Multidetector Computed Tomography for Retrospective, Noninvasive Staging of Liver Fibrosis

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.

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.

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

BACKGROUND

To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.

METHODS

A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models.

RESULTS

The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively.

CONCLUSION

We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.

Reproducibility of MRI-derived liver surface nodularity score: analysis of patients with repeated MRI in various scanners

PURPOSE

To assess trans-regional differences, reproducibility across different MRI scanners, and interobserver agreement of liver surface nodularity (LSN) score from routine liver MRI and to evaluate the correlation between LSN score and liver stiffness (LS) value on MR elastography.

MATERIALS AND METHODS

Ninety patients who underwent gadoxetic acid-enhanced liver MRI twice using different MRI scanners within a year were evaluated. On axial hepatobiliary phase images, right anterior (LSN_{RT_ANT}), right posterior (LSN_{RT_POST}), and left anterior hepatic surface (LSN_LT) were chosen for the quantification of LSN score. Repeated-measures ANOVA, paired t test, Pearson's correlation coefficient analysis, and intraclass correlation coefficient (ICC) were used for statistical analysis.

RESULTS

LSN scores from high to low were LSN_{RT_POST}, LSN_{RT_ANT}, and LSN_LT, representing trans-regional differences (p < 0.001). Reproducibility of LSN measurement across different MRI scanners was high to excellent (ICC = 0.838-0.921). The mean difference between first and second examinations in LSN_{RT_ANT}, LSN_{RT_POST}, and LSN_LT were 0.032 (p = 0.013), 0.002 (p = 0.910), and 0.010 (p = 0.285) for reader 1 and 0.051 (p = 0.004), 0.061 (p = 0.002), and 0.023 (p = 0.005) for reader 2. The first and second examinations were highly correlated in all hepatic regions (r = 0.712-0.839, p < 0.001). There was a low to moderate correlation between LSN score and LS value (r = 0.364-0.592, p ≤ 0.001), which was higher in the chronic hepatitis B (CHB) group than in the non-CHB group in all hepatic regions.

CONCLUSIONS

In our study, LSN measurement on liver MRI showed trans-regional differences and excellent reproducibility across different MRI scanners. To use LSN score more widely, standardization of quantification software and selected hepatic regions is needed.

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

Objective

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

Methods

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

Results

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

Conclusions

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

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.

Multiinstitutional Evaluation of the Liver Surface Nodularity Score on CT for Staging Liver Fibrosis and Predicting Liver-Related Events in Patients With Hepatitis C

BACKGROUND. In single-institution multireader studies, the liver surface nodularity (LSN) score accurately detects advanced liver fibrosis and cirrhosis and predicts liver decompensation in patients with chronic liver disease (CLD) from hepatitis C virus (HCV). OBJECTIVE. The purpose of this study was to assess the diagnostic performance of the LSN score alone and in combination with the (FIB-4; fibrosis index based on four factors) to detect advanced fibrosis and cirrhosis and to predict future liver-related events in a multiinstitutional cohort of patients with CLD from HCV. METHODS. This retrospective study included 40 consecutive patients, from each of five academic medical centers, with CLD from HCV who underwent nontargeted liver biopsy within 6 months before or after abdominal CT. Clinical data were recorded in a secure web-based database. A single central reader measured LSN scores using software. Diagnostic performance for detecting liver fibrosis stage was determined. Multivariable models were constructed to predict baseline liver decompensation and future liver-related events. RESULTS. After exclusions, the study included 191 patients (67 women, 124 men; mean age, 54 years) with fibrosis stages of F0-F1 (n = 37), F2 (n = 44), F3 (n = 46), and F4 (n = 64). Mean LSN score increased with higher stages (F0-F1, 2.26 ± 0.44; F2, 2.35 ± 0.37; F3, 2.42 ± 0.38; F4, 3.19 ± 0.89; p < .001). The AUC of LSN score alone was 0.87 for detecting advanced fibrosis (≥ F3) and 0.89 for detecting cirrhosis (F4), increasing to 0.92 and 0.94, respectively, when combined with FIB-4 scores (both p = .005). Combined scores at optimal cutoff points yielded sensitivity of 75% and specificity of 82% for advanced fibrosis, and sensitivity of 84% and specificity of 85% for cirrhosis. In multivariable models, LSN score was the strongest predictor of baseline liver decompensation (odds ratio, 14.28 per 1-unit increase; p < .001) and future liver-related events (hazard ratio, 2.87 per 1-unit increase; p = .03). CONCLUSION. In a multiinstitutional cohort of patients with CLD from HCV, LSN score alone and in combination with FIB-4 score exhibited strong diagnostic performance in detecting advanced fibrosis and cirrhosis. LSN score also predicted future liver-related events. CLINICAL IMPACT. The LSN score warrants a role in clinical practice as a quantitative marker for detecting advanced liver fibrosis, compensated cirrhosis, and decompensated cirrhosis and for predicting future liver-related events in patients with CLD from HCV.

Intraindividual comparison of MRI-derived liver surface nodularity score at 1.5 T and 3 T

PURPOSE

To compare the MRI-derived liver surface nodularity (LSN) scores acquired on both 1.5 T and 3 T.

MATERIALS AND METHODS

Forty chronic liver disease patients who underwent gadoxetic acid-enhanced MRI at both 1.5 and 3 T were included. Axial hepatobiliary phase images with the same voxel size were used to calculate the LSN score in both liver lobes with a quantitative software. Rank correlation, Wilcoxon test, and Bland-Altman limits of agreement were used for statistical analysis.

RESULTS

There was a weak correlation between the right and left liver lobe on 1.5 T (r_s = 0.331, p = 0.037) and 3 T (r_s = 0.381, p = 0.015). The correlation between 1.5 T and 3 T on both liver lobes showed a very strong correlation (right, r_s = 0.927, p < 0.001; left, r_s = 0.845, p < 0.001). LSN scores differed significantly between both lobes on 1.5 T (median, 1.201 vs. 0.674, right vs. left) and 3 T (1.076 vs. 0.592) (all p < 0.001). LSN scores differed significantly between 1.5 T and 3 T on both lobes (all p < 0.001). The Bland-Altman plot comparing 1.5 T and 3 T on right and left liver lobes showed a systemic bias of 0.08 and 0.07, respectively.

CONCLUSIONS

LSN scores differed significantly on 1.5 T vs. 3 T and right vs. left liver lobe. Caution should be made when comparing LSN scores derived from different field strengths or the hepatic lobe. Interplatform, interlobar reproducibility should be resolved to use LSN scores, which is relatively easy to perform without additional hardware or images.

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.

Performance of morphologic criteria for the diagnosis of cirrhosis in patients with non-alcoholic steatohepatitis compared to other etiologies of chronic liver disease: effect of level of training and experience

PURPOSE

To compare the diagnostic performance of morphologic criteria for detection of cirrhosis in patients with alcoholic liver disease (ALD), hepatitis C (HCV), and non-alcoholic steatohepatitis (NASH).

METHODS

One hundred patients (53 male) with different etiologies of chronic liver disease (NASH, n = 41; HCV, n = 39; and ALD, n = 20) and with different degrees of fibrosis on histopathologic examination (74 with cirrhosis) were retrospectively evaluated. Four readers (R1: fellowship-trained abdominal radiologist, R2: community attending radiologist, R3: senior radiology resident/research fellow, R4: junior radiology resident) analyzed the contrast-enhanced CTs for presence of commonly accepted morphologic changes of cirrhosis and portal hypertension. Each reader assigned an overall score (using a 5-point scale) for possibility of cirrhosis based on liver morphology and features of portal hypertension. Diagnostic performance, sensitivity, and specificity for the diagnosis of cirrhosis were calculated and compared between different etiologies of chronic liver disease.

RESULTS

Performance of readers was affected by their level of training. Less experienced readers had overall lower sensitivity for diagnosis of cirrhosis in NASH group (R3: 81.5%, R4: 63.0% compared to 96.3% for both R1 and R2). Sensitivities for detection of NASH cirrhosis significantly decreased for less experienced readers in the absence of ascites (R3: 75.0%, R4: 62.0%) or other features of portal hypertension (R3: 50.0%; R4: 0%). The specificity was consistently high among different etiologies and for all readers (85.7-100%). Inter-reader agreement for morphologic features ranged widely from fair to almost perfect (k: 0.23-0.85).

CONCLUSION

Cirrhotic changes in NASH are subtler and can be underestimated by less experienced readers.

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.

Diagnostic value of MRI-derived liver surface nodularity score for the non-invasive quantification of hepatic fibrosis in non-alcoholic fatty liver disease

OBJECTIVES

To assess the accuracy of MRI-derived liver surface nodularity (LSN) score for staging of hepatic fibrosis in patients with non-alcoholic fatty liver disease (NAFLD).

METHODS

Forty-seven patients with clinicopathological diagnosis of NAFLD who underwent 1.5-T liver MRI within 12 months of liver biopsy were included. Axial non-contrast T1-weighted 3D GRE was used for image analysis. LSN of the left lobe was measured using a custom semiautomated software. Histopathologic analysis (F0-F4) served as the reference standard for staging of fibrosis. Mann-Whitney test and Spearman's correlation coefficient were used to compare LSN scores between different stages of fibrosis and to assess the correlation. Diagnostic performance of LSN score for detection of significant (F2-F4) and advanced (F3-F4) fibrosis was assessed by receiver operating characteristics (ROC) curve. p value of less than 0.05 was considered statistically significant different.

RESULTS

Twenty-one subjects had advanced fibrosis. The LSN scores among different stages of fibrosis were significantly different (p < 0.001). The correlation between LSN score and stage of fibrosis was also strong (ρ = 0.71; p < 0.001). The areas under ROC curves for detection of significant and advanced fibrosis were 0.80 (95% CI 0.66-0.95) and 0.86 (95% CI 0.75-0.97), using a threshold of 2.23 and 2.44, respectively. This method showed 81% sensitivity and 88% specificity for detection of advanced fibrosis.

CONCLUSION

MR-based LSN score is a promising non-invasive objective tool for detection of advanced fibrosis in patients with NAFLD.

KEY POINTS

• Liver surface nodularity (LSN) score is a fast retrospective method for precise quantification of nodularity of liver surface. • MR-based LSN score is a promising non-invasive objective tool to accurately detect different stages of fibrosis in patients with non-alcoholic fatty liver disease (NAFLD).

Preoperative Liver Function Guiding HCC Resection in Normal and Cirrhotic Liver

Background

Liver resection is the most effective available therapy for patients with hepatocellular carcinoma (HCC). The accurate selection of patients for surgery requires determination of technical resectability and the risk of recurrence, as well as assessment of liver function and functional reserve to avoid postoperative liver failure. Previous studies have underlined the effectiveness and reliability of the LiMAx® test to evaluate liver function preoperatively. Nevertheless, data concerning HCC evaluation are lacking.

Methods

From 2014 to 2019, 92 patients with HCC underwent additional assessment of liver function using the LiMAx test prior to decision for or against liver resection. Preoperative LiMAx results were compared between cirrhotic and noncirrhotic liver. The clinical decision for surgery was evaluated applying the various liver function parameters available.

Results

Forty-six patients underwent liver resection. The LiMAx results were higher in resected patients (388 vs. 322 µg/kg/h; p = 0.004). LiMAx values were an independent risk factor for the presence of liver cirrhosis in multivariate analysis. In 17 patients, surgical treatment was cancelled due to major impairment of liver function. Only 4 out of 46 resected patients presented with post-hepatectomy liver failure (PHLF) grade ≥B. Histologic assessment revealed liver cirrhosis in 10 resected patients without PHLF.

Conclusion

Preoperative determination of liver function by the LiMAx test enables effective and safe patient selection for HCC resection in both cirrhotic and noncirrhotic liver.

Performance of liver surface nodularity quantification for the diagnosis of portal hypertension in patients with cirrhosis: comparison between MRI with hepatobiliary phase sequences and CT

PURPOSE

To assess and compare the performance of liver surface nodularity (LSN) quantification using Gd-BOPTA-enhanced MRI and contrast-enhanced CT for the diagnosis of clinically significant portal hypertension (CSPH) in patients with cirrhosis.

METHODS

This retrospective study included 30 patients with compensated histologically proven cirrhosis who underwent hepatic venous pressure gradient (HVPG), abdominal CT and Gd-BOPTA-MRI within a 60-day interval during pre-surgery workup for hepatocellular carcinoma (HCC) between January 2016 and August 2018. LSN score was derived from CT portal venous phase (PVP), axial T2- and T1-weighted PVP and hepatobiliary phase (HBP). Accuracy for the detection of CSPH was evaluated for each set of images by ROC curve analysis. Intra-observer, inter-observer and inter-method reproducibilities were assessed by the intraclass correlation coefficient (ICC) and coefficient of variation (CV).

RESULTS

Thirty patients were analysed (23 men [77%], mean age 60 ± 11 years old), including 15 (50%) with CSPH. All CT- and MRI-derived LSN quantifications were correlated to HVPG (CT-PVP: r = 0.63, p = 0.001, AUROC = 0.908 ± 0.06; T1-w-PVP: r = 0.43, p = 0.028, AUROC = 0.876 ± 0.07; T1-w-HBP: r = 0.50, p = 0.012, AUROC = 0.823 ± 0.08; T2-w: r = 0.51, p = 0.007, AUROC = 0.801 ± 0.09). There was no significant difference in AUROC pairwise comparisons (p = 0.12-0.88). Patients with CSPH had higher LSN than those without (CT-PVP: 3.2 ± 0.6 vs 2.4 ± 0.5, p < 0.001; T1-w-PVP: 2.7 ± 0.4 vs 2.2 ± 0.4, p = 0.002; T1-w-HBP: 3.0 ± 0.6 vs 2.3 ± 0.3, p < 0.001; T2-w: 3.0 ± 0.6 vs 2.2 ± 0.3, p = 0.001) and 86%, 82%, 85% and 82% of patients were correctly classified, respectively. Reproducibility of inter-image set comparisons was excellent (ICC = 0.84-0.96 and CV = 8.3-14.2%).

CONCLUSION

The diagnostic performance of MRI-based LSN for detecting CSPH is strong and similar to that of CT-based LSN.

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.