review Board-approved study. 99 patients [hepatocellular carcinoma (HCC) group (n=57); liver metastasis group (n=42)] who underwent 279TACEs and had longitudinal pre-/post-therapy contrast-enhanced-MRI (n=388) and complete blood work were included. Outcomes of interest were platelet count (PC), spleen volume, ascites and portosystemic collaterals. Variables included TACE type/number, tumor type, microcatheter location, Child-Pugh, baseline tumor burden (tumor number/total/largest size), vessel invasion, alpha-fetoprotein, Eastern Cooperative Oncology Group (ECOG) performance status, and Model for End-Stage Liver Disease (MELD) score. Generalized Estimating Equations assessed the associations between TACE and outcomes. Power analysis determined the sample size was sufficient.

Results

No significant change in PC over time was observed in either groups, regardless of liver function (P>0.05). Baseline spleen volume was 226 cm³ for metastatic group, and was larger by 204 cm³ for HCC group (P<0.001). Spleen volume increased by 20 cm³ (95%CI: 8-32; P=0.001) for both groups after 1^stTACE and by 16cm³/TACE (P=0.099) over the full follow-up (up to 9TACEs). Spleen volume also tended to increase by 23cm³ (95%CI: -1–48; P=0.064) with higher tumor burden. Odds of developing moderate/severe ascites for metastatic patients was decreased by 0.5 (95%CI: 0.3–0.9; P=0.014), regardless of the Child-Pugh, and increased by 1.5 (95%CI: 1.2–1.9; P<0.001) among HCC patients with unstable Child-Pugh, whereas no change was noted with stable Child-Pugh. HCC patients with unstable Child-Pugh demonstrated a significant increase in portosystemic collaterals number over time (P=0.008). PVP-related complications such as variceal bleeding post-TACE were low (0.4%).

Conclusion

Repeated TACEs did seem to have an impact on PVP. However, the increase in PVP had marginal effects with low portal hypertension-related complications.

Artificial intelligence in the diagnosis of cirrhosis and portal hypertension

Clinically significant portal hypertension is associated with an increased risk of developing gastroesophageal varices and hepatic decompensation. Hepatic venous pressure gradient measurement and esophagogastroduodenoscopy are the gold-standard methods for assessing clinically significant portal hypertension and gastroesophageal 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. Artificial intelligence describes a range of techniques that allow machines to perform tasks typically thought to require human reasoning and problem-solving skills. Artificial intelligence has made great strides in the field of medicine, and is also involved in portal hypertension diagnosis. Artificial intelligence tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time. This review focuses on the recent advances in artificial intelligence for the noninvasive diagnosis of portal hypertension and gastroesophageal varices and monitoring of risk assessment of its complications in clinical practice.

Validation of a standardized CT protocol for the evaluation of varices and porto-systemic shunts in cirrhotic patients

PURPOSE

The aim of the present study was to propose and validate a standardized CT protocol for evaluating all the types of portosystemic collaterals (P-SC), including gastroesophageal varices and spontaneous portosystemic shunts (SPSS), and to evaluate the prognostic role of portal hypertension CT features for the prediction of the hepatic decompensation risk in cirrhotic patients.

METHODS

A retrospective cohort study of 184 advanced chronic liver disease who underwent CT scan between January 2014 and December 2017. Patients with an interval > 6 months between the imaging, elastometric, endoscopic and biochemical evaluation were excluded, as well as patients with previous transjugular intrahepatic portosystemic shunt (TIPS), liver transplantation (LT) or terminal medical conditions. Data on liver disease history, co-morbidities, endoscopic and radiologic findings were collected. The incidence of hepatic decompensation and other events, such as portal vein thrombosis, HCC, TIPS placement, LT, death, and its cause, were also recorded. The procedure was performed at baseline and after the administration of contrast agent using a multiphasic technique and bolus tracking. Two senior radiologists working in different centres and a non-expert radiologist reviewed all CT examinations, to evaluate both intra-observer and inter-observer variability of the CT protocol and to obtain an external validation. The radiological variables were evaluated using both univariate and adjusted multivariate competing risk regression models.

RESULTS

Both intra-observer and inter-observer agreement were excellent in detection and measurement of almost all types of P-SC. The presence of SPSS, a spleen diameter > 16 cm, a portal vein diameter > 17 mm and the presence of ascites resulted independent predictors of decompensation-free survival for cirrhotic patients and were incorporated in an easy-to-use score (AUROC = 0.799, p-value = 0.732) which can the risk of decompensation at 5 years, ranking it as low (11.3%), moderate (35.6%) or high (70.8%).

CONCLUSIONS

The CT protocol commonly performed during the HCC surveillance program for cirrhotic patients is valid for detecting all types of P-SC. The radiological score identified to predict the decompensation-free survival for cirrhotic patients could be an easy-to-use clinical tool.

Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis

Objective

Although the liver-to-spleen volume ratio (LSVR) based on CT reflects portal hypertension, its prognostic role in cirrhotic patients has not been proven. We evaluated the utility of LSVR, automatically measured from CT images using a deep learning algorithm, as a predictor of hepatic decompensation and transplantation-free survival in patients with hepatitis B viral (HBV)-compensated cirrhosis.

Materials and Methods

A deep learning algorithm was used to measure the LSVR in a cohort of 1027 consecutive patients (mean age, 50.5 years; 675 male and 352 female) with HBV-compensated cirrhosis who underwent liver CT (2007–2010). Associations of LSVR with hepatic decompensation and transplantation-free survival were evaluated using multivariable Cox proportional hazards and competing risk analyses, accounting for either the Child-Pugh score (CPS) or Model for End Stage Liver Disease (MELD) score and other variables. The risk of the liver-related events was estimated using Kaplan-Meier analysis and the Aalen-Johansen estimator.

Results

After adjustment for either CPS or MELD and other variables, LSVR was identified as a significant independent predictor of hepatic decompensation (hazard ratio for LSVR increase by 1, 0.71 and 0.68 for CPS and MELD models, respectively; p < 0.001) and transplantation-free survival (hazard ratio for LSVR increase by 1, 0.8 and 0.77, respectively; p < 0.001). Patients with an LSVR of < 2.9 (n = 381) had significantly higher 3-year risks of hepatic decompensation (16.7% vs. 2.5%, p < 0.001) and liver-related death or transplantation (10.0% vs. 1.1%, p < 0.001) than those with an LSVR ≥ 2.9 (n = 646). When patients were stratified according to CPS (Child-Pugh A vs. B–C) and MELD (< 10 vs. ≥ 10), an LSVR of < 2.9 was still associated with a higher risk of liver-related events than an LSVR of ≥ 2.9 for all Child-Pugh (p ≤ 0.045) and MELD (p ≤ 0.009) stratifications.

Conclusion

The LSVR measured on CT can predict hepatic decompensation and transplantation-free survival in patients with HBV-compensated cirrhosis.

Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis

Background Reference intervals guiding volumetric assessment of the liver and spleen have yet to be established. Purpose To establish population-based and personalized reference intervals for liver volume, spleen volume, and liver-to-spleen volume ratio (LSVR). Materials and Methods This retrospective study consecutively included healthy adult liver donors from 2001 to 2013 (reference group) and from 2014 to 2016 (healthy validation group) and patients with viral hepatitis from 2007 to 2017. Liver volume, spleen volume, and LSVR were measured with CT by using a deep learning algorithm. In the reference group, the reference intervals for the volume indexes were determined by using the population-based (ranges encompassing the central 95% of donors) and personalized (quantile regression modeling of the 2.5th and 97.5th percentiles as a function of age, sex, height, and weight) approaches. The validity of the reference intervals was evaluated in the healthy validation group and the viral hepatitis group. Results The reference and healthy validation groups had 2989 donors (mean age ± standard deviation, 30 years ± 9; 1828 men) and 472 donors (mean age, 30 years ± 9; 334 men), respectively. The viral hepatitis group had 158 patients (mean age, 48 years ± 12; 95 men). The population-based reference intervals were 824.5-1700.0 cm³ for liver volume, 81.1-322.0 cm³ for spleen volume, and 3.96-13.78 for LSVR. Formulae and a web calculator (https://i-pacs.com/calculators) were presented to calculate the personalized reference intervals. In the healthy validation group, both the population-based and personalized reference intervals were used to classify the volume indexes of 94%-96% of the donors as falling within the reference interval. In the viral hepatitis group, when compared with the population-based reference intervals, the personalized reference intervals helped identify more patients with volume indexes outside the reference interval (liver volume, 21.5% [34 of 158] vs 13.3% [21 of 158], P = .01; spleen volume, 29.1% [46 of 158] vs 22.2% [35 of 158], P = .01; LSVR, 35.4% [56 of 158] vs 26.6% [42 of 158], P < .001). Conclusion Reference intervals derived from a deep learning approach in healthy adults may enable evidence-based assessments of liver and spleen volume in clinical practice. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Ringl in this issue.

Nomogram for hepatic venous pressure gradient in patients with cirrhosis

OBJECTIVE

The hepatic venous pressure gradient (HVPG) plays an important role in the treatment and prognosis of patients with cirrhosis. Our study aimed to develop and validate a nomogram for an HVPG >12 mmHg.

METHODS

A retrospective study was performed to create a nomogram for an HVPG >12 mmHg in a training cohort that was validated in another cohort. The discriminatory ability and calibration of the nomogram were tested using the C-statistic, area under the receiver operating characteristic curve (AUROC) and calibration plots.

RESULTS

The nomogram was based on portosystemic shunts identified on computed tomography images, the etiology of cirrhosis and the Child-Pugh grade. These parameters were significantly associated with an HVPG >12 mmHg (P < 0.05 for both the training and validation cohorts). In the training cohort, the model showed good discrimination (C-statistic, AUROC, and R² of 0.71, 0.71 and 0.13, respectively) and good calibration. The total cutoff value was 112 and the sensitivity and specificity were 57.1% and 77.6%, respectively. The application of the nomogram in the validation cohort still yielded good discrimination (C-statistic 0.75 [95% confidence interval 0.61-0.89], AUROC 0.75, and R² 0.16) and good calibration.

CONCLUSIONS

This nomogram is a convenient tool for predicting an HVPG >12 mmHg in patients with cirrhosis and can help clinicians quickly identify patients with decompensated cirrhosis.

Predicting the risk of post-hepatectomy portal hypertension using a digital twin: A clinical proof of concept

BACKGROUND & AIMS

Despite improvements in medical and surgical techniques, post-hepatectomy liver failure (PHLF) remains the leading cause of postoperative death. High postoperative portal vein pressure (P_PV) and portocaval gradient (PCG), which cannot be predicted by current tools, are the most important determinants of PHLF. Therefore, we aimed to evaluate a digital twin to predict the risk of postoperative portal hypertension (PHT).

METHODS

We prospectively included 47 patients undergoing major hepatectomy. A mathematical (0D) model of the entire blood circulation was assessed and automatically calibrated from patient characteristics. Hepatic flows were obtained from preoperative flow MRI (n = 9), intraoperative flowmetry (n = 16), or estimated from cardiac output (n = 47). Resection was then simulated in these 3 groups and the computed P_PV and PCG were compared to intraoperative data.

RESULTS

Simulated post-hepatectomy pressures did not differ between the 3 groups, comparing well with collected data (no significant differences). In the entire cohort, the correlation between measured and simulated P_PV values was good (r = 0.66, no adjustment to intraoperative events) or excellent (r = 0.75) after adjustment, as well as for PCG (respectively r = 0.59 and r = 0.80). The difference between simulated and measured post-hepatectomy PCG was ≤3 mmHg in 96% of cases. Four patients suffered from lethal PHLF for whom the model satisfactorily predicted their postoperative pressures.

CONCLUSIONS

We demonstrated that a 0D model could correctly anticipate postoperative PHT, even using estimated hepatic flow rates as input data. If this major conceptual step is confirmed, this algorithm could change our practice toward more tailor-made procedures, while ensuring satisfactory outcomes.

LAY SUMMARY

Post-hepatectomy portal hypertension is a major cause of liver failure and death, but no tool is available to accurately anticipate this potentially lethal complication for a given patient. Herein, we propose using a mathematical model to predict the portocaval gradient at the end of liver resection. We tested this model on a cohort of 47 patients undergoing major hepatectomy and demonstrated that it could modify current surgical decision-making algorithms.

Radiomics and deep learning in liver diseases

Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis

BACKGROUND & AIMS

Noninvasive and accurate methods are needed to identify patients with clinically significant portal hypertension (CSPH). We investigated the ability of deep convolutional neural network (CNN) analysis of computed tomography (CT) or magnetic resonance (MR) to identify patients with CSPH.

METHODS

We collected liver and spleen images from patients who underwent contrast-enhanced CT or MR analysis within 14 days of transjugular catheterization for hepatic venous pressure gradient measurement. The CT cohort comprised participants with cirrhosis in the CHESS1701 study, performed at 4 university hospitals in China from August 2016 through September 2017. The MR cohort comprised participants with cirrhosis in the CHESS1802 study, performed at 8 university hospitals in China and 1 in Turkey from December 2018 through April 2019. Patients with CSPH were identified as those with a hepatic venous pressure gradient of 10 mm Hg or higher. In total, we analyzed 10,014 liver images and 899 spleen images collected from 679 participants who underwent CT analysis, and 45,554 liver and spleen images from 271 participants who underwent MR analysis. For each cohort, participants were shuffled and then sampled randomly and equiprobably for 6 times into training, validation, and test data sets (ratio, 3:1:1). Therefore, a total of 6 deep CNN models for each cohort were developed for identification of CSPH.

RESULTS

The CT-based CNN analysis identified patients with CSPH with an area under the receiver operating characteristic curve (AUC) value of 0.998 in the training set (95% CI, 0.996-1.000), an AUC of 0.912 in the validation set (95% CI, 0.854-0.971), and an AUC of 0.933 (95% CI, 0.883-0.984) in the test data sets. The MR-based CNN analysis identified patients with CSPH with an AUC of 1.000 in the training set (95% CI, 0.999-1.000), an AUC of 0.924 in the validation set (95% CI, 0.833-1.000), and an AUC of 0.940 in the test data set (95% CI, 0.880-0.999). When the model development procedures were repeated 6 times, AUC values for all CNN analyses were 0.888 or greater, with no significant differences between rounds (P > .05).

CONCLUSIONS

We developed a deep CNN to analyze CT or MR images of liver and spleen from patients with cirrhosis that identifies patients with CSPH with an AUC value of 0.9. This provides a noninvasive and rapid method for detection of CSPH (ClincialTrials.gov numbers: NCT03138915 and NCT03766880).

Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images

Objective

Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions.

Materials and Methods

A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation.

Results

In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). For the measurement of volumetric indices, the Bland-Altman 95% LOA was −0.17 ± 3.07% for liver volume and −0.56 ± 3.78% for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institution was comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation.

Conclusion

The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.

Noninvasive imaging assessment of portal hypertension

Portal hypertension (PH) is a spectrum of complications of chronic liver disease (CLD) and cirrhosis, with manifestations including ascites, gastroesophageal varices, splenomegaly, hypersplenism, hepatic hydrothorax, hepatorenal syndrome, hepatopulmonary syndrome and portopulmonary hypertension. PH can vary in severity and is diagnosed via invasive hepatic venous pressure gradient measurement (HVPG), which is considered the reference standard. Accurate diagnosis of PH and assessment of severity are highly relevant as patients with clinically significant portal hypertension (CSPH) are at higher risk for developing acute variceal bleeding and mortality. In this review, we discuss current and upcoming noninvasive imaging methods for diagnosis and assessment of severity of PH.

Primary prevention of bleeding from esophageal varices in patients with liver cirrhosis: An update and review of the literature

All patients with liver cirrhosis and portal hypertension should be stratified by risk groups to individualize different therapeutic strategies to increase the effectiveness of treatment. In this regard, the development of primary prophylaxis of variceal bleeding and its management according to the severity of portal hypertension may be promising. This paper is to describe the modern principles of primary prophylaxis of esophageal variceal bleeding in patients with liver cirrhosis. The PubMed and EMbase databases, Web of Science, Google Scholar, and the Cochrane Database of Systematic Reviews were used to search for relevant publications from 1999 to 2019. The results suggested that depending on the severity of portal hypertension, patients with cirrhosis should be divided into those who need preprimary prophylaxis, which aims to prevent the formation of esophageal varices, and those who require measures that aim to prevent esophageal variceal bleeding. In subclinical portal hypertension, therapy should be etiological and pathogenetic. Cirrhosis with clinically significant portal hypertension should receive nonselective β-blockers if they have small esophageal varices and risk factors for variceal bleeding. Nonselective β-blockers are the first-line drugs for the primary prevention of bleeding from medium to large-sized esophageal varices. Endoscopic band ligation is indicated for the patients who are intolerant to nonselective β-blockers or in the case of contraindications to pharmacological therapy. In summary, the stratification of cirrhotic patients by the severity of portal hypertension and an individual approach to the choice of treatment may increase the effectiveness of therapy as well as improve survival rate of these patients.

An index based on deep learning-measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis

OBJECTIVES

Deep learning enables an automated liver and spleen volume measurements on CT. The purpose of this study was to develop an index combining liver and spleen volumes and clinical factors for detecting high-risk varices in B-viral compensated cirrhosis.

METHODS

This retrospective study included 419 patients with B-viral compensated cirrhosis who underwent endoscopy and CT from 2007 to 2008 (derivation cohort, n = 239) and from 2009 to 2010 (validation cohort, n = 180). The liver and spleen volumes were measured on CT images using a deep learning algorithm. Multivariable logistic regression analysis of the derivation cohort developed an index to detect endoscopically confirmed high-risk varix. The cumulative 5-year risk of varix bleeding was evaluated with patients stratified by their index values.

RESULTS

The index of spleen volume-to-platelet ratio was devised from the derivation cohort. In the validation cohort, the cutoff index value for balanced sensitivity and specificity (> 3.78) resulted in the sensitivity of 69.4% and the specificity of 78.5% for detecting high-risk varix, and the cutoff index value for high sensitivity (> 1.63) detected all high-risk varices. The index stratified all patients into the low (index value ≤ 1.63; n = 118), intermediate (n = 162), and high (index value > 3.78; n = 139) risk groups with cumulative 5-year incidences of varix bleeding of 0%, 1.0%, and 12.0%, respectively (p < .001).

CONCLUSION

The spleen volume-to-platelet ratio obtained using deep learning-based CT analysis is useful to detect high-risk varices and to assess the risk of varix bleeding.

KEY POINTS

• The criterion of spleen volume to platelet > 1.63 detected all high-risk varices in the validation cohort, while the absence of visible varix did not exclude all high-risk varices. • Visual varix grade ≥ 2 detected high-risk varix with a high specificity (96.5-100%). • Combining spleen volume-to-platelet ratio ≤ 1.63 and visual varix grade of 0 identified low-risk patients who had no high-risk varix and varix bleeding on 5-year follow-up.

Association between spleen volume and the post-hepatectomy liver failure and overall survival of patients with hepatocellular carcinoma after resection

OBJECTIVES

Post-hepatectomy liver failure (PHLF) can occur as a major complication after hepatic resection (HR) in patients with hepatocellular carcinoma (HCC) and negatively affects the prognosis. We aimed to retrospectively assess whether the spleen volume (SV) measured from preoperative CT images would be associated with the development of PHLF and overall survival (OS) after HR in patients with HCC.

METHODS

We enrolled 317 consecutive patients with very early/early stage HCC who underwent a preoperative CT and HR between January 2010 and December 2016. The SV was obtained from preoperative CT images using semi-automated volumetric software and was divided by body surface area to yield SV_BSA. Receiver operating characteristic (ROC) curves and logistic regression analyses were performed to identify factors affecting the development of PHLF. The Cox proportional hazard model was used to identify prognostic factors for OS.

RESULTS

PHLF was observed in 72 patients (22.7% [72/317]). SV_BSA was associated with the development of PHLF (odds ratio, 2.321; 95% CI, 1.347-4.001; p = 0.002) with the area under the ROC curve of 0.663 using the cutoff value of 107.5 cm³ (p < 0.001). SV_BSA was also an influencing factor for OS (hazard ratio, 3.935; 95% CI 1.520-10.184; p = 0.005), with the optimal cutoff of 146 cm³. The 5-year OS rate was higher in 245 patients with a SV_BSA ≤ 146 cm³ than in 72 patients with a SV_BSA > 146 cm³ (95.0% vs. 78.7%, p < 0.001).

CONCLUSIONS

In patients with HCC, a larger SV_BSA was associated with a higher rate of PHLF and worse OS after HR. The SV_BSA may be useful in selecting good surgical candidates.

KEY POINTS

• A significantly higher spleen volume divided by body surface area was observed in patients who experienced post-hepatectomy liver failure than in patients who did not (148 cm³ vs. 112 cm³, p < 0.001). • The area under the receiver operating characteristic curve of spleen volume divided by body surface area to predict the development of post-hepatectomy liver failure was 0.663 (p < 0.001). • Spleen volume divided by body surface area was a significant influencing factor for overall survival (hazard ratio, 3.935; 95% CI, 1.520-10.184; p < 0.001), with the optimal cutoff of 146 cm³.

Assessment of a biofluid mechanics-based model for calculating portal pressure in canines

BACKGROUND

Portal hypertension is a severe complication caused by various chronic liver diseases. The standard methods for detecting portal hypertension (hepatic venous pressure gradient and free portal pressure) are available in only a few hospitals due to their technical difficulty and invasiveness; thus, non-invasive measuring methods are needed. This study aimed to establish and assess a novel model to calculate free portal pressure based on biofluid mechanics.

RESULT

Comparison of each dog's virtual and actual free portal pressure showed that a biofluid mechanics-based model could accurately predict free portal pressure (mean difference: -0.220, 95% CI: - 0.738 to 0.298; upper limit of agreement: 2.24, 95% CI: 1.34 to 3.14; lower limit of agreement: -2.68, 95% CI: - 3.58 to - 1.78; intraclass correlation coefficient: 0.98, 95% CI: 0.96 to 0.99; concordance correlation coefficient: 0.97, 95% CI: 0.93 to 0.99) and had a high AUC (0.984, 95% CI: 0.834 to 1.000), sensitivity (92.3, 95% CI: 64.0 to 99.8), specificity (91.7, 95% CI: 61.5 to 99.8), positive likelihood ratio (11.1, 95% CI: 1.7 to 72.8), and low negative likelihood ratio (0.08, 95% CI: 0.01 to 0.6) for detecting portal hypertension.

CONCLUSIONS

Our study suggests that the biofluid mechanics-based model was able to accurately predict free portal pressure and detect portal hypertension in canines. With further research and validation, this model might be applicable for calculating human portal pressure, detecting portal hypertensive patients, and evaluating disease progression and treatment efficacy.

Accurate ultrasonography-based portal pressure assessment in patients with hepatocellular carcinoma

BACKGROUND

Portal pressure is of great significance in the treatment of hepatocellular carcinoma (HCC), but direct measurement is complicated and costly; thus, non-invasive measurement methods are urgently needed.

AIM

To investigate whether ultrasonography (US)-based portal pressure assessment could replace invasive transjugular measurement.

METHODS

A cohort of 102 patients with HCC was selected (mean age: 54 ± 13 years, male/female: 65/37). Pre-operative US parameters were assessed by two independent investigators, and multivariate logistic analysis and linear regression analysis were conducted to develop a predictive formula for the portal pressure gradient (PPG). The estimated PPG predictors were compared with the transjugular PPG measurements. Validation was conducted on another cohort of 20 non-surgical patients.

RESULTS

The mean PPG was 17.32 ± 1.97 mmHg. Univariate analysis identified the association of the following four parameters with PPG: Spleen volume, portal vein diameter, portal vein velocity (PVV), and portal blood flow (PBF). Multiple linear regression analysis was performed, and the predictive formula using the PVV and PBF was as follows: PPG score = 19.336 - 0.312 × PVV (cm/s) + 0.001 × PBF (mL/min). The PPG score was confirmed to have good accuracy with an area under the curve (AUC) of 0.75 (0.68-0.81) in training patients. The formula was also accurate in the validation patients with an AUC of 0.820 (0.53-0.83).

CONCLUSION

The formula based on ultrasonographic Doppler flow parameters shows a significant correlation with invasive PPG and, if further confirmed by prospective validation, may replace the invasive transjugular assessment.

Relevance of liver surface nodularity for preoperative risk assessment in patients with resectable hepatocellular carcinoma

BACKGROUND

Quantification of liver surface nodularity (LSN) on routine preoperative CT images allows detection of cirrhosis and clinically significant portal hypertension. This study aimed to assess the relevance of LSN in preoperative assessment of operative risks for patients with resectable hepatocellular carcinoma (HCC).

METHODS

All patients undergoing hepatectomy for HCC between 2012 and 2017 were analysed retrospectively. LSN was assessed at the liver-fat interface on the left liver lobe on preoperative CT images. The feasibility of LSN quantification was assessed. The association between LSN and outcomes (severe complications and posthepatectomy liver failure (PHLF)) was evaluated by multivariable analysis and after propensity score matching.

RESULTS

Among 210 patients, LSN measurement was successful in 187 (89·0 per cent). Among these, the median LSN score was 2·42 (i.q.r. 2·21-2·66) and 52·9 per cent had severe fibrosis, including 33·7 per cent with cirrhosis. LSN score increased with hepatic venous pressure gradient (P = 0·048), severity of steatosis (P = 0·011) and fibrosis grade (P = 0·001). LSN score was independently associated with severe complications (odds ratio (OR) 5·25; P = 0·006) and PHLF (OR 6·78; P = 0·003). After matching with respect to model for end-stage liver disease, aspartate aminotransferase-to-platelet ratio index and fibrosis-4 score, patients with a LSN score of 2·63 or higher retained an increased risk of PHLF (OR 5·81; P = 0·018). In the subgroup of patients without severe fibrosis, LSN was accurate in predicting severe complications (P = 0·005). Patients with (P = 0·039) or without (P = 0·018) severe fibrosis with increased LSN score had a higher comprehensive complication index score. Among patients with cirrhosis who had clinically significant portal hypertension, a LSN value below 2·63 ruled out the risk of PHLF.

CONCLUSION

LSN measurement represents a practical tool that may allow improvement in the preoperative evaluation and management of patients with HCC.

Radiomics and Deep Learning: Hepatic Applications

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

Spleen volumetry and liver transient elastography: Predictors of persistent posthepatectomy decompensation in patients with hepatocellular carcinoma

BACKGROUND

Posthepatectomy decompensation remains a frequent and poor outcome after hepatectomy, but its prediction is still inaccurate. Liver stiffness measurement can predict posthepatectomy decompensation, but there is a so-called "gray zone" that requires another predictor. Because splenomegaly is an objective sign of portal hypertension, we hypothesized that spleen volumetry could improve the identification of patients at risk.

METHODS

Patients with hepatocellular carcinoma who underwent hepatectomy in our tertiary center between August 2014 and December 2017 were reviewed. The primary endpoint was to determine if the spleen volumetry and liver stiffness measurement were independent predictors of posthepatectomy decompensation, and secondarily, to determine if they were synergistic through a theoretic predictive model.

RESULTS

One hundred and seven patients were included. The median follow-up time was 3 months (3-5). Postoperative 90-day mortality was 4.7%. By multivariate analysis, liver stiffness measurement and spleen volumetry predicted posthepatectomy decompensation. The liver stiffness measurement had a cutoff point of 11.6 kPa (area under receiver operating curve = 0.71 confidence interval 95% 0.71-0.88, sensitivity: 89%, specificity: 47%). The spleen volumetry cutoff point was 381.1 cm³ (area under receiver operating curve = 0.78, 95% confidence interval 0.77-0.93, sensitivity: 55%, specificity: 91%). The spleen volumetry improved prediction of posthepatectomy decompensation, because use of the spleen volumetry increased sensitivity (from 62% to 97%) and the negative predictive value (from 96% to 100%) along with a negligible decrease in specificity (from 96.7 to 93.4) and positive predictive value (from 64% to 59%) (P = .003).

CONCLUSION

Spleen volumetry (>380 cm³) and liver stiffness measurement (>12 kPa) are non-invasive, independent, and synergistic tools that appear to be able to predict posthepatectomy decompensation. The importance of this finding is that these measurements may help to anticipate posthepatectomy decompensation and may possibly be used to direct alternative treatments to resection.

Application of CT-based radiomics in predicting portal pressure and patient outcome in portal hypertension

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.

Assessment of liver fibrosis severity using computed tomography-based liver and spleen volumetric indices in patients with chronic liver disease

OBJECTIVES

To evaluate whether the liver and spleen volumetric indices, measured on portal venous phase CT images, could be used to assess liver fibrosis severity in chronic liver disease.

METHODS

From 2007 to 2017, 558 patients (mean age 48.7 ± 13.1 years; 284 men and 274 women) with chronic liver disease (n = 513) or healthy liver (n = 45) were retrospectively enrolled. The liver volume (sVol_L) and spleen volume (sVol_S), normalized to body surface area and liver-to-spleen volume ratio (Vol_L/Vol_S), were measured on CT images using a deep learning algorithm. The correlation between the volumetric indices and the pathologic liver fibrosis stages combined with the presence of decompensation (F0, F1, F2, F3, F4C [compensated cirrhosis], and F4D [decompensated cirrhosis]) were assessed using Spearman's correlation coefficient. The performance of the volumetric indices in the diagnosis of advanced fibrosis, cirrhosis, and decompensated cirrhosis were evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The sVol_S (ρ = 0.47-0.73; p < .001) and Vol_L/Vol_S (ρ = -0.77-- 0.48; p < .001) showed significant correlation with liver fibrosis stage in all etiological subgroups (i.e., viral hepatitis, alcoholic and non-alcoholic fatty liver, and autoimmune diseases), while the significant correlation of sVol_L was noted only in the viral hepatitis subgroup (ρ = - 0.55; p < .001). To diagnose advanced fibrosis, cirrhosis, and decompensated cirrhosis, the Vol_L/Vol_S (AUC 0.82-0.88) and sVol_S (AUC 0.82-0.87) significantly outperformed the sVol_L (AUC 0.63-0.72; p < .001).

CONCLUSION

The Vol_L/Vol_S and sVol_S may be used for assessing liver fibrosis severity in chronic liver disease.

KEY POINTS

• Volumetric indices of liver and spleen measured on computed tomography images may allow liver fibrosis severity to be assessed in patients with chronic liver disease.

Diagnostic accuracy of non-invasive methods detecting clinically significant portal hypertension in liver cirrhosis: a systematic review and meta-analysis

INTRODUCTION

We attempted to investigate non-invasive techniques and their diagnostic performances for evaluating clinically significant portal hypertension.

EVIDENCE ACQUISITION

The systematic search was performed on PubMed, Embase, Scopus, and Web of Science TM core index databases before 13 December 2018 restricted to English language and human studies.

EVIDENCE SYNTHESIS

Thirty-two studies were included, with total populations of 3,987. The overall pooled analysis was performed by bivariate random effect model, which revealed significantly higher sensitivity and specificity of 77.1% (95% confidence interval, 76.8-78.5%) and 80.1% (95% confidence interval, 78.2-81.9%), respectively; positive likelihood ratio (3.67), negative likelihood ratio (0.26); and diagnostic odd ratio (16.24). Additionally, the area under curve exhibited significant diagnostic accuracy of 0.871. However, notable heterogeneity existed in between studies (I2=87.1%), therefore, further subgroup analysis was performed. It demonstrated ultrasonography, elastography, biomarker, and computed tomography scan had a significant overall summary sensitivity (specificity) of 89.6% (78.9%), 81.7% (83.2%), 72.2% (76.8%), and 77.2% (81.2%), respectively. Moreover, the areas under curve values were significantly higher in elastography (0.906), followed by computed tomography scan (0.847), biomarker (0.825), and ultrasonography (0.803).

CONCLUSIONS

In future, non-invasive techniques could be the future choice of investigations for screening and diagnosis of clinically significant portal hypertension in cirrhosis. However, standardization of diagnostic indices and their cut-off values in each non-invasive method needs to be addressed.

Virtual Hepatic Venous Pressure Gradient with CT Angiography (CHESS 1601): A Prospective Multicenter Study for the Noninvasive Diagnosis of Portal Hypertension

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.

Spleno-hepatic index to predict portal hypertension by equilibrium radionuclide ventriculography

BACKGROUND

Structural and morphological changes accompanying liver cirrhosis lead to portal hypertension (PHT), which is the first step of most of the complications in patients with liver cirrhosis. Therefore, the development of noninvasive techniques to detect PHT is crucial for prognosis and treatment.

AIM

The aim of our study was to assess the diagnostic performance of a new spleno-hepatic index (SHI) measured from equilibrium radionuclide ventriculography (ERV) images in detecting patients with cirrhotic PHT.

METHODS AND RESULTS

A total of 38 patients with PHT were compared with 30 controls without liver disease. The SHI was measured on the sum of the tomographic images from the ERV and calculated according to the following formula: SHI=(mean splenic count×longest hepatic length)/mean hepatic count. Mean SHI was 54±14 and 36±8 (P<0.001) among patients with PHT and controls, respectively. A cutoff value of 40 for the SHI allowed a sensitivity of 90% and specificity of 77% to detect PHT. SHI greater than 51 was 100% specific. In a subset of 25 patients, SHI was not correlated with hepatic venous pressure gradient measured invasively in the right hepatic vein (R=-0.08, P=0.70).

CONCLUSION

Quantification of SHI derived from ERV could be used to detect liver cirrhosis with PHT although it is not linearly correlated with the hepatic venous pressure gradient. SHI should be considered as a useful index for the identification of PHT in patients referred for the detection/exploration of cirrhotic cardiomyopathy by ERV.

Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study

Clinically significant portal hypertension (CSPH) is associated with an incremental risk of esophageal varices and overt clinical decompensations. However, hepatic venous pressure gradient (HVPG) measurement, the gold standard for defining CSPH (HVPG≥10 mm Hg) is invasive and therefore not suitable for routine clinical practice. This study aims to develop and validate a radiomics-based model as a noninvasive method for accurate detection of CSPH in cirrhosis. The prospective multicenter diagnostic trial (CHESS1701, ClinicalTrials.gov identifier: NCT03138915) involved 385 patients with cirrhosis from five liver centers in China between August 2016 and September 2017. Patients who had both HVPG measurement and contrast-enhanced CT within 14 days prior to the catheterization were collected. The noninvasive radiomics model, termed rHVPG for CSPH was developed based on CT images in a training cohort consisted of 222 consecutive patients and the diagnostic performance was prospectively assessed in 163 consecutive patients in four external validation cohorts. rHVPG showed a good performance in detection of CSPH with a C-index of 0·849 (95%CI: 0·786-0·911). Application of rHVPG in four external prospective validation cohorts still gave excellent performance with the C-index of 0·889 (95%CI: 0·752-1·000, 0·800 (95%CI: 0·614-0·986), 0·917 (95%CI: 0·772-1·000), and 0·827 (95%CI: 0·618-1·000), respectively. Intraclass correlation coefficients for inter- and intra-observer agreement were 0·92-0·99 and 0·97-0·99, respectively. A radiomics signature was developed and prospectively validated as an accurate method for noninvasive detection of CSPH in cirrhosis. The tool of rHVPG assessment can facilitate the identification of CSPH rapidly when invasive transjugular procedure is not available.

Impact of Splenic Volume and Splenectomy on Prognosis of Hepatocellular Carcinoma Within Milan Criteria After Curative Hepatectomy

BACKGROUND

The prognosis of hepatocellular carcinoma (HCC) with portal hypertension (PH) is very poor. Splenomegaly is considered important evidence of PH. Our aim was to clarify the prognostic value of splenic volume (SV) and the effect of splenectomy on the prognosis of HCC within the Milan criteria after curative hepatectomy.

METHODS

In this single-center retrospective study, we reviewed 160 patients with HCC that met the Milan criteria, including 138 who had undergone hepatectomy and 22 who had undergone hepatectomy and splenectomy between July 2004 and December 2010. SV was measured by three-dimensional computed tomography and patients allocated to three groups (high SV ≥300 mL; low <300 mL; and splenectomy) to compare post-hepatectomy survival rates.

RESULTS

Multivariate analyses showed that SV is an independent prognostic factor for overall and disease-free survival. The overall survival rates at 5 years in the high SV, low SV, and splenectomy groups were 39, 75, and 88%, respectively. The overall survival rate in the high SV group was significantly worse than in the low SV and splenectomy groups (P < 0.001). There was no significant difference between the low SV and splenectomy groups (P = 0.831).

CONCLUSIONS

High SV is an independent predictor of post-hepatectomy HCC recurrence and overall survival. There is no significant difference in prognosis between low SV and splenectomy groups, even though the latter had high SV. Combined splenectomy with hepatectomy for HCC and PH may improve prognosis and be an appropriate alternative when liver transplantation cannot be performed.

Quantification of Liver Surface Nodularity at CT: Utility for Detection of Portal Hypertension

Purpose To determine whether quantification of liver surface nodularity (LSN) provides an estimate of the presence of clinically significant portal hypertension (CSPH) in patients with cirrhosis. Materials and Methods This retrospective study included a training cohort (n = 189) and separate external validation cohort (n = 78), both composed of patients with cirrhosis who underwent abdominal CT and hepatic venous pressure gradient (HVPG) measurement between 2010 and 2016. The LSN score, liver and spleen volumes, liver-to-spleen volume ratio, platelet count to spleen diameter ratio, Iranmanesh score, aspartate amino transferase-to-platelet ratio index, and Fibrosis-4 index were derived from CT images and serum laboratories. The accuracy of the various tests for predicting CSPH was evaluated with area under the receiver operating characteristic curve (AUROC) and compared by using the DeLong test. Student t test and Pearson correlation coefficient were used. Results One hundred eighty-nine patients were analyzed (119 men [mean age ± standard deviation, 57 years ± 11; range, 29-81 years] and 70 women [mean age, 61 years ± 10; range, 34-83 years]; overall mean age, 58 years ± 10; range, 29-83 years). A total of 102 patients (54%) had CSPH. LSN score correlated with HVPG (r = 0.75; P < .001). Patients with CSPH had a higher LSN score than did those without CSPH (3.2 ± 0.6 vs 2.4 ± 0.3; P < .001). A cutoff value of 2.8 had a positive predictive value of 88% for CSPH; the AUROC of LSN was 0.88 ± 0.03. This was higher than that of other available noninvasive tests (DeLong, all P < .001). In the validation cohort, LSN score of 2.8 had a positive predictive value of 86% for CSPH; the AUROC was 0.87 ± 0.04. Conclusion The CT-based liver surface nodularity score demonstrated high diagnostic performance for detecting clinically significant portal hypertension and outperformed multiple other noninvasive tests. © RSNA, 2018 Online supplemental material is available for this article.

Diagnosis and treatment of hepatocellular carcinoma with portal hypertension

Hepatocellular carcinoma (HCC) is a common malignant tumor in China. HCC complicated with portal hypertension is a common problem in clinical practice. Bleeding from esophagogastric varices is a major cause of death in patients with HCC complicated with portal hypertension. How to treat HCC and portal hypertension simultaneously remains one of the challenges for liver surgeons. This article briefly reviews the current status of treatment of HCC with portal hypertension.

Noninvasive Monitoring of Liver Disease Regression after Hepatitis C Eradication Using Gadoxetic Acid-Enhanced MRI

We evaluated changes in relative liver enhancement (RLE) obtained by gadoxetic acid-enhanced MRI (GA-MRI) in the hepatobiliary phase and changes in splenic volume (SV) after hepatitis C virus (HCV) eradication as well as their predictive value for the development of (further) hepatic decompensation during follow-up. This retrospective study comprised 31 consecutive patients with HCV-induced advanced chronic liver disease who underwent GA-MRI before and after successful interferon-free treatment, as well as a cohort of 14 untreated chronic HCV-patients with paired GA-MRI. RLE increased by 66% (20%-94%; P < 0.001) from pre- to posttreatment, while SV decreased by -16% (-28% to -8%; P < 0.001). However, SV increased in 16% (5/31) of patients, the identical subjects who showed a decrease in RLE (GA-MRI-nonresponse). We observed an inverse correlation between the changes in RLE and SV (ρ=-0.608; P < 0.001). In the untreated patients, there was a decrease in RLE by -11% (-25% to -3%; P=0.019) and an increase in SV by 23% (7%-43%; P=0.004) (both P < 0.001 versus treated patients). Interestingly, GA-MRI-nonresponse was associated with a substantially increased risk of (further) hepatic decompensation 2 years after the end of treatment: 80% versus 8%; P < 0.001. GA-MRI might distinguish between individuals at low and high risk of (further) hepatic decompensation (GA-MRI-nonresponse) after HCV eradication. This could allow for individualized surveillance strategies.