Optical analysis of computed tomography images of the liver predicts fibrosis stage and distribution in chronic hepatitis C

Radiology of fibrosis part II: abdominal organs

Fibrosis is the aberrant process of connective tissue deposition from abnormal tissue repair in response to sustained tissue injury caused by hypoxia, infection, or physical damage. It can affect almost all organs in the body causing dysfunction and ultimate organ failure. Tissue fibrosis also plays a vital role in carcinogenesis and cancer progression. The early and accurate diagnosis of organ fibrosis along with adequate surveillance are helpful to implement early disease-modifying interventions, important to reduce mortality and improve quality of life. While extensive research has already been carried out on the topic, a thorough understanding of how this relationship reveals itself using modern imaging techniques has yet to be established. This work outlines the ways in which fibrosis shows up in abdominal organs and has listed the most relevant imaging technologies employed for its detection. New imaging technologies and developments are discussed along with their promising applications in the early detection of organ fibrosis.

An easy-to-use AIHF-nomogram to predict advanced liver fibrosis in patients with autoimmune hepatitis

Background

The evaluation of liver fibrosis is essential in the management of patients with autoimmune hepatitis (AIH). We aimed to establish and validate an easy-to-use nomogram to identify AIH patients with advanced liver fibrosis.

Methods

AIH patients who underwent liver biopsies were included and randomly divided into a training set and a validation set. The least absolute shrinkage and selection operator (LASSO) regression was used to select independent predictors of advanced liver fibrosis from the training set, which were utilized to establish a nomogram. The performance of the nomogram was evaluated using the receiver characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

The median age of 235 patients with AIH was 54 years old, with 83.0% of them being female. Six independent factors associated with advanced fibrosis, including sex, age, red cell distribution width, platelets, alkaline phosphatase, and prothrombin time, were combined to construct a predictive AIH fibrosis (AIHF)-nomogram. The AIHF-nomogram showed good agreement with real observations in the training and validation sets, according to the calibration curve. The AIHF-nomogram performed significantly better than the fibrosis-4 and aminotransferase-to-platelet ratio scores in the training and validation sets, with an area under the ROCs for predicting advanced fibrosis of 0.804 in the training set and 0.781 in the validation set. DCA indicated that the AIHFI-nomogram was clinically useful. The nomogram will be available at http://ndth-zzy.shinyapps.io/AIHF-nomogram/as a web-based calculator.

Conclusions

The novel, easy-to-use web-based AIHF-nomogram model provides an insightful and applicable tool to identify AIH patients with advanced liver fibrosis.

Autoimmune Hepatitis and Fibrosis

Autoimmune hepatitis (AIH) is a chronic immune-inflammatory disease of the liver, generally considered a rare condition. The clinical manifestation is extremely varied and can range from paucisymptomatic forms to severe hepatitis. Chronic liver damage causes activation of hepatic and inflammatory cells leading to inflammation and oxidative stress through the production of mediators. This results in increased collagen production and extracellular matrix deposition leading to fibrosis and even cirrhosis. The gold standard for the diagnosis of fibrosis is liver biopsy; however, there are serum biomarkers, scoring systems, and radiological methods useful for diagnosis and staging. The goal of AIH treatment is to suppress fibrotic and inflammatory activities in the liver to prevent disease progression and achieve complete remission. Therapy involves the use of classic steroidal anti-inflammatory drugs and immunosuppressants, but in recent years scientific research has focused on several new alternative drugs for AIH that will be discussed in the review.

Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker

BACKGROUND

To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images.

MATERIALS AND METHODS

This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men).

RESULTS

In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70-87%, specificity range of 71-97%, and accuracy range of 82-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis.

CONCLUSION

Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.

Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model

Objectives: Preoperative prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) is significant for developing appropriate treatment strategies. We aimed to establish a radiomics-based clinical model for preoperative prediction of PHLF in HCC patients using gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI).

Methods: A total of 144 HCC patients from two medical centers were included, with 111 patients as the training cohort and 33 patients as the test cohort, respectively. Radiomics features and clinical variables were selected to construct a radiomics model and a clinical model, respectively. A combined logistic regression model, the liver failure (LF) model that incorporated the developed radiomics signature and clinical risk factors was then constructed. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) with 95% confidence interval (CI).

Results: The radiomics model showed a higher AUC than the clinical model in the training cohort and the test cohort for predicting PHLF in HCC patients. Moreover, the LF model had the highest AUCs in both cohorts [0.956 (95% CI: 0.955–0.962) and 0.844 (95% CI: 0.833–0.886), respectively], compared with the radiomics model and the clinical model.

Conclusions: We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences

Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and death. Ultrasound (US) shear wave elastography (SWE) is a relatively new, popular, non-invasive technique among radiologists. Although many studies have been published validating the SWE technique either in a clinical setting, or by applying machine learning on SWE elastograms, minimal work has been done on comparing the performance of popular pre-trained deep learning networks on CLD assessment. Currently available literature reports suggest technical advancements on specific deep learning structures, with specific inputs and usually on a limited CLD fibrosis stage class group, with limited comparison on competitive deep learning schemes fed with different input types. The aim of the present study is to compare some popular deep learning pre-trained networks using temporally stable and full elastograms, with or without augmentation as well as propose suitable deep learning schemes for CLD diagnosis and progress assessment. 200 liver biopsy validated patients with CLD, underwent US SWE examination. Four images from the same liver area were saved to extract elastograms and processed to exclude areas that were temporally unstable. Then, full and temporally stable masked elastograms for each patient were separately fed into GoogLeNet, AlexNet, VGG16, ResNet50 and DenseNet201 with and without augmentation. The networks were tested for differentiation of CLD stages in seven classification schemes over 30 repetitions using liver biopsy as the reference. All networks achieved maximum mean accuracies ranging from 87.2%-97.4% and area under the receiver operating characteristic curves (AUCs) ranging from 0.979-0.990 while the radiologists had AUCs ranging from 0.800-0.870. ResNet50 and DenseNet201 had better average performance than the other networks. The use of the temporal stability mask led to improved performance on about 50% of inputs and network combinations while augmentation led to lower performance for all networks. These findings can provide potential networks with higher accuracy and better setting in the CLD diagnosis and progress assessment. A larger data set would help identify the best network and settings for CLD assessment in clinical practice.

Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach

Background

To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT).

Methods

A total of 259 patients who underwent diagnostic abdominal CT (unenhanced, contrast-enhanced arterial, and venous phases) were included in this retrospective study. Child-Pugh scores were determined based on laboratory and clinical parameters. Linear regression (LR), Random Forest (RF), and convolutional neural network (CNN) algorithms were used to predict the Child-Pugh class. Their performances were compared to the prediction of experienced radiologists (ERs). Spearman correlation coefficients and accuracy were assessed for all predictive models. Additionally, a binary classification in low disease severity (Child-Pugh class A) and advanced disease severity (Child-Pugh class ≥ B) was performed.

Results

Eleven imaging features exhibited a significant correlation when adjusted for multiple comparisons with Child-Pugh class. Significant correlations between predicted and measured Child-Pugh classes were observed (ρ_LA = 0.35, ρ_RF = 0.32, ρ_CNN = 0.51, ρ_ERs = 0.60; p < 0.001). Significantly better accuracies for the prediction of Child-Pugh classes versus no-information rate were found for CNN and ERs (p ≤ 0.034), not for LR and RF (p ≥ 0.384). For binary severity classification, the area under the curve at receiver operating characteristic analysis was significantly lower (p ≤ 0.042) for LR (0.71) and RF (0.69) than for CNN (0.80) and ERs (0.76), without significant differences between CNN and ERs (p = 0.144).

Conclusions

The performance of a CNN in assessing Child-Pugh class based on multiphase abdominal CT images is comparable to that of ERs.

Comparison of Sound Touch Elastography, Shear Wave Elastography and Vibration-Controlled Transient Elastography in Chronic Liver Disease Assessment using Liver Biopsy as the "Reference Standard"

Chronic liver disease (CLD) is currently a major cause of death. Ultrasound elastography (USE) is an imaging method that has been developed for CLD assessment. Our aim in the study described here was to evaluate and compare a new commercial variant of USE, sound touch elastography (STE), with already established USE methods, shear wave elastography (SWE) and vibration-controlled transient elastography (VCTE), using liver biopsy as the "reference standard." For our study, 139 consecutive patients underwent standard liver STE, SWE and VCTE examinations with the corresponding ultrasound devices. A receiver operator characteristic (ROC) curve analysis was performed on the stiffness values measured with each method. ROC analysis revealed, for SWE, STE and VCTE, areas under the ROC curve of 0.9397, 0.9224 and 0.9348 for fibrosis stage (F), F ≥ F1; 0.9481, 0.9346 and 0.9415 for F ≥ F2; 0.9623, 0.9591 and 0.9631 for F ≥ F3; and 0.9581, 0.9541 and 0.9632 for F = F4, respectively. In conclusion, STE performs similarly to SWE and VCTE in CLD stage differentiation.

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment

PURPOSE

To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examination study and deep learning algorithm employing convolutional neural networks (CNNs).

MATERIALS AND METHODS

Two hundred SWE image sequences from 88 healthy individuals (F0 fibrosis stage) and 112 CLD patients (46 with mild fibrosis (F1), 16 with significant fibrosis (F2), 22 with severe fibrosis (F3), and 28 with cirrhosis (F4)) were analyzed to detect temporal stiffness stability between frames. An inverse Red, Green, Blue (RGB) colormap-to-stiffness process was performed for each image sequence, followed by a wavelet transform and fuzzy c-means clustering algorithm. This resulted in a binary mask depicting areas of high and low stiffness temporal stability. The mask was then applied to the first image of the SWE sequence, and the derived, masked SWE image was used to estimate its impact in standard clinical examination and CNN classification. Regarding the impact of the masked SWE image in clinical examination, one measurement by two radiologists was performed in each SWE image and two in the corresponding masked image measuring areas with high and low stiffness temporal stability. Then, stiffness stability parameters, interobserver variability evaluation and diagnostic performance by means of ROC analysis were assessed. The masked and unmasked sets of SWE images were fed into a CNN scheme for comparison.

RESULTS

The clinical impact evaluation study showed that the masked SWE images decreased the interobserver variability of the radiologists' measurements in the high stiffness temporal stability areas (interclass correlation coefficient (ICC) = 0.92) compared to the corresponding unmasked ones (ICC = 0.76). In terms of diagnostic accuracy, measurements in the high-stability areas of the masked SWE images (area-under-the-curve (AUC) ranging from 0.800 to 0.851) performed similarly to those in the unmasked SWE images (AUC ranging from 0.805 to 0.893). Regarding the measurements in the low stiffness temporal stability areas of the masked SWE images, results for interobserver variability (ICC = 0.63) and diagnostic accuracy (AUC ranging from 0.622 to 0.791) were poor. Regarding the CNN classification, the masked SWE images showed improved accuracy (ranging from 82.5% to 95.5%) compared to the unmasked ones (ranging from 79.5% to 93.2%) for various CLD stage combinations.

CONCLUSION

Our detection algorithm excludes unreliable areas in SWE images, reduces interobserver variability, and augments CNN's accuracy scores for many combinations of fibrosis stages.

Computed tomography texture analysis to facilitate therapeutic decision making in hepatocellular carcinoma

This study explored the potential of computed tomography (CT) textural feature analysis for the stratification of single large hepatocellular carcinomas (HCCs) > 5 cm, and the subsequent determination of patient suitability for liver resection (LR) or transcatheter arterial chemoembolization (TACE). Wavelet decomposition was performed on portal-phase CT images with three bandwidth responses (filter 0, 1.0, and 1.5). Nine textural features of each filter were extracted from regions of interest. Wavelet-2-H (filter 1.0) in LR and wavelet-2-V (filter 0 and 1.0) in TACE were related to survival. Subsequently, LR and TACE patients were divided based on the wavelet-2-H and wavelet-2-V median at filter 1.0 into two subgroups (+ or −). LR+ patients showed the best survival, followed by LR-, TACE+, and TACE-. We estimated that LR+ patients treated using TACE would exhibit a survival similar to TACE- patients and worse than TACE+ patients, with a severe compromise in overall survival. LR was recommended for TACE- patients, whereas TACE was preferred for LR- and TACE+ patients. Independent of tumor size, CT textural features showed positive and negative correlations with survival after LR and TACE, respectively. Although further validation is needed, texture analysis demonstrated the feasibility of using HCC patient stratification for determining the suitability of LR vs. TACE.

A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography

The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77-0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination.

Hepatic steatosis and fibrosis: Non-invasive assessment

Chronic liver disease is a major cause of morbidity and mortality worldwide and usually develops over many years, as a result of chronic inflammation and scarring, resulting in end-stage liver disease and its complications. The progression of disease is characterised by ongoing inflammation and consequent fibrosis, although hepatic steatosis is increasingly being recognised as an important pathological feature of disease, rather than being simply an innocent bystander. However, the current gold standard method of quantifying and staging liver disease, histological analysis by liver biopsy, has several limitations and can have associated morbidity and even mortality. Therefore, there is a clear need for safe and non-invasive assessment modalities to determine hepatic steatosis, inflammation and fibrosis. This review covers key mechanisms and the importance of fibrosis and steatosis in the progression of liver disease. We address non-invasive imaging and blood biomarker assessments that can be used as an alternative to information gained on liver biopsy.

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.

Diagnostic accuracy of intracellular uptake rates calculated using dynamic Gd-EOB-DTPA-enhanced MRI for hepatic fibrosis stage

PURPOSE

To assess the diagnostic accuracy of intracellular uptake rates (K_i ), and other quantitative pharmacokinetic (PK) parameters, for hepatic fibrosis stage; to compare this accuracy with a previously published semiquantitative metric, contrast enhancement index (CEI); and to assess variability of these parameters between liver regions.

MATERIALS AND METHODS

This was a case-control study design. Dynamic Gd-EOB-DTPA-enhanced 1.5T magnetic resonance imaging (MRI) was performed prospectively in 22 subjects with varying known stages of hepatic fibrosis. PK parameters and CEI were derived from the whole livers and from three fixed regions of interest (ROIs) in all subjects. Spearman rank correlation coefficients were computed to assess the relationship between fibrosis stages and each parameter. Receiver operating characteristic (ROC) curves were constructed to discriminate severe fibrosis (stages 3-4) from nonsevere fibrosis (stages 0-2). The coefficient of variation (CV) was calculated to assess variability in parameters between ROIs.

RESULTS

K_i and fibrosis stage were significantly correlated (R = -0.55, 95% confidence interval [CI] [-0.79, -0.14], P = 0.01). Area under ROC curve (AUC) in distinguishing severe from nonsevere fibrosis for K_i was 0.84 (95% CI [0.65,1.00]), and for CEI was 0.64 (95% CI [0.39, 0.89]) (P = 0.0248). CV for K_i and CEI were 33.4 and 5.8, respectively. The only other parameter in the PK model having significant correlation with fibrosis stage was absolute arterial blood flow (F_a ) (R = -0.48, 95% CI [-0.75,-0.05], P = 0.03).

CONCLUSION

Hepatocyte intracellular uptake rate, K_i , derived from dynamic contrast-enhanced MRI, correlates with fibrosis stage and may contribute to a noninvasive biomarker of hepatic fibrosis.

LEVEL OF EVIDENCE

2 J. Magn. Reson. Imaging 2017;45:1177-1185.

Imaging biomarkers for steatohepatitis and fibrosis detection in non-alcoholic fatty liver disease

There is a need, in NAFLD management, to develop non-invasive methods to detect steatohepatitis (NASH) and to predict advanced fibrosis stages. We evaluated a tool based on optical analysis of liver magnetic resonance images (MRI) as biomarkers for NASH and fibrosis detection by investigating patients with biopsy-proven NAFLD who underwent magnetic resonance (MR) protocols using 1.5T General Electric (GE) or Philips devices. Two imaging biomarkers (NASHMRI and FibroMRI) were developed, standardised and validated using area under the receiver operating characteristic curve (AUROC) analysis. The results indicated NASHMRI diagnostic accuracy for steatohepatitis detection was 0.83 (95% CI: 0.73–0.93) and FibroMRI diagnostic accuracy for significant fibrosis determination was 0.85 (95% CI: 0.77–0.94). These findings were independent of the MR system used. We conclude that optical analysis of MRI has high potential to define non-invasive imaging biomarkers for the detection of steatohepatitis (NASHMRI) and the prediction of significant fibrosis (FibroMRI) in NAFLD patients.

A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging

PURPOSE

Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system.

METHODS

The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system.

RESULTS

With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval.

CONCLUSIONS

The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.

Using texture analyses of contrast enhanced CT to assess hepatic fibrosis

PURPOSE

To determine the ability of texture analyses of contrast-enhanced CT images for distinguishing between varying degrees of hepatic fibrosis in patients with chronic liver disease using histopathology as the reference standard.

MATERIALS AND METHODS

Following IRB approval, 83 patients who underwent contrast enhanced 64-MDCT of the abdomen and pelvis in the portal venous phase between 12/2005 and 01/2013 and who had a liver biopsy within 6 months of the CT were included. An in-house developed, MATLAB-based texture analysis program was employed to extract 41 texture features from each of 5 axial segmented volumes of liver. Using the Ishak fibrosis staging scale, histopathologic grades of hepatic fibrosis were correlated with texture parameters after stratifying patients into three analysis groups, comparing Ishak scales 0-2 with 3-6, 0-3 with 4-6, and 0-4 with 5-6. To assess the utility of texture features, receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) was used to determine the performance of each feature in distinguishing between normal/low and higher grades of hepatic fibrosis.

RESULTS

A total of 19 different texture features with 7 histogram features, one grey level co-occurrence matrix, 6 gray level run length, 1 Laws feature, and 4 gray level gradient matrix demonstrated statistically significant differences for discriminating between fibrosis groupings. The highest AUC values fell in the range of fair performance for distinguishing between different fibrosis groupings.

CONCLUSION

These findings suggest that texture-based analyses of contrast-enhanced CT images offer a potential avenue toward the non-invasive assessment of liver fibrosis.

Non-invasive diagnosis of liver fibrosis and cirrhosis

The evaluation and follow up of liver fibrosis and cirrhosis have been traditionally performed by liver biopsy. However, during the last 20 years, it has become evident that this “gold-standard” is imperfect; even according to its proponents, it is only “the best” among available methods. Attempts at uncovering non-invasive diagnostic tools have yielded multiple scores, formulae, and imaging modalities. All are better tolerated, safer, more acceptable to the patient, and can be repeated essentially as often as required. Most are much less expensive than liver biopsy. Consequently, their use is growing, and in some countries the number of biopsies performed, at least for routine evaluation of hepatitis B and C, has declined sharply. However, the accuracy and diagnostic value of most, if not all, of these methods remains controversial. In this review for the practicing physician, we analyze established and novel biomarkers and physical techniques. We may be witnessing in recent years the beginning of the end of the first phase for the development of non-invasive markers. Early evidence suggests that they might be at least as good as liver biopsy. Novel experimental markers and imaging techniques could produce a dramatic change in diagnosis in the near future.

Texture analysis of preoperative CT images for prediction of postoperative hepatic insufficiency: a preliminary study

BACKGROUND

Texture analysis is a promising method of analyzing imaging data to potentially enhance diagnostic capability. This approach involves automated measurement of pixel intensity variation that may offer further insight into disease progression than do standard imaging techniques alone. We postulated that postoperative liver insufficiency, a major source of morbidity and mortality, correlates with preoperative heterogeneous parenchymal enhancement that can be quantified with texture analysis of cross-sectional imaging.

STUDY DESIGN

A retrospective case-matched study (waiver of informed consent and HIPAA authorization, approved by the Institutional Review Board) was performed comparing patients who underwent major hepatic resection and developed liver insufficiency (n = 12) with a matched group of patients with no postoperative liver insufficiency (n = 24) by procedure, remnant volume, and year of procedure. Texture analysis (with gray-level co-occurrence matrices) was used to quantify the heterogeneity of liver parenchyma on preoperative CT scans. Statistical significance was evaluated using Wilcoxon's signed rank and Pearson's chi-square tests.

RESULTS

No statistically significant differences were found between study groups for preoperative patient demographics and clinical characteristics, with the exception of sex (p < 0.05). Two texture features differed significantly between the groups: correlation (linear dependency of gray levels on neighboring pixels) and entropy (randomness of brightness variation) (p < 0.05).

CONCLUSIONS

In this preliminary study, the texture of liver parenchyma on preoperative CT was significantly more varied, less symmetric, and less homogeneous in patients with postoperative liver insufficiency. Therefore, texture analysis has the potential to provide an additional means of preoperative risk stratification.

Biomarkers of liver fibrosis

Liver fibrosis is the final common pathway for almost all causes of chronic liver injury. Liver fibrosis is now known to be a dynamic process having significant potential for resolution. Therefore, fibrosis prediction is an essential part of the assessment and management of patients with chronic liver disease. As such, there is strong demand for reliable liver biomarkers that provide insight into disease etiology, diagnosis, therapy, and prognosis in lieu of more invasive approaches such as liver biopsy. Current diagnostic strategies range from use of serum biomarkers to more advanced imaging techniques including transient elastography and magnetic resonance imaging. In addition to these modalities, there are other approaches including the use of novel, but yet to be validated, biomarkers. In this chapter, we discuss the biomarkers of liver fibrosis including the use of invasive and noninvasive biomarkers and disease-specific biomarkers in various chronic liver diseases.

Non-invasive assessment of liver fibrosis in patients with chronic hepatitis B

In patients with chronic hepatitis B (CHB), liver fibrosis assessment is essential not only for determining prognosis but also for identifying patients who should receive treatment. Liver biopsy is limited by its invasiveness and sampling error. To explore effective non-invasive methods for liver fibrosis assessment, we reviewed international literature published over the past decade that focused on patients with CHB. Biomarker panels such as API, FIB-4, Forns Index, HepaScore, FibroMeter, FibroTest, Zeng Index and Hui Index detect advanced fibrosis and cirrhosis with fairly satisfactory accuracy with area under the receiver-operating characteristics curve higher than 0.85. However, most panels and the suggested cutoffs have not been independently validated. Transient elastography is accurate in detecting advanced fibrosis and cirrhosis, and the relative cutoffs have been defined. False-positive results may, however, occur in cases of active necroinflammation and cholestasis. Other promising imaging methods such as acoustic radiation force impulse and magnetic resonance elastography still require further validating studies. We conclude that transient elastography, FibroTest and API are the most widely validated. Transient elastography has been validated as the most useful non-invasive method for liver fibrosis assessment. To improve non-invasive performance of detecting liver fibrosis, a combined application of transient elastography and biomarkers may be the preferred course of action.

A genome-wide association study of HCV-induced liver cirrhosis in the Japanese population identifies novel susceptibility loci at the MHC region

BACKGROUND & AIMS

We performed a genome-wide association study (GWAS) of hepatitis C virus (HCV)-induced liver cirrhosis (LC) to identify predictive biomarkers for the risk of LC in patients with chronic hepatitis C (CHC).

METHODS

A total of 682 HCV-induced LC cases and 1045 CHC patients of Japanese origin were genotyped by Illumina Human Hap 610-Quad bead Chip.

RESULTS

Eight SNPs which showed possible associations (p<1.0 × 10(-5)) at the GWAS stage were further genotyped using 936 LC cases and 3809 CHC patients. We found that two SNPs within the major histocompatibility complex (MHC) region on chromosome 6p21, rs910049 and rs3135363, were significantly associated with the progression from CHC to LC (pcombined=9.15 × 10(-11) and 1.45 × 10(-10), odds ratio (OR)=1.46 and 1.37, respectively). We also found that HLA-DQA1(*)0601 and HLA-DRB1(*)0405 were associated with the progression from CHC to LC (p=4.53 × 10(-4) and 1.54 × 10(-4) with OR=2.80 and 1.45, respectively). Multiple logistic regression analysis revealed that rs3135363, rs910049, and HLA-DQA1(*)0601 were independently associated with the risk of HCV-induced LC. In addition, individuals with four or more risk alleles for these three loci have a 2.83-fold higher risk for LC than those with no risk allele, indicating the cumulative effects of these variations.

CONCLUSIONS

Our findings elucidated the crucial roles of multiple genetic variations within the MHC region as prognostic/predictive biomarkers for CHC patients.

Intra-voxel incoherent motion MRI in rodent model of diethylnitrosamine-induced liver fibrosis

RATIONALE AND OBJECTIVES

To compare the apparent diffusion coefficient (ADC) and the perfusion fraction measured by intra-voxel incoherent motion (IVIM) Magnetic Resonance Imaging (MRI) with liver fibrosis degrees in a rodent model.

MATERIALS AND METHODS

All experiments received approval from our institutional animal care and use committee. Liver fibrosis was induced in 13 rats by oral gavage with diethylnitrosamine; 4 untreated rats with normal livers were used as controls. Diffusion Weighted MRI was performed and 8 gradient factors (0, 50, 100, 150, 200, 300, 400 and 500s/mm(2)) were acquired. The values of ADC, true diffusion coefficient D and perfusion fraction f were measured based on Li Bihan's method. The percentage of liver fibrosis was assessed via quantitative analysis of Masson trichrome staining using an average of 30 fields per section. The MRI measurements were compared to the histological fibrotic grade to evaluate the correlation between them.

RESULTS

ADC contained the contribution of diffusion and perfusion. The ADC and f values decreased significantly with the increasing fibrosis level (correlation coefficient: ADC: ρ=-0.781, p<0.001; f: ρ=-0.720, p=0.001); but D was poorly correlated with fibrosis level (ρ=-0.502, p=0.040).

CONCLUSION

The hepatic ADC and the perfusion fraction f were significantly correlated with the liver fibrosis level; however, D was not. This might suggest that hepatic perfusion is altered during the progression of hepatic fibrosis.

Can NASH be diagnosed, graded, and staged noninvasively?

Nonalcoholic bland steatosis and nonalcoholic steatohepatitis (NASH) are stages in the spectrum of nonalcoholic fatty liver disease (NAFLD). NASH may progress to end-stage liver disease. Liver biopsy distinguishes between patients with NASH and no NASH and can stage fibrosis. Markers of hepatocyte apoptosis hold promise as noninvasive tests for NASH diagnosis. Several scoring systems that combine routine clinical and laboratory variables and some proprietary panels can assist in predicting fibrosis severity. Noninvasive imaging modalities are reasonably accurate available tools to determine severity of fibrosis in NAFLD, but none of them yet can replace liver biopsy.

Dual-phase computed tomography for assessment of pancreatic fibrosis and anastomotic failure risk following pancreatoduodenectomy

INTRODUCTION

Delayed or decreased computed tomography (CT) enhancement characteristics in pancreatic fibrosis have been described.

METHODS

A review of 157 consecutive patients with preoperative dual-phase CT between 2004 and 2009 was performed. Pancreatic CT attenuation upstream from the tumor was measured in the pancreatic and hepatic imaging phases. The ratio of the mean CT attenuation value [hepatic to pancreatic phase; late/early (L/E) ratio] and histological grade of pancreatic fibrosis was correlated with the development of a clinically relevant pancreatic anastomotic failure (PAF) and other clinical parameters.

RESULTS

A clinically relevant PAF was observed in 21 patients (13.4%) with morbidity and mortality of 39.5% and 0%, respectively. The PAF group showed maximum enhancement in the pancreatic and washout in the hepatic CT phase, while the no PAF group showed a delayed enhancement pattern. Degree of pancreatic fibrosis and L/E ratio were significantly lower for the PAF group than the no PAF group (0.86 ± 0.14 vs. 1.09 ± 0.24; P < 0.0001 and 21.0 ± 17.9 vs. 40.4 ± 29.8; P < 0.0001); fewer PAF patients showed an atrophic histological pattern (14% vs. 39%; P = 0.046). The L/E ratio was positively correlated with pancreatic fibrosis. Pancreatic fibrosis and L/E ratio increased with larger duct size (P < 0.001), the presence of diabetes (P < 0.05), and the surgeon's assessment of pancreas firmness (P < 0.001). In multivariate analyses, L/E ratio and body mass index were significant predictors for the development of a clinically relevant PAF; a 0.1-U increase of L/E ratio decreased the odds of a PAF by 54%.

CONCLUSION

Pancreatic CT enhancement pattern can accurately assess pancreatic fibrosis and is a powerful tool to predict the risk of developing a clinically relevant PAF following PD.

Comparison of transient elastography and acoustic radiation force impulse for non-invasive staging of liver fibrosis in patients with chronic hepatitis C

OBJECTIVES

Transient elastography (TE) is adequate for a diagnosis of cirrhosis, but its accuracy for milder stages of fibrosis is much less satisfactory. The objective of this study was to compare the performance and the discordance rate of acoustic radiation force impulse (ARFI) and TE with liver biopsy in a cohort of chronic hepatitis C (CHC) patients.

METHODS

One hundred thirty-nine consecutive patients with CHC were enrolled in two tertiary centers, and evaluated for histological (Metavir score) and biochemical features. All patients underwent TE and ARFI.

RESULTS

TE was unreliable in nine patients (6.5%), while in no cases (0%) were ARFI invalid measurements recorded (P=0.029). By area under receiver operating characteristic curve (AUROC), the best cutoff values for TE and ARFI for significant fibrosis (≥F2) were ≥6.5 kPa (AUROC: 0.78) and ≥1.3 m/s (AUROC: 0.86), respectively. For severe fibrosis (F3-F4), these cutoff values were 8.8 kPa (AUROC: 0.83) for TE and 1.7 m/s (AUROC: 0.94) for ARFI. For cirrhosis, TE had its best cutoff at ≥11 kPa (AUROC: 0.80) and ARFI at ≥2.0 m/s (AUROC: 0.89). By pairwise comparison of AUROC, ARFI was significantly more accurate than TE for a diagnosis of significant and severe fibrosis (P=0.024 and P=0.002, respectively), while this difference was only marginal for cirrhosis (P=0.09). By partial AUROC analysis, ARFI performance results significantly higher for all three stages of fibrosis. The average concordance rates of TE and ARFI vs. liver biopsy were 45.4 and 54.7%, respectively. By multivariate analysis, ARFI was not associated with alanine aminotransferase (ALT), body mass index, Metavir grade, and liver steatosis, while TE was significantly correlated with the ALT value (P=0.027).

CONCLUSIONS

In a cohort of patients with CHC, ARFI imaging was more accurate than TE for the non-invasive staging of both significant and severe classes of liver fibrosis.

Non-invasive assessment of liver fibrosis in chronic hepatitis C

Quantification of hepatic fibrosis is of critical importance in chronic hepatitis C not only for prognosis, but also for antiviral treatment indication. Two end points are clinically relevant: detection of significant fibrosis (indication for antiviral treatment) and detection of cirrhosis (screening for eosphageal varices and hepatocellular carcinoma). Until recently, liver biopsy was considered the reference method for the evaluation of liver fibrosis. Limitations of liver biopsy (invasiveness, sampling error, and inter-observer variability) have led to the development of non-invasive methods. Currently available methods rely on two different approaches: a "biological" approach based on the dosage of serum fibrosis biomarkers; and a "physical" approach based on the measurement of liver stiffness, using transient elastography (TE). This review is aimed at discussing the advantages and limits of non-invasive methods and liver biopsy and the perspectives for their rational use in clinical practice in the management of patients with chronic hepatitis C.

Noninvasive assessment of liver fibrosis

Liver biopsy has long been an important tool for assessing the degree of liver fibrosis. Information on the presence and degree of liver fibrosis is useful before making therapeutic decisions or predicting disease outcomes. The need to stage liver fibrosis, however, should decrease as treatment options become more successful (as has occurred with viral hepatitis). In recent years, noninvasive tests have demonstrated a reasonable ability to identify significant fibrosis, cirrhosis in particular, nor is it surprising that liver disease specialists and patients favor a noninvasive approach. However, only those tests with the highest diagnostic accuracy, cost-effectiveness, and availability should be implemented. Apart from their diagnostic accuracy, the potential ability of these tests to predict disease outcomes (a more relevant endpoint) should be compared with that of liver biopsy. Indeed, the use of a standardized system to evaluate the utility of biomarkers would facilitate their implementation in clinical practice.

Transient elastography and other noninvasive tests to assess hepatic fibrosis in patients with viral hepatitis

The limitations of liver biopsy (invasive procedure, sampling errors, inter-observer variability and nondynamic fibrosis evaluation) have stimulated the search for noninvasive approaches for the assessment of liver fibrosis in patients with viral hepatitis. A variety of methods including the measurement of liver stiffness, using transient elastography, and serum markers, ranging from routine laboratory tests to more complex algorithms or indices combining the results of panels of markers, have been proposed. Among serum indices, Fibrotest has been the most extensively studied and validated. Transient elastography appears as a promising method but has been mostly validated in chronic hepatitis C with performance equivalent to that of serum markers for the diagnosis of significant fibrosis. The combination of both approaches as first-line assessment of liver fibrosis could avoid the performance of liver biopsy in the majority of patients with chronic hepatitis C, a strategy that deserves further evaluation in patients with hepatitis B or HIV-HCV coinfection. Transient elastography also appears to be an excellent tool for early detection of cirrhosis and may have prognostic value in this setting. Guidelines are now awaited for the use of noninvasive methods in clinical practice.

Noninvasive diagnosis of liver fibrosis in patients with HIV infection and HCV/HBV co-infection

The measurement of fibrosis stage critically affects the identification of the progression of liver disease, the establishment of a prognosis and therapeutic decision making. Liver biopsy has been the single, most useful method to determine the degree of liver fibrosis (LF), but with recognized limitations, mainly associated with its invasiveness. In recent years, alternative noninvasive methods have been developed, including imaging methods, such as transient elastometry, and assays based on serum biomarkers. This article reviews the available studies evaluating the value of various noninvasive methods for the assessment of LF in patients with HIV-infection and HBV/HCV co-infection, and makes recommendations on how to best use and combine them in clinical practice.

Noninvasive assessment of liver fibrosis: serum markers, imaging, and other modalities

Liver fibrosis is a common pathway of injury after chronic insult to the liver. The evolution of liver fibrosis to cirrhosis has many clinical implications, including bleeding, infection, hepatocellular carcinoma, and death. The reference standard for diagnosing liver fibrosis is currently histologic assessment of tissue obtained through liver biopsy. Although this provides valuable information, it has limitations, including its invasiveness, sampling error, observer variability, and the use of categorical scoring systems. This article outlines the various noninvasive markers, including blood tests, imaging, and novel technologies. It examines the principles behind their development, their diagnostic accuracy, and their evolution.