Hepatosplenic volumetric assessment at MDCT for staging liver fibrosis

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.

Post-contrast CT liver attenuation alone is superior to the liver-spleen difference for identifying moderate hepatic steatosis

OBJECTIVE

To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values.

MATERIALS AND METHODS

A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis.

RESULTS

The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001).

CONCLUSION

Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less.

CLINICAL RELEVANCE STATEMENT

Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection.

KEY POINTS

The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone.

Computed Tomography-Derived Extracellular Volume Fraction and Splenic Size for Liver Fibrosis Staging

OBJECTIVE

Extracellular volume fraction (fECV) and liver and spleen size have been correlated with liver fibrosis stages and cirrhosis. The purpose of the current study was to determine the predictive value of fECV alone and in conjunction with measurement of liver and spleen size for severity of liver fibrosis.

METHODS

This was a retrospective study of 95 subjects (65 with liver biopsy and 30 controls). Spearman rank correlation coefficient was used to assess correlation between radiological markers and fibrosis stage. Receiver operating characteristic analysis was performed to assess the discriminative ability of radiological markers for significant (F2+) and advanced (F3+) fibrosis and cirrhosis (F4), by reporting the area under the curve (AUC).

RESULTS

The cohort had a mean age of 51.4 ± 14.4 years, and 52 were female (55%). There were 36, 5, 6, 9, and 39 in fibrosis stages F0, F1, F2, F3, and F4, respectively. Spleen volume alone showed the highest correlation (r = 0.552, P < 0.001) and AUCs of 0.823, 0.807, and 0.785 for identification of significant and advanced fibrosis and cirrhosis, respectively. Adding fECV to spleen length improved AUCs (0.764, 0.745, and 0.717 to 0.812, 0.781, and 0.738, respectively) compared with splenic length alone. However, adding fECV to spleen volume did not improve the AUCs for significant or advanced fibrosis or cirrhosis.

CONCLUSIONS

Spleen size (measured in length or volume) showed better correlation with liver fibrosis stages compared with fECV. The combination of fECV and spleen length had higher accuracy compared with fECV alone or spleen length alone.

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.

Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly

BACKGROUND. Splenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. OBJECTIVE. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds. METHODS. This retrospective study included a primary (screening) sample of 8901 patients (4235 men, 4666 women; mean age, 56 ± 10 [SD] years) who underwent CT colonoscopy (n = 7736) or renal donor CT (n = 1165) from April 2004 to January 2017 and a secondary sample of 104 patients (62 men, 42 women; mean age, 56 ± 8 years) with end-stage liver disease who underwent contrast-enhanced CT performed as part of evaluation for potential liver transplant from January 2011 to May 2013. The automated deep learning AI tool was used for spleen segmentation, to determine splenic volumes. Two radiologists independently reviewed a subset of segmentations. Weight-based volume thresholds for splenomegaly were derived using regression analysis. Performance of linear measurements was assessed. Frequency of splenomegaly in the secondary sample was determined using weight-based volumetric thresholds. RESULTS. In the primary sample, both observers confirmed splenectomy in 20 patients with an automated splenic volume of 0 mL; confirmed incomplete splenic coverage in 28 patients with a tool output error; and confirmed adequate segmentation in 21 patients with low volume (< 50 mL), 49 patients with high volume (> 600 mL), and 200 additional randomly selected patients. In 8853 patients included in analysis of splenic volumes (i.e., excluding a value of 0 mL or error values), the mean automated splenic volume was 216 ± 100 [SD] mL. The weight-based volumetric threshold (expressed in milliliters) for splenomegaly was calculated as (3.01 × weight [expressed as kilograms]) + 127; for weight greater than 125 kg, the splenomegaly threshold was constant (503 mL). Sensitivity and specificity for volume-defined splenomegaly were 13% and 100%, respectively, at a true craniocaudal length of 13 cm, and 78% and 88% for a maximum 3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. The mean automated splenic volume in the 103 remaining patients was 796 ± 457 mL; 84% (87/103) of patients met the weight-based volume-defined splenomegaly threshold. CONCLUSION. We derived a weight-based volumetric threshold for splenomegaly using an automated AI-based tool. CLINICAL IMPACT. The AI tool could facilitate large-scale opportunistic screening for splenomegaly.

Comparison between Intravoxel Incoherent Motion and Splenic Volumetry to Predict Hepatic Fibrosis Staging in Preoperative Patients

Intravoxel incoherent motion (IVIM) and splenic volumetry (SV) for hepatic fibrosis (HF) prediction have been reported to be effective. Our purpose is to compare the HF prediction of IVIM and SV in 67 patients with pathologically staged HF. SV was divided by body surface area (BSA). IVIM indices, such as slow diffusion-coefficient related to molecular diffusion (D), fast diffusion-coefficient related to perfusion in microvessels (D*), apparent diffusion-coefficient (ADC), and perfusion related diffusion-fraction (f), were calculated by two observers (R1/R2). D (p = 0.718 for R1, p = 0.087 for R2) and D* (p = 0.513, p = 0.708, respectively) showed a poor correlation with HF. ADC (p = 0.034, p = 0.528, respectively) and f (p < 0.001, p = 0.007, respectively) decreased as HF progressed, whereas SV/BSA increased (p = 0.015 for R1). The AUCs of SV/BSA (0.649-0.698 for R1) were higher than those of f (0.575-0.683 for R1 + R2) for severe HF (≥F3-4 and ≥F4), although AUCs of f (0.705-0.790 for R1 + R2) were higher than those of SV/BSA (0.628 for R1) for mild or no HF (≤F0-1). No significant differences to identify HF were observed between IVIM and SV/BSA. SV/BSA allows a higher estimation for evaluating severe HF than IVIM. IVIM is more suitable than SV/BSA for the assessment of mild or no HF.

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.

Diagnosis of HDV: From virology to non-invasive markers of fibrosis

Hepatitis D viral infection in humans is a disease that requires the establishment of hepatitis B, relying on hepatitis B surface Ag and host cellular machinery to replicate and propagate the infection. Since its discovery in 1977, substantial progress has been made to better understand the hepatitis D viral life cycle, pathogenesis and modes of transmission along with expanding on clinical knowledge related to prevention, diagnosis, monitoring and treatment. The availability of serologic diagnostic assays for hepatitis D infection has evolved over time with current widespread availability, improved detection and standardized reporting. With human migration, the epidemiology of hepatitis D infection has changed over time. Thus, the ability to use diagnostic assays remains essential to monitor the global impact of hepatitis D infection. Separately, while liver biopsy remains the gold standard for the staging of this rapidly progressive and severe form of chronic viral hepatitis, there is an unmet need for clinical monitoring of chronic hepatitis D infection for management of progressive disease. Thus, exploration of the utility of non-invasive fibrosis markers in hepatitis D is ongoing. In this review, we discuss the virology, the evolution of diagnostics and the development of non-invasive markers for the detection and monitoring of fibrosis in patients with hepatitis D infection.

Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging

Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.

Noninvasive Fibrosis Testing in Chronic Liver Disease Including Caveats

Assessment of liver fibrosis is important as the range of liver disease management has expanded, rendering biopsy both imperfect and impractical in many situations. Noninvasive tests of fibrosis leverage laboratory, imaging and elastography techniques to estimate disease extent, often with the goal of identifying advanced fibrosis. This review attempts to summarize their utility across a broad range of possible clinical scenarios while considering the central tenets of health care quality: access, quality, and cost. For each test, it also discusses the caveats whereby each test may have reduced effectiveness and how to consider each in a typical clinical setting.

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.

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.

Non-invasive precise staging of liver fibrosis using deep residual network model based on plain CT images

PURPOSE

The aim of this study was to explore the application of five-class deep residual network models based on plain CT images and clinical features for the precise staging of liver fibrosis.

METHODS

This retrospective clinical study included 347 patients who underwent liver CT, with pathological staging of liver fibrosis as the gold standard. We established three ResNet models to stage liver fibrosis. The output diagnosis labels of models were 0, 1, 2, 3 and 4, which correspond to F0, F1, F2, F3, and F4 stages. Confusion matrices were used to evaluate the performances of models to precisely stage liver fibrosis. The performance for diagnosing cirrhosis (F4), advanced fibrosis (≥ F3) and significant fibrosis (≥ F2) of models was evaluated with receiver operating characteristic (ROC) analyses.

RESULTS

The kappa coefficients of the five-class ResNet model (based on plain CT images), the five-class ResNet clinical model (based on clinical features), and the five-class mixed ResNet model (based on plain CT images and clinical features) for precise staging liver fibrosis were 0.566, 0.306, and 0.63, respectively. The recall rates and precision rates for F0, F1, F2, and F3 of three models were lower than 60%. The ROC AUC values of the five-class ResNet model, the five-class ResNet clinical model, and the five-class mixed ResNet model for diagnosing cirrhosis, advanced fibrosis, and significant fibrosis were 0.95, 0.88, and 0.82, 0.80, 0.72, and 0.70, 0.95, 0.90, and 0.83, respectively.

CONCLUSIONS

The five-class ResNet models are of high value in the diagnosis of liver cirrhosis, advanced liver fibrosis, and significant liver fibrosis. However, for the precise staging of liver fibrosis, the models cannot accurately distinguish other liver fibrosis stages except F4. Plain CT images combined with clinical features have the potential to improve the performance of the ResNet models in diagnosing liver fibrosis.

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.

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.

Pre-operative CT scan helps predict outcome after liver transplantation for acute-on-chronic grade 3 liver failure

OBJECTIVES

The aim of this study was to identify the prognostic value of pre-operative imaging to predict post-transplantation survival in critically ill cirrhotic patients with severe acute-on-chronic liver failure (ACLF).

METHODS

Patients with grade 3 ACLF who underwent liver transplantation between January 2010 and January 2020 and with available contrast-enhanced abdominal computed tomography (CT) performed less than 3 months before LT were retrospectively included (n = 82). Primary endpoint was 1-year mortality. Imaging parameters (sarcopenia, liver morphology and volumetry, and signs of portal hypertension) were screened and tested to build a prognostic score.

RESULTS

In the multivariate analysis, three independent CT-derived prognostic factors were found: splenomegaly (p = 0.021; HR = 5.6 (1.29-24.1)), liver atrophy (p = 0.05; HR = 2.93 (1.01-10.64)), and vena cava diameter ratio (p < 0.0001; HR = 12.7 (3.4-92)). A simple prognostic score was proposed, based on the presence of splenomegaly (5 points), liver atrophy (5 points), and vena cava diameter ratio < 0.2 (12 points). A cutoff at 10 points distinguished a high-risk group (score > 10) from a low-risk group (score ≤ 10) with 1-year survival of 27% vs. 67% respectively (p < 0.001). It was found to be an independent predictive factor in association with the Transplantation for ACLF3 Model (TAM) score.

CONCLUSION

Pre-transplantation contrast-enhanced abdominal CT has a significant impact on selection of patients in ACLF3 in order to predict 1-year survival after LT.

KEY POINTS

• Splenomegaly, liver atrophy, and vena cava diameter ratio are independent CT-derived prognostic factors after transplantation for severe acute-on-chronic liver failure. • A simple CT-based prognostic score is an independent predictive factor, complementary to clinical and biological parameters. • The use of the CT-derived score allows stratification based on 1-year mortality for patients with otherwise uncertain prognosis with clinical and biological parameters alone.

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.

DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults

BACKGROUND

Although MR elastography allows for quantitative evaluation of liver stiffness to assess chronic liver diseases, it has associated drawbacks related to additional scanning time, patient discomfort, and added costs.

OBJECTIVE

To develop a machine learning model that can categorically classify the severity of liver stiffness using both anatomical T2-weighted MRI and clinical data for children and young adults with known or suspected pediatric chronic liver diseases.

MATERIALS AND METHODS

We included 273 subjects with known or suspected chronic liver disease. We extracted data including axial T2-weighted fast spin-echo fat-suppressed images, clinical data (e.g., demographic/anthropomorphic data, particular medical diagnoses, laboratory values) and MR elastography liver stiffness measurements. We propose DeepLiverNet (a deep transfer learning model) to classify patients into one of two groups: no/mild liver stiffening (<3 kPa) or moderate/severe liver stiffening (≥3 kPa). We conducted internal cross-validation using 178 subjects, and external validation using an independent cohort of 95 subjects. We assessed diagnostic performance using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AuROC).

RESULTS

In the internal cross-validation experiment, the combination of clinical and imaging data produced the best performance (AuROC=0.86) compared to clinical (AuROC=0.83) or imaging (AuROC=0.80) data alone. Using both clinical and imaging data, the DeepLiverNet correctly classified patients with accuracy of 88.0%, sensitivity of 74.3% and specificity of 94.6%. In our external validation experiment, this same deep learning model achieved an accuracy of 80.0%, sensitivity of 61.1%, specificity of 91.5% and AuROC of 0.79.

CONCLUSION

A deep learning model that incorporates clinical data and anatomical T2-weighted MR images might provide a means of risk-stratifying liver stiffness and directing the use of MR elastography.

Safe standard remnant liver volume after hepatectomy in HCC patients in different stages of hepatic fibrosis

Background

To determine the standard remnant liver volume (SRLV) threshold to avoid postoperative hepatic insufficiency inpatients in different stages of hepatic fibrosis who undergo right hemi-hepatectomy.

Methods

Data for 85 patients at our single medical center were analysed prospectively to examine whether the following factors differed significantly between those who experienced postoperative hepatic insufficiency and those who did not: height, prothrombin time, remnant liver volume, SRLV or hepatic fibrosis stage.

Results

Logistic regression showed SRLV and hepatic fibrosis stage to be independent risk factors for postoperative hepatic insufficiency. The threshold SRLV for predicting insufficiency was 203.2 ml/m² across all patients [area under receiver operating characteristic curve (AUC) 0.778, sensitivity 66.67%, specificity 83.64%, p<0.0001), 193.8 ml/m² for patients with severe hepatic fibrosis (AUC 0.938, sensitivity 91.30%, specificity 85.71%, p<0.0001), and 224.3 ml/m² for patients with cirrhosis (AUC 0.888, sensitivity 100%, specificity 64.29%, p<0.0001).

Conclusions

Right hemi-hepatectomy may be safer in Chinese patients when the standard remnant liver volume is more than 203.2 ml/m² in the absence of hepatic fibrosis or cirrhosis, 193.8 ml/m² in the presence of severe hepatic fibrosis or 224.3 ml/m² in the presence of cirrhosis.

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.

Impact of TIPS on Splenic Volume and Thrombocytopenia

OBJECTIVE. Splenomegaly and thrombocytopenia are common complications in patients with cirrhosis. The present study aimed to evaluate changes in splenic volumes and platelet counts after TIPS insertion. MATERIALS AND METHODS. A total of 104 patients who had a diagnosis of portal hypertension and had undergone TIPS placement between November 2015 and August 2019 were enrolled in this retrospective cohort study. We retrospectively calculated splenic volumes before TIPS placement and at 1-2 and 6-12 months after TIPS placement and monitored the platelet count at 1, 3, 6, and 12 months after TIPS placement. RESULTS. The mean (± SD) portal pressure gradient before TIPS placement was 28.3 ± 4.6 mm Hg; after TIPS placement, it was 11.3 ± 4.5 mm Hg (p < .001). The mean splenic volume of all 104 patients before TIPS placement was 868 ± 409 cm³, and at 1-2 months after TIPS placement, it was 710 ± 336 cm³ (p < .001). Among the 43 patients for whom splenic volume data were available at both 1-2 and 6-12 months after TIPS placement, the mean splenic volume decreased from 845 ± 342 cm³ to 691 ± 301 cm³ and then to 674 ± 333 cm³, respectively. Correspondingly, the number of patients with severe thrombocytopenia decreased from 25 patients (35.7%) before the TIPS procedure to 16 patients (22.9%) in the 1-2 months after TIPS placement and then to 11 patients (15.7%) in the 6-12 months after TIPS implantation. The increase in the platelet count was significantly correlated with decreasing splenic volume (r² = 0.3735; p < .001). CONCLUSION. In most patients, TIPS placement resulted in a significant decrease in splenic volume and a significant increase in the platelet count during the same period.

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.

Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard

BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.

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.

Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment

Background Nonalcoholic fatty liver disease and its consequences are a growing public health concern requiring cross-sectional imaging for noninvasive diagnosis and quantification of liver fat. Purpose To investigate a deep learning-based automated liver fat quantification tool at nonenhanced CT for establishing the prevalence of steatosis in a large screening cohort. Materials and Methods In this retrospective study, a fully automated liver segmentation algorithm was applied to noncontrast abdominal CT examinations from consecutive asymptomatic adults by using three-dimensional convolutional neural networks, including a subcohort with follow-up scans. Automated volume-based liver attenuation was analyzed, including conversion to CT fat fraction, and compared with manual measurement in a large subset of scans. Results A total of 11 669 CT scans in 9552 adults (mean age ± standard deviation, 57.2 years ± 7.9; 5314 women and 4238 men; median body mass index [BMI], 27.8 kg/m²) were evaluated, including 2117 follow-up scans in 1862 adults (mean age, 59.2 years; 971 women and 891 men; mean interval, 5.5 years). Algorithm failure occurred in seven scans. Mean CT liver attenuation was 55 HU ± 10, corresponding to CT fat fraction of 6.4% (slightly fattier in men than in women [7.4% ± 6.0 vs 5.8% ± 5.7%; P < .001]). Mean liver Hounsfield unit varied little by age (<4 HU difference among all age groups) and only weak correlation was seen with BMI (r² = 0.14). By category, 47.9% (5584 of 11 669) had negligible or no liver fat (CT fat fraction <5%), 42.4% (4948 of 11 669) had mild steatosis (CT fat fraction of 5%-14%), 8.8% (1025 of 11 669) had moderate steatosis (CT fat fraction of 14%-28%), and 1% (112 of 11 669) had severe steatosis (CT fat fraction >28%). Excellent agreement was seen between automated and manual measurements, with a mean difference of 2.7 HU (median, 3 HU) and r² of 0.92. Among the subcohort with longitudinal follow-up, mean change was only -3 HU ± 9, but 43.3% (806 of 1861) of patients changed steatosis category between first and last scans. Conclusion This fully automated CT-based liver fat quantification tool allows for population-based assessment of hepatic steatosis and nonalcoholic fatty liver disease, with objective data that match well with manual measurement. The prevalence of at least mild steatosis was greater than 50% in this asymptomatic screening cohort. © RSNA, 2019.

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.

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.

CT predicts liver fibrosis: Prospective evaluation of morphology- and attenuation-based quantitative scores in routine portal venous abdominal scans

Objectives

Our aim was to prospectively determine whether quantitative computed tomography (CT) scores, consisting of simplified indices for liver remodeling and attenuation, may predict liver fibrosis in abdominal CT scans.

Materials and methods

This cross-sectional, prospective study was approved by the local IRB (Kantonale Ethikkommission Bern). Written informed consent was given from all patients undergoing study-MR exams. Between 02/16 and 05/17, four different liver fibrosis scores (CRL-R = caudate-right-lobe ratio, LIMV-, LIMA- and LIMVA-fibrosis score, with “LIM” for liver imaging morphology, “V” for liver vein diameter and “A” for attenuation) were calculated in 1534 consecutive abdominal CT scans, excluding patients with prior liver surgery and liver metastasis. Patients were invited to undergo magnetic resonance (MR) elastography as the non-invasive gold standard to evaluate liver fibrosis. MR elastography shear modulus ≥2.8 kPa was defined as beginning liver fibrosis, while ≥3.5 kPa was defined as significant liver fibrosis (which would correspond to fibrosis stage F2 or higher in histology). Cutoff values, sensitivities and specificities obtained from the receiver operating characteristics (ROC) analysis were then calculated in 141 patients who followed the invitation for MR elastography. To mitigate selection bias, prevalence was estimated in the screened total population (n = 1534) by applying the cutoff values with sensitivities and specificities calculated in the MR elastography sub-group. Positive predictive values (PPV) and negative predictive values (NPV) were then calculated.

Results

Fibrosis scores including liver vein attenuation LIMA-FS and LIMVA-FS showed higher areas under the ROC curves (0.96–0.97) than CRL-R (0.82) to detect significant liver fibrosis, while LIMV-FS showed good performance as well (0.92). The prevalence-corrected PPV were 29% for CRL-R, 70% for LIMV-FS, 76% for LIMA-FS and 82% for LIMVA-FS.

Conclusion

CT fibrosis scores, notably LIMA-FS and LIMVA-FS, may predict significant liver fibrosis on routine abdominal CT scans.

Deep learning for staging liver fibrosis on CT: a pilot study

OBJECTIVES

To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.

METHODS

This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (F_DLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance.

RESULTS

The F_DLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using F_DLCT scores were 0.74 (0.64-0.85), 0.76 (0.66-0.85) and 0.73 (0.62-0.84), respectively.

CONCLUSIONS

Liver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance.

KEY POINTS

• Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance. • Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging. • Further improvement are necessary before utilisation in clinical settings.

CT perfusion imaging of the liver and the spleen in patients with cirrhosis: Is there a correlation between perfusion and portal venous hypertension?

OBJECTIVES

To correlate hepatic and splenic CT perfusion parameters with hepatic venous pressure gradient (HVPG) measurements in patients with cirrhosis.

METHODS

Twenty-one patients with cirrhosis (males, 17; females, 4; mean ± SD age, 57 ± 7 years) underwent hepatic and splenic perfusion CT on a 320-detector row volume scanner as well as invasive measurement of HVPG. Different CT perfusion algorithms (maximum slope analysis and Patlak plot) were used to measure hepatic arterial flow (HAF), portal venous flow (PVF), hepatic perfusion index (HPI), splenic arterial flow (SAF), splenic blood volume (SBV) and splenic clearance (SCL). Hepatic and splenic perfusion parameters were correlated with HVPG, and sensitivity and specificity for detection of severe portal hypertension (≥12 mmHg) were calculated.

RESULTS

The Spearman correlation coefficient was -0.53 (p < 0.05) between SAF and HVPG, and -0.68 (p < 0.01) between HVPG and SCL. Using a cut-off value of 125 ml/min/100 ml for SCL, sensitivity for detection of a HVPG of ≥12 mmHg was 94%, and specificity 100%. There was no significant correlation between hepatic perfusion parameters and HVPG.

CONCLUSION

CT perfusion in patients with cirrhosis showed a strong correlation between SCL and HVPG and may be used for detection of severe portal hypertension.

KEY POINTS

• SAF and SCL are statistically significantly correlated with HVPG • SCL showed stronger correlation with HVPG than SAF • 125 ml/min/100 ml SCL-cut-off yielded 94 % sensitivity, 100 % specificity for severe PH • HAF, PVF and HPI showed no statistically significant correlation with HVPG.