The Liver Segmental Volume Ratio for Noninvasive Detection of Cirrhosis: Comparison With Established Linear and Volumetric Measures

Liver segmental volumes and their relationship with 5-year prognostication

PURPOSE

This study aimed to analyze the predictive value of caudate to right lobe ratio (CRL-R) and liver segmental volume ratio (LSVR) for chronic liver disease (CLD) on routine abdominal CT scans and their association with 5-year decompensation- and transplant-free survival.

METHOD

This retrospective study included 108 patients without CLD and 98 patients with biopsy-proven CLD. All patients underwent abdominal CT scans between 03/2015 and 08/2017. Patients with CLD were divided into three groups: early CLD (F0-F2; eCLD; n = 40), advanced CLD (F3-F4; aCLD; n = 20), and aCLD with clinically significant portal hypertension (aCLDPH; n = 38). CRL-R and LSVR were compared between groups using Kruskal-Wallis test and ROC analysis to determine cutoff-values. 5-year decompensation- and transplant-free survival were assessed by Kaplan-Meier curve analysis.

RESULTS

CRL-R and LSVR were significantly different between all groups (p < 0.001). A CRL-R cutoff-value of > 0.99 predicted aCLD with a sensitivity of 69% and a specificity of 80% (AUC = 0.75, p < 0.001), while LSVR > 0.37 had a sensitivity of 67% and a specificity of 84% (AUC = 0.80, p < 0.001). CLD-patients with both CRL-R > 0.99 and LSVR > 0.37 had a significantly lower probability of 5-year decompensation-free survival (31%) as well as lower probability of 5-year transplant-free survival (41%) than those with a CRL-R < 0.99 and/or LSVR < 0.37 (70%, 62%, p = 0.006, p = 0.038).

CONCLUSION

CRL-R and LSVR showed a high predictive value for CLD on routine abdominal CT scans. In patients with CLD, both CRL-R and LSVR may be combined and are associated with 5-year decompensation-free and transplant-free survival.

The impact of hepatic and splenic volumetric assessment in imaging for chronic liver disease: a narrative review

Chronic liver disease is responsible for significant morbidity and mortality worldwide. Abdominal computed tomography (CT) and magnetic resonance imaging (MRI) can fully visualise the liver and adjacent structures in the upper abdomen providing a reproducible assessment of the liver and biliary system and can detect features of portal hypertension. Subjective interpretation of CT and MRI in the assessment of liver parenchyma for early and advanced stages of fibrosis (pre-cirrhosis), as well as severity of portal hypertension, is limited. Quantitative and reproducible measurements of hepatic and splenic volumes have been shown to correlate with fibrosis staging, clinical outcomes, and mortality. In this review, we will explore the role of volumetric measurements in relation to diagnosis, assessment of severity and prediction of outcomes in chronic liver disease patients. We conclude that volumetric analysis of the liver and spleen can provide important information in such patients, has the potential to stratify patients' stage of hepatic fibrosis and disease severity, and can provide critical prognostic information. CRITICAL RELEVANCE STATEMENT: This review highlights the role of volumetric measurements of the liver and spleen using CT and MRI in relation to diagnosis, assessment of severity, and prediction of outcomes in chronic liver disease patients. KEY POINTS: Volumetry of the liver and spleen using CT and MRI correlates with hepatic fibrosis stages and cirrhosis. Volumetric measurements correlate with chronic liver disease outcomes. Fully automated methods for volumetry are required for implementation into routine clinical practice.

Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers

A diagnosis of cirrhosis initiates a shift in the management of chronic liver disease and affects the diagnostic workflow and treatment decision of primary liver cancer. Liver biopsy remains the gold standard for cirrhosis diagnosis, but it is invasive and susceptible to sampling bias and observer variability. Various qualitative and quantitative imaging biomarkers based on ultrasound, CT and MRI have been proposed for noninvasive diagnosis of cirrhosis. Qualitative imaging features are easy to apply but have moderate diagnostic sensitivity. Elastography techniques allow quantitative assessment of liver stiffness and are highly accurate for cirrhosis diagnosis. Ultrasound elastography are widely used in clinical practice, while MR elastography has narrower availability. Although not applicable in clinical practice yet, other quantitative imaging features, including liver surface nodularity, linear and volumetric measurement, extracellular volume fraction, liver enhancement on hepatobiliary phase, and parameters derived from diffusion-weighted imaging, can provide additional information of liver morphology, perfusion, and function, thus may increase diagnosis performance. The introduction of radiomics and deep learning has further improved diagnostic accuracy while reducing subjectivity. Several imaging features may also help to assess liver function and outcomes in patients with cirrhosis. In this review, we summarize the qualitative and quantitative imaging biomarkers for noninvasive cirrhosis diagnosis, and the assessment of liver function and outcomes, and discuss the challenges and future directions in this field.

Comparison between CT volumetry, technetium99m galactosyl-serum-albumin scintigraphy, and gadoxetic-acid-enhanced MRI to estimate the liver fibrosis stage in preoperative patients

OBJECTIVES

To compare the efficacy of computed tomography volumetry (CTV), technetium99m galactosyl-serum-albumin (99mTc-GSA) scintigraphy, and gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic-acid-enhanced MRI (EOB-MRI) in estimating the liver fibrosis (LF) stage in patients undergoing liver resection.

METHODS

This retrospective study included 91 consecutive patients who had undergone preoperative dynamic CT and 99mTc-GSA scintigraphy. EOB-MRI was performed in 76 patients. CTV was used to measure the total liver volume (TLV), spleen volume (SV), normalised to the body surface area (BSA), and liver-to-spleen volume ratio (TLV/SV). 99mTc-GSA scintigraphy provided LHL15, HH15, and GSA indices. The liver-to-spleen ratio (LSR) was calculated in the hepatobiliary phase of EOB-MRI. Hyaluronic acid and type 4 collagen levels were measured in 65 patients. Logistic regression and receiver operating characteristic (ROC) analyses were performed to identify useful parameters for estimating the LF stage and laboratory data.

RESULTS

According to the multivariable logistic regression analysis, SV/BSA (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.003-1.02; p = 0.011), LSR (OR, 0.06; 95%CI, 0.004-0.70; p = 0.026), and hyaluronic acid (OR, 1.01; 95%CI, 1.001-1.02; p = 0.024) were independent variables for severe LF (F3-4). Combined SV/BSA, LSR, and hyaluronic acid correctly estimated severe LF, with an AUC of 0.91, which was significantly larger than the AUCs of the GSA index (AUC = 0.84), SV/BSA (AUC = 0.83), or LSR (AUC = 0.75) alone.

CONCLUSIONS

Combined CTV, EOB-MRI, and hyaluronic acid analyses improved the estimation accuracy of severe LF compared to CTV, EOB-MRI, or 99mTc-GSA scintigraphy individually.

CLINICAL RELEVANCE STATEMENT

The combined analysis of spleen volume on CT volumetry, liver-to-spleen ratio on gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic-acid-enhanced MRI, and hyaluronic acid can identify severe liver fibrosis associated with a high risk of liver failure after hepatectomy and recurrence in patients with hepatocellular carcinoma.

KEY POINTS

• Spleen volume of CT volumetry normalised to the body surface area, liver-to-spleen ratio of EOB-MRI, and hyaluronic acid were independent variables for liver fibrosis. • CT volumetry and EOB-MRI enable the detection of severe liver fibrosis, which may correlate with post-hepatectomy liver failure and complications. • Combined CT volumetry, gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic-acid-enhanced MRI (EOB-MRI), and hyaluronic acid analyses improved the estimation of severe liver fibrosis compared to technetium99m galactosyl-serum-albumin scintigraphy.

CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease

Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.

Portal hypertension increases the risk of hepatic decompensation after 90Yttrium radioembolization in patients with hepatocellular carcinoma: a cohort study

Background

Transarterial radioembolization (TARE) is increasingly used in patients with hepatocellular carcinoma (HCC). This treatment can induce or impair portal hypertension, leading to hepatic decompensation. TARE also promotes changes in liver and spleen volumes that may modify therapeutic decisions and outcomes after therapy.

Objectives

We aimed to investigate the impact of TARE on the incidence of decompensation events and its predictive factors.

Design

In all, 63 consecutive patients treated with TARE between February 2012 and December 2018 were retrospectively included.

Methods

We assessed clinical (including Barcelona Clinic Liver Cancer stage, portal hypertension assessment, and liver decompensation), laboratory parameters, and liver and spleen volumes before and 6 and 12 weeks after treatment. A multivariate analysis was performed.

Results

In total, 18 out of 63 (28.6%) patients had liver decompensation (ascites, variceal bleeding, jaundice, or encephalopathy) within the first 3 months after therapy, not associated with tumor progression. Clinically significant portal hypertension (CSPH) and bilobar treatment independently predicted the development of liver decompensation after TARE. A significant volume increase in the non-treated hemi-liver was observed only in patients with unilobar treatment (median volume increase of 20.2% in patients with right lobe TARE; p = 0.007), especially in those without CSPH. Spleen volume also increased after TARE (median volume increase of 16.1%; p = 0.0001) and was associated with worsening liver function scores and decreased platelet count.

Conclusion

Bilobar TARE and CSPH may be associated with an increased risk of liver decompensation in patients with intermediate or advanced HCC. A careful assessment considering these variables before therapy may optimize candidate selection and improve treatment planning.

Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning

PURPOSE

To evaluate the effectiveness of automated liver segmental volume quantification and calculation of the liver segmental volume ratio (LSVR) on a non-contrast T1-vibe Dixon liver MRI sequence using a deep learning segmentation pipeline.

METHOD

A dataset of 200 liver MRI with a non-contrast 3 mm T1-vibe Dixon sequence was manually labeledslice-by-sliceby an expert for Couinaud liver segments, while portal and hepatic veins were labeled separately. A convolutional neural networkwas trainedusing 170 liver MRI for training and 30 for evaluation. Liver segmental volumes without liver vessels were retrieved and LSVR was calculated as the liver segmental volumes I-III divided by the liver segmental volumes IV-VIII. LSVR was compared with the expert manual LSVR calculation and the LSVR calculated on CT scans in 30 patients with CT and MRI within 6 months.

RESULTS

Theconvolutional neural networkclassified the Couinaud segments I-VIII with an average Dice score of 0.770 ± 0.03, ranging between 0.726 ± 0.13 (segment IVb) and 0.810 ± 0.09 (segment V). The calculated mean LSVR with liver MRI unseen by the model was 0.32 ± 0.14, as compared with manually quantified LSVR of 0.33 ± 0.15, resulting in a mean absolute error (MAE) of 0.02. A comparable LSVR of 0.35 ± 0.14 with a MAE of 0.04 resulted with the LSRV retrieved from the CT scans. The automated LSVR showed significant correlation with the manual MRI LSVR (Spearman r = 0.97, p < 0.001) and CT LSVR (Spearman r = 0.95, p < 0.001).

CONCLUSIONS

A convolutional neural network allowed for accurate automated liver segmental volume quantification and calculation of LSVR based on a non-contrast T1-vibe Dixon sequence.

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.

Comparison between CT volumetry and extracellular volume fraction using liver dynamic CT for the predictive ability of liver fibrosis in patients with hepatocellular carcinoma

OBJECTIVES

To compare the predictive ability of liver fibrosis (LF) by CT-volumetry (CTV) for liver and spleen and extracellular volume fraction (ECV) for liver in patients undergoing liver resection.

METHODS

We retrospectively analysed 90 consecutive patients who underwent CTV and ECV. Manually placed region-of-interest ECV (manual-ECV), rigid-registration ECV (rigid-ECV), and nonrigid-registration ECV (nonrigid-ECV) were calculated as ECV(%) = (1-haematocrit) × (ΔHUliver/ΔHUaorta), where ΔHU = subtraction of unenhanced phase from equilibrium phase (240 s). Manual-ECV was compared with CTV for the estimation of LF. The total liver volume to body surface area (TLV/BSA), splenic volume to BSA (SV/BSA), ratio of TLV to SV (TLV/SV), ratio of right liver volume to SV (RV/SV), and liver segmental volume ratio (LSVR) were measured. ROC analyses were performed for ECV and CTV.

RESULTS

After excluding 10 patients, seventy-eight (97.5%) out of 80 patients had a Child-Pugh score of 5 points, and two (2.5%) patients had a Child-Pugh score of 6 points. AUC of ECV showed no significant difference among manual-ECV, rigid-ECV, and nonrigid-ECV. TLV/BSA, SV/BSA, TLV/SV, and RV/SV had a higher correlation with LF grades than manual-ECV. AUC of SV/BSA was significantly higher than that of manual-ECV in F0-1 vs F2-4 and F0-2 vs F3-4. AUC of SV/BSA (0.76-0.83) was higher than that of manual-ECV (0.61-0.75) for all LF grades, although manual-ECV could differentiate between F0-3 and F4 at high AUC (0.75).

CONCLUSIONS

In patients undergoing liver resection, SV/BSA is a better method for estimating severe LF grades, although manual-ECV has the ability to estimate cirrhosis (≥ F4).

KEY POINTS

The splenic volume is a better method for estimating liver fibrosis grades. The extracellular volume fraction is also a candidate for the estimation of severe liver fibrosis.

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.

Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis

PURPOSE

To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I-III/IV-VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis.

MATERIALS AND METHODS

For this Health Insurance Portability and Accountability Act-compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0-F4, 2000-2016). Dataset 2 consisted of patients who had cirrhosis from other causes who underwent liver biopsy (Ishak 0-6, 2001-2021). Whole liver, LSVR, and spleen volumes were measured with contrast-enhanced CT by radiologists and the DL model. Areas under the receiver operating characteristic curve (AUCs) for diagnosing advanced fibrosis (≥METAVIR F2 or Ishak 3) and cirrhosis (≥METAVIR F4 or Ishak 5) were calculated. Multivariable models were built on dataset 1 and tested on datasets 1 (hold out) and 2.

RESULTS

Datasets 1 and 2 consisted of 406 patients (median age, 50 years [IQR, 44-56 years]; 297 men) and 207 patients (median age, 50 years [IQR, 41-57 years]; 147 men), respectively. In dataset 1, the prediction of cirrhosis was similar between the manual versus automated measurements for spleen volume (AUC, 0.86 [95% CI: 0.82, 0.9] vs 0.85 [95% CI: 0.81, 0.89]; significantly noninferior, P < .001) and LSVR (AUC, 0.83 [95% CI: 0.78, 0.87] vs 0.79 [95% CI: 0.74, 0.84]; P < .001). The best performing multivariable model achieved AUCs of 0.94 (95% CI: 0.89, 0.99) and 0.79 (95% CI: 0.71, 0.87) for cirrhosis and 0.8 (95% CI: 0.69, 0.91) and 0.71 (95% CI: 0.64, 0.78) for advanced fibrosis in datasets 1 and 2, respectively.

CONCLUSION

The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis.Keywords: CT, Liver, Cirrhosis, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2022.

Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques

Liver fibrosis features excessive protein accumulation in the liver interstitial space resulting from repeated tissue injury due to chronic liver disease. Liver fibrosis eventually proceeds to cirrhosis and associated complications. So, early diagnosis and staging of liver fibrosis are of vital importance for clinical treatment. Liver biopsy remains the gold standard for the diagnosing and staging of fibrosis, but it is suboptimal due to various limitations. Recently, efforts have been made to migrate toward noninvasive techniques for assessing liver fibrosis. CT is relatively easy to perform, relatively standardized for different scanners, and does not require additional hardware in liver fibrosis staging. MRI is frequently performed to characterize indeterminate liver lesions. Because it does not use ionizing radiation and features high image contrast, its role has increased in the staging of liver fibrosis. More recently, several studies on liver fibrosis staging using deep learning algorithms in CT or MRI have been proposed and have shown meaningful results. In this review, we summarize the basic concept, diagnostic performance, and advantages and limitations of each technique to noninvasively stage liver fibrosis.

Value-added Opportunistic CT Screening: State of the Art

Opportunistic CT screening leverages robust imaging data embedded within abdominal and thoracic scans that are generally unrelated to the specific clinical indication and have heretofore gone largely unused. This incidental imaging information may prove beneficial to patients in terms of wellness, prevention, risk profiling, and presymptomatic detection of relevant disease. The growing interest in CT-based opportunistic screening relates to a confluence of factors: the objective and generalizable nature of CT-based body composition measures, the emergence of fully automated explainable AI solutions, the sheer volume of body CT scans performed, and the increasing emphasis on precision medicine and value-added initiatives. With a systematic approach to body composition and other useful CT markers, initial evidence suggests that their ability to help radiologists assess biologic age and predict future adverse cardiometabolic events rivals even the best available clinical reference standards. Emerging data suggest that standalone "intended" CT screening over an unorganized opportunistic approach may be justified, especially when combined with established cancer screening. This review will discuss the current status of opportunistic CT screening, including specific body composition markers and the various disease processes that may be impacted. The remaining hurdles to widespread clinical adoption include generalization to more diverse patient populations, disparate technical settings, and reimbursement.

Assessment of Three-Phasic CT Scan Findings of Cirrhosis Due to Primary Sclerosing Cholangitis Versus Cryptogenic Cirrhosis

BACKGROUND

The CT findings of cirrhosis caused by primary sclerosing cholangitis (PSC) differ from cryptogenic cirrhosis. PSC could become complicated with biliary cirrhosis and cholangiocarcinoma. This study aimed at augmenting the information on the role of the three-phasic-abdominopelvic CT scan in PSC.

MATERIAL AND METHODS

A total of 185 CT scans were retrospectively reviewed, including 100 patients with cryptogenic cirrhosis and 85 patients with PSC-cirrhosis. Different morphologic criteria were compared, including segmental atrophy/hypertrophy, hepatic contour, portal-hypertension, perihilar lymphadenopathy, biliary tree dilatation, gallbladder appearance. Inflammatory-bowel-disease (IBD) and cholangiocarcinoma frequency, presence of perihilar lymph nodes (LNs), and their size during end-stage PSC cirrhosis are investigated.

RESULTS

Six findings occur more frequently with PSC than those diagnosed with cryptogenic cirrhosis. Modified caudate/right lobe (m-CRL) ratio >0.73, moderate and severe lobulated liver contour, lateral left lobe atrophy, over distended gallbladder (GB), biliary tree dilatation and wall thickening, and LN sizes were higher in PSC patients as compared to cryptogenic cirrhosis (P < 0.005). Ascites and portosystemic collateral formations were significant in cryptogenic cirrhosis compared to PSC patients (P < 0.005). Cholangiocarcinoma frequency in PSC patients was 14.7%, and the frequency of inflammatory bowel disease (IBD) was 57.6%. Further, 22.4% of the patients were diagnosed with IBD and PSC simultaneously. The LN number and size in PSC patients were not different between those with or without cholangiocarcinoma.

CONCLUSION

Using three-phasic CT scans and PSC characteristics could be considered as an additional suggestion besides pathology measures. Diagnosis of PSC based on histological findings could be a last resort due to its invasive essence and specific characteristics of PSC in imaging.

Artificial intelligence enhances the accuracy of portal and hepatic vein extraction in computed tomography for virtual hepatectomy

BACKGROUND/PURPOSE

Current conventional algorithms used for 3-dimensional simulation in virtual hepatectomy still have difficulties distinguishing the portal vein (PV) and hepatic vein (HV). The accuracy of these algorithms was compared with a new deep-learning based algorithm (DLA) using artificial intelligence.

METHODS

A total of 110 living liver donor candidates until 2017, and 46 donor candidates until 2019 were allocated to the training group and validation groups for the DLA, respectively. All PV or HV branches were labeled based on Couinaud's segment classification and the Brisbane 2000 Terminology by hepato-biliary surgeons. Misclassified and missing branches were compared between a conventional tracking-based algorithm (TA) and DLA in the validation group.

RESULTS

The sensitivity, specificity, and Dice coefficient for the PV were 0.58, 0.98, and 0.69 using the TA; and 0.84, 0.97, and 0.90 using the DLA (P < .001, excluding specificity); and for the HV, 0.81, 087, and 0.83 using the TA; and 0.93, 0.94 and 0.94 using the DLA (P < .001 to P = .001). The DLA exhibited greater accuracy than the TA.

CONCLUSION

Compared with the TA, artificial intelligence enhanced the accuracy of extraction of the PV and HVs in computed tomography.

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (n = 1960) or renal donor evaluation (n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Two-dimensional (2D) morphologic measurements can quantify the severity of liver disease in children with autosomal recessive polycystic kidney disease (ARPKD)

PURPOSE

To evaluate the correlation of 2D shape-based features with magnetic resonance elastography (MRE)-derived liver stiffness and portal hypertension (pHTN) in children with ARPKD-associated congenital hepatic fibrosis.

METHODS

In a prospective IRB-approved study, 14 children with ARPKD (mean age ± SD = 13.8 ± 5.8 years) and 14 healthy controls (mean age ± SD = 13.7 ± 3.9 years) underwent liver MRE. A 2D region of interest (ROI) outlining the left liver lobe at the level of the abdominal aorta was drawn on sagittal T2-weighted images. Eight shape features (perimeter, major axis length, maximum diameter, perimeter to surface ratio (PSR), elongation, sphericity, minor axis length, and mesh surface) describing the 2D-ROI were calculated. Spearman's correlation was calculated between shape features and MRE-derived liver stiffness (kPa) (n = 28). Shape features were compared between participants with ARPKD with pHTN (splenomegaly and thrombocytopenia), (n = 4) and without pHTN (n = 8) using the Mann Whitney U test. Receiver operating characteristic (ROC) curves were generated to examine the diagnostic accuracy of shape features in identifying cases with liver stiffness > 2.9 kPa.

RESULTS

In ARPKD participants and healthy controls, all eight shape features, except elongation, showed moderate to strong correlation with liver stiffness (kPa); the perimeter surface ratio had the strongest correlation (rho = - 0.75, p < 0.001). In ROC analysis, a cut-off of PSR ≤ 0.057 mm^-1 gave 100% (95% CI: 59.0-100.0) sensitivity and 100% (95% CI: 83.9-100.0) specificity in identifying ARPKD participants with liver stiffness > 2.9 kPa, with an area under the ROC curve (AUC) of 1.0 (95% CI: 0.88-1.00). Individuals with pHTN had a lower median PSR (mean ± SD = 0.05 ± 0.01) than those without (0.07 ± 0.01; p = 0.027) with an AUC of 0.91 (95% CI: 0.60-0.99) in differentiating the participants with and without pHTN.

CONCLUSION

Shape-based features of the left liver lobe show potential as non-invasive biomarkers of liver fibrosis and portal hypertension in children with ARPKD.

Computed Tomography Techniques, Protocols, Advancements, and Future Directions in Liver Diseases

Computed tomography (CT) is often performed as the initial imaging study for the workup of patients with known or suspected liver disease. Our article reviews liver CT techniques and protocols in clinical practice along with updates on relevant CT advances, including wide-detector CT, radiation dose optimization, and multienergy scanning, that have already shown clinical impact. Particular emphasis is placed on optimizing the late arterial phase of enhancement, which is critical to evaluation of hepatocellular carcinoma. We also discuss emerging techniques that may soon influence clinical care.

Liver segmental volume and attenuation ratio (LSVAR) on portal venous CT scans improves the detection of clinically significant liver fibrosis compared to liver segmental volume ratio (LSVR)

Background

The aim of this proof-of-concept study was to show that the liver segmental volume and attenuation ratio (LSVAR) improves the detection of significant liver fibrosis on portal venous CT scans by adding the liver vein to cava attenuation (LVCA) to the liver segmental volume ratio (LSVR).

Material and methods

Patients who underwent portal venous phase abdominal CT scans and MR elastography (reference standard) within 3 months between 02/2016 and 05/2017 were included. The LSVAR was calculated on portal venous CT scans as LSVR*LVCA, while the LSVR represented the volume ratio between Couinaud segments I-III and IV-VIII, and the LVCA represented the density of the liver veins compared to the density in the vena cava. The LSVAR and LSVR were compared between patients with and without significantly elevated liver stiffness (based on a cutoff value of 3.5 kPa) using the Mann–Whitney U test and ROC curve analysis.

Results

The LSVR and LSVAR allowed significant differentiation between patients with (n = 19) and without (n = 122) significantly elevated liver stiffness (p < 0.001). However, the LSVAR showed a higher area under the curve (AUC = 0.96) than the LSVR (AUC = 0.74). The optimal cutoff value was 0.34 for the LSVR, which detected clinically increased liver stiffness with a sensitivity of 53% and a specificity of 88%. With a cutoff value of 0.67 for the LSVAR, the sensitivity increased to 95% while maintaining a specificity of 89%.

Conclusion

The LSVAR improves the detection of significant liver fibrosis on portal venous CT scans compared to the LSVR.

Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ^©RSNA, 2021.

Utility of Multiparametric CT for Identification of High-Risk NAFLD

OBJECTIVE. The purpose of this study was to evaluate the utility of laboratory and CT metrics in identifying patients with high-risk nonalcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS. Patients with biopsy-proven NAFLD who underwent CT within 1 year of biopsy were included. Histopathologic review was performed by an experienced gastrointestinal pathologist to determine steatosis, inflammation, and fibrosis. The presence of any lobular inflammation and hepatocyte ballooning was categorized as nonalcoholic steatohepatitis (NASH). Patients with NAFLD and advanced fibrosis (stage F3 or higher) were categorized as having high-risk NAFLD. Aspartate transaminase to platelet ratio index and Fibrosis-4 (FIB-4) laboratory scores were calculated. CT metrics included hepatic attenuation, liver segmental volume ratio (LSVR), splenic volume, liver surface nodularity score, and selected texture features. In addition, two readers subjectively assessed the presence of NASH (present or not present) and fibrosis (stages F0-F4). RESULTS. A total of 186 patients with NAFLD (mean age, 49 years; 74 men and 112 women) were included. Of these, 87 (47%) had NASH and 112 (60%) had moderate to severe steatosis. A total of 51 patients were classified as fibrosis stage F0, 42 as F1, 23 as F2, 37 as F3, and 33 as F4. Additionally, 70 (38%) had advanced fibrosis (stage F3 or F4) and were considered to have high-risk NAFLD. FIB-4 score correlated with fibrosis (ROC AUC of 0.75 for identifying high-risk NAFLD). Of the individual CT parameters, LSVR and splenic volume performed best (AUC of 0.69 for both for detecting high-risk NAFLD). Subjective reader assessment performed best among all parameters (AUCs of 0.78 for reader 1 and 0.79 for reader 2 for detecting high-risk NAFLD). FIB-4 and subjective scores were complementary (combined AUC of 0.82 for detecting high-risk NAFLD). For NASH assessment, FIB-4 performed best (AUC of 0.68), whereas the AUCs were less than 0.60 for all individual CT features and subjective assessments. CONCLUSION. FIB-4 and multiple CT findings can identify patients with high-risk NAFLD (advanced fibrosis or cirrhosis). However, the presence of NASH is elusive on CT.

Necrosis volume and Choi criteria predict the response to endoscopic ultrasonography-guided HybridTherm ablation of locally advanced pancreatic cancer

Background and study aims  Endoscopic ultrasound (EUS)-guided ablation of pancreatic ductal adenocarcinoma (PDAC) with HybridTherm-Probe (EUS-HTP) is feasible and safe, but the radiological response and ideal tool to measure it have not been investigated yet. The aims of this study were to: 1) assess the radiological response to EUS-HTP evaluating the vital tumor volume reduction rate, Response Evaluation Criteria in Solid Tumors (RECIST1.1) and Choi criteria; 2) determine the prognostic predictive yield of these criteria. Patients and methods  A retrospective analysis was performed of patients with locally advanced PDAC after primary treatment or unfit for chemotherapy prospectively treated by EUS-HTP. Computed tomography scan was performed 1 month after EUS-HTP to evaluate: 1) vital tumor volume reduction rate (VTVRR) by measuring necrosis and tumor volumes through a computer-aided detection system; and 2) RECIST1.1 and Choi criteria. Results  EUS-HTP was feasible in 22 of 31 patients (71 %), with no severe adverse events. Median post-HTP survival was 7 months (1 - 35). Compared to pre-HTP tumor volume, a significant 1-month VTVRR (mean 21.4 %) was observed after EUS-HTP ( P  = 0.005). We identified through ROC analysis a VTVRR > 11.46 % as the best cut-off to determine post-HTP 6-month survival outcome (AUC = 0.733; sensitivity = 70.0 %, specificity = 83.3 %). This cut-off was significantly associated with longer overall survival (HR = 0.372; P  = 0.039). According to RECIST1.1 and Choi criteria, good responders to EUS-HTP were 60 % and 46.7 %, respectively. Good responders according to Choi, but not to RECIST1.1, had longer survival (HR = 0.407; P  = 0.04). Conclusions  EUS-HTP induces a significant 1-month VTVRR. This effect is assessed accurately by evaluation of necrosis and tumor volumes. Use of VTVRR and Choi criteria, but not RECIST 1.1 criteria, might identify patients who could benefit clinically from EUS-HTP.

Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis

PURPOSE

Liver shape variations have been considered as feasible indicators of liver fibrosis. However, current statistical shape models (SSM) based on principal component analysis represent gross shape variations without considering the association with the fibrosis stage. Therefore, we aimed at the application of a statistical shape modelling approach using partial least squares regression (PLSR), which explicitly uses the stage as supervised information, for understanding the shape variations associated with the stage as well as predicting it in contrast-enhanced MR images.

METHODS

Contrast-enhanced MR images of 51 patients with fibrosis stages F0/1 (n = 18), F2 (n = 15), F3 (n = 7) and F4 (n = 11) were used. The livers were manually segmented from the images. An SSM was constructed using PLSR, by which shape variation modes (scores) that were explicitly associated with the reference pathological fibrosis stage were derived. The stage was predicted using a support vector machine (SVM) based on the PLSR scores. The performance was assessed using the area under receiver operating characteristic curve (AUC).

RESULTS

In addition to commonly known shape variations, such as enlargement of left lobe and shrinkage of right lobe, our model represented detailed variations, such as enlargement of caudate lobe and the posterior part of right lobe, and shrinkage in the anterior part of right lobe. These variations qualitatively agreed with localized volumetric variations reported in clinical studies. The accuracy (AUC) at classifications F0/1 versus F2‒4 (significant fibrosis), F0‒2 versus F3‒4 and F0‒3 versus F4 (cirrhosis) were 0.90 ± 0.03, 0.80 ± 0.05 and 0.82 ± 0.05, respectively.

CONCLUSIONS

The proposed approach offered an explicit representation of commonly known as well as detailed shape variations associated with liver fibrosis stage. Thus, the application of PLSR-based SSM is feasible for understanding the shape variations associated with the liver fibrosis stage and predicting it.

Current Imaging Techniques for Noninvasive Staging of Hepatic Fibrosis

OBJECTIVE. The purpose of this article is to discuss quantitative methods of CT, MRI, and ultrasound (US) for noninvasive staging of hepatic fibrosis. Hepatic fibrosis is the hallmark of chronic liver disease (CLD), and staging by random liver biopsy is invasive and prone to sampling errors and subjectivity. Several noninvasive quantitative imaging methods are under development or in clinical use. The accuracy, precision, technical aspects, advantages, and disadvantages of each method are discussed. CONCLUSION. The most promising methods are the liver surface nodularity score using CT and measurement of liver stiffness using MR elastography or US elastography.

Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data

OBJECTIVE. The purpose of this study is to develop a machine learning model to categorically classify MR elastography (MRE)-derived liver stiffness using clinical and nonelastographic MRI radiomic features in pediatric and young adult patients with known or suspected liver disease. MATERIALS AND METHODS. Clinical data (27 demographic, anthropomorphic, medical history, and laboratory features), MRI presence of liver fat and chemical shift-encoded fat fraction, and MRE mean liver stiffness measurements were retrieved from electronic medical records. MRI radiomic data (105 features) were extracted from T2-weighted fast spin-echo images. Patients were categorized by mean liver stiffness (< 3 vs ≥ 3 kPa). Support vector machine (SVM) models were used to perform two-class classification using clinical features, radiomic features, and both clinical and radiomic features. Our proposed model was internally evaluated in 225 patients (mean age, 14.1 years) and externally evaluated in an independent cohort of 84 patients (mean age, 13.7 years). Diagnostic performance was assessed using ROC AUC values. RESULTS. In our internal cross-validation model, the combination of clinical and radiomic features produced the best performance (AUC = 0.84), compared with clinical (AUC = 0.77) or radiomic (AUC = 0.70) features alone. Using both clinical and radiomic features, the SVM model was able to correctly classify patients with accuracy of 81.8%, sensitivity of 72.2%, and specificity of 87.0%. In our external validation experiment, this SVM model achieved an accuracy of 75.0%, sensitivity of 63.6%, specificity of 82.4%, and AUC of 0.80. CONCLUSION. An SVM learning model incorporating clinical and T2-weighted radiomic features has fair-to-good diagnostic performance for categorically classifying liver stiffness.

Multiparametric CT for Noninvasive Staging of Hepatitis C Virus-Related Liver Fibrosis: Correlation With the Histopathologic Fibrosis Score

OBJECTIVE

The objective was to develop a multiparametric CT algorithm to stage liver fibrosis in patients with chronic hepatitis C virus (HCV) infection.

MATERIALS AND METHODS

Abdominal CT and laboratory measures in 469 patients with HCV (340 men and 129 women; mean age, 50.1 years) were compared against the histopathologic Metavir fibrosis reference standard (F0, n = 49 patients; F1, n = 69 patients; F2, n = 102 patients; F3, n = 76 patients; F4, n = 173 patients). From the initial candidate pool, nine CT and two laboratory measures were included in the final assessment (CT-based features: hepatosplenic volumetrics, texture features, liver surface nodularity [LSN] score, and linear CT measurements; laboratory-based measures: Fibrosis-4 [FIB-4] score and aspartate transaminase-to-platelets ratio index [APRI]). Univariate logistic regression and multivariate logistic regression were performed with ROC analysis, proportional odds modeling, and probabilities.

RESULTS

ROC AUC values for the model combining all 11 parameters for discriminating significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (F4) were 0.928, 0.956, and 0.972, respectively. For all nine CT-based parameters, these values were 0.905, 0.936, and 0.972, respectively. Using more simplified panels of two, three, or four parameters yielded good diagnostic performance; for example, a two-parameter model combining only LSN score with FIB-4 score had ROC AUC values of 0.886, 0.915, and 0.932, for significant fibrosis, advanced fibrosis, and cirrhosis. The LSN score performed best in the univariate analysis.

CONCLUSION

Multiparametric CT assessment of HCV-related liver fibrosis further improves performance over the performance of individual parameters. An abbreviated panel of LSN score and FIB-4 score approached the diagnostic performance of more exhaustive panels. Results of the abbreviated panel compare favorably with elastography, but this approach has the advantage of retrospective assessment using preexisting data without planning.

Volumetric analysis at abdominal CT: oncologic and non-oncologic applications

Volumetric analysis is an objective three-dimensional assessment of a lesion or organ that may more accurately depict the burden of complex objects compared to traditional linear size measurement. Small changes in linear size are amplified by corresponding changes in volume, which could have significant clinical implications. Though early methods of calculating volumes were time-consuming and laborious, multiple software platforms are now available with varying degrees of user-software interaction ranging from manual to fully automated. For the assessment of primary malignancy and metastatic disease, volumetric measurements have shown utility in the evaluation of disease burden prior to and following therapy in a variety of cancers. Additionally, volume can be useful in treatment planning prior to resection or locoregional therapies, particularly for hepatic tumours. The utility of CT volumetry in a wide spectrum of non-oncologic pathology has also been described. While clear advantages exist in certain applications, some data have shown that volume is not always the superior method of size assessment and the associated labor intensity may not be worthwhile. Further, lack of uniformity among software platforms is a challenge to widespread implementation. This review will discuss CT volumetry and its potential oncologic and non-oncologic applications in abdominal imaging, as well as advantages and limitations to this quantitative technique.

Accuracy of liver surface nodularity quantification on MDCT for staging hepatic fibrosis in patients with hepatitis C virus

PURPOSE

To evaluate semi-automated measurement of liver surface nodularity (LSN) on MDCT in a cause-specific cohort of patients with chronic hepatitis C virus infection (HCV) for identification of hepatic fibrosis (stages F0-4).

METHODS

MDCT scans in patients with known HCV were evaluated with an independently validated, semi-automated LSN measurement tool. Consecutive LSN measurements along the anterior liver surface were performed to derive mean LSN scores. Scores were compared with METAVIR fibrosis stage (F0-4). Fibrosis stages F0-3 were based on biopsy results within 1 year of CT. Most patients with cirrhosis (F4) also had biopsy within 1 year; the remaining cases had unequivocal clinical/imaging evidence of cirrhosis and biopsy was not indicated.

RESULTS

288 patients (79F/209M; mean age, 49.7 years) with known HCV were stratified based on METAVIR fibrosis stage: F0 (n = 43), F1 (n = 29), F2 (n = 53), F3 (n = 37), and F4 (n = 126). LSN scores increased with increasing fibrosis (mean: F0 = 2.3 ± 0.2, F1 = 2.4 ± 0.3, F2 = 2.6 ± 0.5, F3 = 2.9 ± 0.6, F4 = 3.8 ± 1.0; p < 0.001). For identification of significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (≥ F4), the ROC AUCs were 0.88, 0.89, and 0.90, respectively. The sensitivity and specificity for significant fibrosis (≥ F2) using LSN threshold of 2.80 were 0.68 and 0.97; for advanced fibrosis (≥ F3; threshold = 2.77) were 0.83 and 0.85; and for cirrhosis (≥ F4, LSN threshold = 2.9) were 0.90 and 0.80.

CONCLUSION

Liver surface nodularity assessment at MDCT allows for accurate discrimination of intermediate stages of hepatic fibrosis in a cause-specific cohort of patients with HCV, particularly at more advanced levels.

CT texture analysis of the liver for assessing hepatic fibrosis in patients with hepatitis C virus

OBJECTIVE

To evaluate CT texture analysis (CTTA) for non-invasively staging of hepatic fibrosis (stages F0-F4) in a cohort of patients with hepatitis C virus (HCV).

METHODS

Quantitative texture analysis of the liver was performed on abdominal multidimensional CT scans. Single slice region of interest measurements of the total liver, Couinaud segments IV-VIII and segments I-III were made. CT texture parameters were tested against stage of hepatic fibrosis in segments IV-VIII on the portal venous phase. Texture parameters were correlated with biopsy performed within 1 year for all cases with intermediate fibrosis (F0-F3).

RESULTS

CT scans of 556 adults (360 males, 196 females; mean age, 49.8 years), including a healthy control group (F0, n = 77) and patients with hepatitis C virus and Stage 0 disease (n = 49), and patients with increasing stages of fibrosis (F1, n = 80; F2 n = 99; F3 n = 87; F4 n = 164) were evaluated. Mean gray level intensity increased with increasing fibrosis. For significant fibrosis (≥F2), mean showed receiver operatingcharacteristic area under the curve (AUC) of 0.80 with sensitivity and specificity of 74 and 75% using a threshold of 0.44, with similar receiver operatingcharacteristic AUC and sensitivity/specificity for advanced fibrosis (≥F3). Skewness and kurtosis were inversely associated with hepatic fibrosis, most prominently in cirrhotic patients. A multivariate model combining these four texture features (mean, mpp, skewness and kurtosis) showed slightly improved performance with AUC of 0.82, 0.82 and 0.86 for any fibrosis (F0 vs F1-F4), significant fibrosis (F0-1 vs F2-4) and advanced fibrosis (F0-2 vs F3-4) respectively.

CONCLUSION

CT texture features may be associated with hepatic fibrosis and have utility in staging fibrosis, particularly at advanced levels.

ADVANCES IN KNOWLEDGE

CTTA may be helpful in detecting and staging hepatic fibrosis, particularly at advanced levels. CT measures like CTTA can be retrospectively evaluated without special equipment.

Multidetector Computed Tomography for Retrospective, Noninvasive Staging of Liver Fibrosis

Although not traditionally used to assess hepatic fibrosis, computed tomography (CT) is fast, accessible, robust, and commonly used for abdominal indications. CT metrics are often easily retrospectively obtained without special equipment. Metrics such as liver segmental volume ratio, which quantifies regional hepatic volume changes; splenic volume; and liver surface nodularity scoring show diagnostic performance comparable to elastography techniques for detecting significant and advanced fibrosis. Other emerging CT tools, such as CT texture analysis and fractional extracellular volume, have also shown promise in identifying fibrosis and warrant further study.

Texture analysis of the liver at MDCT for assessing hepatic fibrosis

PURPOSE

To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0-F4) METHODS: Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using commercially available software (TexRAD), which uses a filtration-histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I-III were obtained. CTTA parameters were correlated against fibrosis stage (F0-F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1-F3).

RESULTS

The study cohort consisted of 289 adults (158M/131W; mean age, 51 years), including healthy controls (F0, n = 77), and patients with increasing stages of fibrosis (F1, n = 42; F2 n = 37; F3 n = 53; F4 n = 80). Mean gray-level intensity increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cutoff 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71-0.73 across medium- and coarse- filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87, respectively, at coarse-filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3.

CONCLUSION

CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.

Hepatosplenic volumetric assessment at MDCT for staging liver fibrosis

PURPOSE

To investigate hepatosplenic volumetry at MDCT for non-invasive prediction of hepatic fibrosis.

METHODS

Hepatosplenic volume analysis in 624 patients (mean age, 48.8 years; 311 M/313 F) at MDCT was performed using dedicated software and compared against pathological fibrosis stage (F0 = 374; F1 = 48; F2 = 40; F3 = 65; F4 = 97). The liver segmental volume ratio (LSVR) was defined by Couinaud segments I-III over segments IV-VIII. All pre-cirrhotic fibrosis stages (METAVIR F1-F3) were based on liver biopsy within 1 year of MDCT.

RESULTS

LSVR and total splenic volumes increased with stage of fibrosis, with mean(±SD) values of: F0: 0.26 ± 0.06 and 215.1 ± 88.5 mm³; F1: 0.25 ± 0.08 and 294.8 ± 153.4 mm³; F2: 0.331 ± 0.12 and 291.6 ± 197.1 mm³; F3: 0.39 ± 0.15 and 509.6 ± 402.6 mm³; F4: 0.56 ± 0.30 and 790.7 ± 450.3 mm³, respectively. Total hepatic volumes showed poor discrimination (F0: 1674 ± 320 mm³; F4: 1631 ± 691 mm³). For discriminating advanced fibrosis (≥F3), the ROC AUC values for LSVR, total liver volume, splenic volume and LSVR/spleen combined were 0.863, 0.506, 0.890 and 0.947, respectively.

CONCLUSION

Relative changes in segmental liver volumes and total splenic volume allow for non-invasive staging of hepatic fibrosis, whereas total liver volume is a poor predictor. Unlike liver biopsy or elastography, these CT volumetric biomarkers can be obtained retrospectively on routine scans obtained for other indications.

KEY POINTS

• Regional changes in hepatic volume (LSVR) correlate well with degree of fibrosis. • Total liver volume is a very poor predictor of underlying fibrosis. • Total splenic volume is associated with the degree of hepatic fibrosis. • Hepatosplenic volume assessment is comparable to elastography for staging fibrosis. • Unlike elastography, volumetric analysis can be performed retrospectively.

Quantification of hepatic and visceral fat by CT and MR imaging: relevance to the obesity epidemic, metabolic syndrome and NAFLD

Trends in obesity have continued to increase in the developed world over the past few decades, along with related conditions such as metabolic syndrome, which is strongly associated with this epidemic. Novel and innovative methods to assess relevant obesity-related biomarkers are needed to determine the clinical significance, allow for surveillance and intervene if appropriate. Aggregations of specific types of fat, specifically hepatic and visceral adiposity, are now known to be correlated with these conditions, and there are a variety of imaging techniques to identify and quantify their distributions and provide diagnostic information. These methods are particularly salient for metabolic syndrome, which is related to both hepatic and visceral adiposity but currently not defined by it. Simpler non-specific fat measurements, such as body weight, abdominal circumference and body mass index are more frequently used but lack the ability to characterize fat location. In addition, non-alcoholic fatty liver disease (NAFLD) is a related condition that carries relevance not only for obesity-related diseases but also for the progression of the liver-specific disease, including non-alcoholic steatohepatitis and cirrhosis, albeit at a much lower frequency. Recent CT and MRI techniques have emerged to potentially optimize diagnosing metabolic syndrome and NAFLD through non-invasive quantification of visceral fat and hepatic steatosis with high accuracy. These imaging modalities should aid us in further understanding the relationship of hepatic and visceral fat to the obesity-related conditions such as metabolic syndrome, NAFLD and cardiovascular disease.