Liver Surface Nodularity Score Allows Prediction of Cirrhosis Decompensation and Death

Fully Automated and Explainable Measurement of Liver Surface Nodularity in CT: Utility for Staging Hepatic Fibrosis

RATIONALE AND OBJECTIVES

In the United States, cirrhosis was the 12th leading cause of death in 2016. Despite end-stage cirrhosis being irreversible, earlier stages of hepatic fibrosis can be reversed via early diagnosis and intervention. The objective is to investigate the utility of a fully automated technique to measure liver surface nodularity (LSN) for staging hepatic fibrosis (stages F0-F4).

MATERIALS AND METHODS

In this retrospective study, a dataset consisting of patients with multiple etiologies of liver disease collected at Institution-A (METAVIR F0-F4, 2000-2016) was used. The LSN was automatically measured in contrast-enhanced CT volumes and compared against scores from a manual tool. Area under the receiver operating characteristics curve (AUC) was used to distinguish between clinically significant fibrosis (≥ F2), advanced fibrosis (≥F3), and end-stage cirrhosis (F4).

RESULTS

The study sample had 480 patients (304 men, 176 women, mean age, 49±9). Automatically derived LSN scores progressively increased with the fibrosis stage: F0 (1.64 [mean]±1.13 [standard deviation]), F1 (2.16±2.39), F2 (2.17±2.55), F3 (2.23±2.52), and F4 (4.21±2.94). For discriminating significant fibrosis (≥F2), advanced fibrosis (≥F3), and cirrhosis (F4), the automated tool achieved ROC AUCs of 73.9%, 82.5%, and 87.8% respectively. The sensitivity and specificity for significant fibrosis (nodularity threshold 1.51) was 85.2% and 73.3%, advanced fibrosis (nodularity threshold 1.73) was 84.2% and 79.5%, and cirrhosis (nodularity threshold 2.18) was 86.5% and 79.5%. Statistical tests revealed that the automated LSN scores distinguished patients with advanced fibrosis (p<.001) and cirrhosis (p<.001).

CONCLUSION

The fully automated LSN measurement retained its predictive power for distinguishing between advanced fibrosis and cirrhosis. The clinical impact is that the fully automated LSN measurement may be useful for early interventions and population-based studies. It can automatically predict the fibrosis stage in ∼45 s in comparison to the ∼2 min needed to manually measure the LSN in a CT volume.

Non-invasive imaging biomarkers in chronic liver disease

Chronic liver disease (CLD) is a global and worldwide clinical challenge, considering that different underlying liver entities can lead to hepatic dysfunction. In the past, blood tests and clinical evaluation were the main noninvasive tools used to detect, diagnose and follow-up patients with CLD; in case of clinical suspicion of CLD or unclear diagnosis, liver biopsy has been considered as the reference standard to rule out different chronic liver conditions. Nowadays, noninvasive tests have gained a central role in the clinical pathway. Particularly, liver stiffness measurement (LSM) and cross-sectional imaging techniques can provide transversal information to clinicians, helping them to correctly manage, treat and follow patients during time. Cross-sectional imaging techniques, namely computed tomography (CT) and magnetic resonance imaging (MRI), have plenty of potential. Both techniques allow to compute the liver surface nodularity (LSN), associated with CLDs and risk of decompensation. MRI can also help quantify fatty liver infiltration, mainly with the proton density fat fraction (PDFF) sequences, and detect and quantify fibrosis, especially thanks to elastography (MRE). Advanced techniques, such as intravoxel incoherent motion (IVIM), T1- and T2- mapping are promising tools for detecting fibrosis deposition. Furthermore, the injection of hepatobiliary contrast agents has gained an important role not only in liver lesion characterization but also in assessing liver function, especially in CLDs. Finally, the broad development of radiomics signatures, applied to CT and MR, can be considered the next future approach to CLDs. The aim of this review is to provide a comprehensive overview of the current advancements and applications of both invasive and noninvasive imaging techniques in the evaluation and management of CLD.

Liver-specific DICER1 syndrome model mice develop cystic liver tumors with defective primary cilia

DICER1 syndrome is a tumor predisposition syndrome caused by familial genetic mutations in DICER1. Pathogenic variants of DICER1 have been discovered in many rare cancers, including cystic liver tumors. However, the molecular mechanisms underlying liver lesions induced by these variants remain unclear. In the present study, we sought to gain a better understanding of the pathogenesis of these variants by generating a mouse model of liver-specific DICER1 syndrome. The mouse model developed bile duct hyperplasia with fibrosis, similar to congenital hepatic fibrosis, as well as cystic liver tumors resembling those in Caroli's syndrome, intrahepatic cholangiocarcinoma, and hepatocellular carcinoma. Interestingly, the mouse model of DICER1 syndrome showed abnormal formation of primary cilia in the bile duct epithelium, which is a known cause of bile duct hyperplasia and cyst formation. These results indicated that DICER1 mutations contribute to cystic liver tumors by inducing defective primary cilia. The mouse model generated in this study will be useful for elucidating the potential mechanisms of tumorigenesis induced by DICER1 variants and for obtaining a comprehensive understanding of DICER1 syndrome. © 2024 The Pathological Society of Great Britain and Ireland.

Systematic review and meta-analysis of biomarkers predicting decompensation in patients with compensated cirrhosis

BACKGROUND AND AIMS

The transition from compensated to decompensated cirrhosis is crucial, drastically reducing prognosis from a median survival of over 10 years to 2 years. There is currently an unmet need to accurately predict decompensation. We systematically reviewed and meta-analysed data regarding biomarker use to predict decompensation in individuals with compensated cirrhosis.

METHODS

PubMed and EMBASE database searches were conducted for all studies from inception until February 2024. The study was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality of Prognosis Studies framework was used to assess the risk of bias. The meta-analysis was conducted with a random effects model using STATA software.

RESULTS

Of the 652 studies initially identified, 63 studies (n=31 438 patients) were included in the final review, examining 49 biomarkers. 25 studies (40%) were prospective with the majority of studies looking at all-cause decompensation (90%). The most well-studied biomarkers were platelets (n=17), Model for End-Stage Liver Disease (n=17) and albumin (n=16). A meta-analysis revealed elevated international normalised ratio was the strongest predictor of decompensation, followed by decreased albumin. However, high statistical heterogeneity was noted (l2 result of 96.3%). Furthermore, 21 studies were assessed as having a low risk of bias (34%), 26 (41%) moderate risk and 16 (25%) high risk.

CONCLUSIONS

This review highlights key biomarkers that should potentially be incorporated into future scoring systems to predict decompensation. However, future biomarker studies should be conducted with rigorous and standardised methodology to ensure robust and comparable data.

Quantitative Edge Analysis Can Differentiate Pancreatic Carcinoma from Normal Pancreatic Parenchyma

This study aimed to introduce specific image feature analysis, focusing on pancreatic margins, and to provide a quantitative measure of edge irregularity, evidencing correlations with the presence/absence of pancreatic adenocarcinoma. We selected 50 patients (36 men, 14 women; mean age 63.7 years) who underwent Multi-detector computed tomography (MDCT) for the staging of pancreatic adenocarcinoma of the tail of the pancreas. Computer-assisted quantitative edge analysis was performed on the border fragments in MDCT images of neoplastic and healthy glandular parenchyma, from which we obtained the root mean square deviation SD of the actual border from the average boundary line. The SD values relative to healthy and neoplastic borders were compared using a paired t-test. A significant SD difference was observed between healthy and neoplastic borders. A threshold SD value was also found, enabling the differentiation of adenocarcinoma with 96% specificity and sensitivity. We introduced a quantitative measure of boundary irregularity, which correlates with the presence/absence of pancreatic adenocarcinoma. Quantitative edge analysis can be promptly performed on select border fragments in MDCT images, providing a useful supporting tool for diagnostics and a possible starting point for machine learning recognition based on lower-dimensional feature space.

The Negative Impact of Sarcopenia on Hepatocellular Carcinoma Treatment Outcomes

INTRODUCTION

Hepatocellular carcinoma (HCC) represents a major global health concern, characterized by evolving etiological patterns and a range of treatment options. Among various prognostic factors, sarcopenia, characterized by loss of skeletal muscle mass, strength, and function, has emerged as a pivotal contributor to HCC outcomes. Focusing on liver transplantation, surgical resection, locoregional treatments, and systemic therapies, this review aims to analyze the impact of sarcopenia on HCC treatment outcomes, shedding light on an underexplored subject in the pursuit of more personalized management.

METHODS

A comprehensive literature review was conducted by searching peer-reviewed articles on sarcopenia and treatment outcomes in patients with HCC from inception up to October 2023.

RESULTS

Sarcopenia was found to be prevalent among HCC patients, exhibiting different occurrence, possibly attributable to diverse diagnostic criteria. Notably, despite variations in studies utilizing skeletal muscle indices, sarcopenia independently correlated with lower overall survival (OS), recurrence-free survival (RFS), and progression-free survival (PFS) across surgical (both transplantation and resection), locoregional, and systemic therapies, including tyrosine-kinase inhibitors (TKIs) and immune-checkpoint inhibitors (ICIs). Moreover, a link between sarcopenia and increased rate and severity of adverse events, particularly in surgery and TKIs recipients, and larger tumor size at diagnosis was observed. While baseline sarcopenia negatively influenced treatment outcomes, alterations in muscle mass post-treatment emerged as primary determinants of reduced OS.

CONCLUSIONS

Sarcopenia, either present before or after HCC treatment, negatively correlates with response to it, across all etiologies and therapeutic strategies. Although only a few studies have evaluated the impact of supervised physical activity training on muscle mass and OS after HCC treatment, it is crucial to evaluate the presence of sarcopenia before treatment initiation, to better stratify patients' prognosis, thus performing a more tailored approach, and identify therapies able to restore muscle mass in HCC patients. Conversely, the impact of sarcopenia on HCC recurrence and extrahepatic spread remains inadequately explored.

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.

Prediction of Hepatic Encephalopathy After Transjugular Intrahepatic Portosystemic Shunt Based on CT Radiomic Features of Visceral Adipose Tissue

RATIONALE AND OBJECTIVES

To evaluate the performance and clinical utility of CT radiomic features of visceral adipose tissue (VAT) in the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS).

MATERIALS AND METHODS

This multi-center study was retrospectively designed. Patients with cirrhosis who underwent TIPS were recruited from January 2015 to December 2020. Pre-TIPS contrast-enhanced CT images were collected for VAT segmentation and radiomic feature extraction. Least absolute shrinkage and selection operator regression with ten-fold cross-validation was performed to reduce dimension. Logistic regression with regularization, support vector machine, and random forest were used for model construction.

RESULTS

A total of 130 patients (90 men; mean age, 54 ± 11 years) were finally enrolled. The cohort was split into 85 patients for the training set (58 men; mean age, 53 ± 12 years) with 19 HE, 21 patients for the internal test set (17 men; mean age, 53 ± 11 years) with 5 HE, and 24 patients for the external test set (15 men; mean age, 55 ± 11 years). Ten radiomic features and C-reactive protein constituted radiomic-clinical models with the best performance. The average area under the receiver operating characteristic curve is 0.97 in the training set and 0.84 in the test sets. For a fixed sensitivity of 0.90, the specificity and negative predictive value of the model is 0.63 and 1.00, respectively; while for a fixed specificity of 0.90, the sensitivity and positive predictive value is 0.60 and 0.75, respectively.

CONCLUSION

Machine learning models based on CT radiomic features extracted from VAT can predict post-TIPS HE with satisfactory performance.

CLINICAL RELEVANCE STATEMENT

Our machine learning models based on CT radiomic features of visceral adipose tissue in patients with cirrhosis may assist in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt, indicating its potential in patient selection and clinical decision-making.

KEY POINTS

Radiomics of visceral adipose tissue provide great help in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt. The clinical-radiomic models showed satisfactory performance with an average area under the receiver operating characteristic curve of 0.84. The model can hypothetically provide 90% sensitivity and 100% negative predictive value for guiding patients who are considering transjugular intrahepatic portosystemic shunt.

Quantitative edge analysis of pancreatic margins in patients with head pancreatic tumors: correlations between pancreatic margins and the onset of postoperative pancreatic fistula

OBJECTIVE

To assess the correlation between pancreatic quantitative edge analysis as a surrogate of parenchymal stiffness and the incidence of postoperative pancreatic fistula (POPF), in patients undergoing pancreaticoduodenectomy (PD).

METHODS

All consecutive patients who underwent PD at our Institution between March 2018 and November 2019 with an available preoperative CT were included. Pancreatic margin score (PMS) was calculated through computer-assisted quantitative edge analysis on the margins of the pancreatic body and tail (the expected pancreatic remnant) on non-contrast scans with in-house software. Intraoperative assessment of pancreatic stiffness by manual palpation was also performed, classifying pancreatic texture into soft and non-soft. PMS values were compared between groups using an unpaired T-test and correlated with the intraoperative evaluation of stiffness and with the grading of postoperative pancreatic fistula according to the International Study Group on Pancreatic Surgery (ISGPS).

RESULTS

Patient population included 200 patients (mean age 64.6 years), 146 without onset of POPF (73%, non-POPF group), and 54 with POPF (27%, POPF group). A significant difference in PMS values was observed between POPF and non-POPF (respectively 1.88 ± 0.05 vs 0.69 ± 0.01; p < 0.0001). PMS values of pancreatic parenchymas intraoperatively considered "soft" were significantly higher than those evaluated as "non-soft" (1.21 ± 0.04 vs 0.73 ± 0.02; p < 0.0001). A significant correlation between PMS values and POPF grade was observed (r = 0.8316), even in subgroups of patients with soft (r = 0.8016) and non-soft (r = 0.7602) pancreas (all p < 0.0001).

CONCLUSIONS

Quantitative edge analysis with dedicated software may stratify patients with different pancreatic stiffness, thus potentially improving preoperative risk assessment and strategies for POPF mitigation.

CLINICAL RELEVANCE STATEMENT

This study proposes quantitative pancreas edge analysis as a predictor for postoperative pancreatic fistula. The test has high accuracy and correlation with fistula grade according to the International Study Group on Pancreatic Surgery.

KEY POINTS

• Prediction of postoperative pancreatic fistula (POPF) onset risk after pancreaticoduodenectomy is based only on intraoperative evaluation. • Quantitative edge analysis may preoperatively identify patients with higher risk of POPF. • Quantification of pancreatic stiffness through the analysis of pancreatic margins could be done on preoperative CT.

Liver surface nodularity and ascites are associated with mortality risk in acute alcohol-associated hepatitis

BACKGROUND

Acute alcohol-associated hepatitis (AH) is associated with high mortality. CT-derived liver surface nodularity (LSN) is a robust prognostic biomarker in other chronic liver diseases. The aim of this study was to determine relationships between LSN, disease severity, and mortality in AH.

METHODS

Adults hospitalized with AH from January 2016 to March 2020 were included if an abdominal CT was performed between 8 weeks prior to 72 h after hospitalization. LSN was measured using quantitative methods (Liver Surface Nodularity Software version 0.88, Birmingham, AL, USA). Cox proportional hazards models, logistic regression and AUROC analysis were used to examine relationships between LSN and 180-day transplant-free survival.

RESULTS

Of 386 patients hospitalized with AH during the study period, 230 had CT scans performed, and 205 met inclusion criteria. Mean transplant-free survival was 127 days (95% CI 118-137). Within each cohort, patients were grouped into low [LSN-LOW, N = 109 (53.2%)] and high [LSN-HIGH, N = 96 (46.8%)] LSN strata based on an optimal cutoff of 2.86 derived from unadjusted ROC curves. Patients with high LSN had features of portal hypertension, which included encephalopathy [53 (55.2%) vs. 43 (39.4%), p = 0.017], ascites on CT [81 (84.4%) vs. 69 (63.3%), p = 0.001] and portosystemic shunts [78 (81.2%) vs. 69 (63.3%), p = 0.003]. High LSN, ascites and MELD were independently associated with lower likelihood of 180-day transplant-free survival, and inclusion of a score assigning 1 point each for high LSN or ascites on CT (AHRADS score) to MELD enhanced diagnostic accuracy of AUROC for 180-day survival compared to MELD alone [AUROC 0.782 (95% CI 0.719-0.845) vs. 0.735 (0.667-0.802), p = 0.023].

CONCLUSIONS

CT-derived factors that include LSN and ascites are radiographic biomarkers associated with 180-day transplant-free survival in alcohol-associated hepatitis.

3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study

BACKGROUND

Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE.

METHODS

In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed.

RESULTS

The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child-Pugh score, ammonia level, or the indication for TIPS.

CONCLUSION

3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis.

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.

Quantitative Edge Analysis of Pancreatic Margins in Patients with Chronic Pancreatitis: A Correlation with Exocrine Function

BACKGROUND

Many efforts have been made to improve accuracy and sensitivity in diagnosing chronic pancreatitis (CP), obtaining quantitative assessments related to functional data. Our purpose was to correlate a computer-assisted analysis of pancreatic morphology, focusing on glandular margins, with exocrine function-measured by fecal elastase values-in chronic pancreatitis patients.

METHODS

We retrospectively reviewed chronic pancreatitis patients who underwent fecal elastase assessment and abdominal MRI in our institute within 1 year. We identified 123 patients divided into three groups based on the fecal elastase value: group A with fecal elastase > 200 μg/g; group B with fecal elastase between 100 and 200 μg/g; and group C with fecal elastase < 100 μg/g. Computer-assisted quantitative edge analysis of pancreatic margins was made on non-contrast-enhanced water-only Dixon T1-weighted images, obtaining the pancreatic margin score (PMS). PMS values were compared across groups using a Kruskal-Wallis test and the correlation between PMS and fecal elastase values was tested with the Spearman's test.

RESULTS

A significant difference in PMS was observed between the three groups (p < 0.0001), with a significant correlation between PMS and elastase values (r = 0.6080).

CONCLUSIONS

Quantitative edge analysis may stratify chronic pancreatitis patients according to the degree of exocrine insufficiency, potentially contributing to the morphological and functional staging of this pathology.

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.

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.

Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features

Hepatic cirrhosis is an increasing cause of mortality in developed countries-it is the pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since cirrhosis evolves from the asymptomatic phase, it is of paramount importance to detect it as quickly as possible, because entering the symptomatic phase commonly leads to hospitalization and can be fatal. Understanding the state of the liver based on the abdominal computed tomography (CT) scans is tedious, user-dependent and lacks reproducibility. We tackle these issues and propose an end-to-end and reproducible approach for detecting cirrhosis from CT. It benefits from the introduced clinically-inspired features that reflect the patient's characteristics which are often investigated by experienced radiologists during the screening process. Such features are coupled with the radiomic ones extracted from the liver, and from the suggested region of interest which captures the liver's boundary. The rigorous experiments, performed over two heterogeneous clinical datasets (two cohorts of 241 and 32 patients) revealed that extracting radiomic features from the liver's rectified contour is pivotal to enhance the classification abilities of the supervised learners. Also, capturing clinically-inspired image features significantly improved the performance of such models, and the proposed features were consistently selected as the important ones. Finally, we showed that selecting the most discriminative features leads to the Pareto-optimal models with enhanced feature-level interpretability, as the number of features was dramatically reduced (280×) from thousands to tens.

Regional Analysis of Liver Surface Nodularity in a Single Axial MR Image for Staging Liver Fibrosis

BACKGROUND

The assessment of liver surface nodularity (LSN) for staging hepatic fibrosis is restricted in clinical practice because it requires customized software and time-consuming procedures. A simplified method to estimate LSN score may be useful in the clinic.

PURPOSE

To evaluate the regional analysis of LSN and processing time in a single axial liver MR image for staging liver fibrosis.

STUDY TYPE

Retrospective.

POPULATION

A total of 210 subjects, a multicenter study.

FIELD STRENGTH/SEQUENCE

A 3 T/noncontrast gradient echo T1WI.

ASSESSMENT

Subjects were divided into five fibrosis groups (F₀  = 29; F₁  = 20; F₂  = 32; F₃  = 50; F₄  = 79) based on the METAVIR fibrosis scoring system. The mean LSN (on three slices) and regional LSN (on one slice) measurements, and the processing times, are compared. The regional LSN scores in five regions-of-interests (ROI_1-5 ) were analyzed in a single axial MRI at the level of the hilum by two independent observers.

STATISTICAL TESTS

Regional variations in LSN scores were compared using ANOVA with Tukey test. Agreement between the mean and regional LSN measurements was evaluated using Pearson correlation coefficients (r) and Bland-Altman plots. The diagnostic performance of mean and regional LSN scores according to fibrosis stage was evaluated with the AUROC. A P value < 0.05 was considered statistically significant.

RESULTS

Total processing time for a regional LSN measurement (3.6 min) was 75.5% less than that for mean LSN measurement (14.7 min). Mean LSN scores and all five regional LSN scores showed significant differences between fibrosis groups. Among regional LSN scores, ROI₅ showed the highest AUROC (0.871 at cut-off 1.12) for discriminating F_0-2 vs. F_3-4 and the best correlation with mean LSN score (r = 0.800, -0.07 limit of agreement).

CONCLUSION

Quantitative regional LSN measurement in a single axial MR image reduces processing time. Regional ROI₅ LSN score might be useful for clinical decision-making and for distinguishing the difference between early fibrosis (F_0-2 ) and advanced fibrosis (F_3-4 ) in the liver.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

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.

Contrast-enhanced CT and liver surface nodularity for the diagnosis of porto-sinusoidal vascular disorder: A case-control study

BACKGROUND AND AIMS

Porto-sinusoidal vascular disorder (PSVD) is a rare and commonly overlooked cause of portal hypertension. The interest of CT analysis, including quantification of liver surface nodularity (LSN) for PSVD diagnosis has not been established. This study aimed at assessing the performance of LSN and CT features for a PSVD diagnosis in patients with signs of portal hypertension.

APPROACH AND RESULTS

This retrospective case-control study included a learning cohort consisting of 50 patients with histologically proven PSVD, according to VALDIG criteria, and 100 control patients with histologically proven cirrhosis, matched on ascites. All patients and controls had at least one sign of portal hypertension and CT available within 1 year of liver biopsy. Principal component analysis of CT features separated patients with PSVD from patients with cirrhosis. Patients with PSVD had lower median LSN than those with cirrhosis (2.4 vs. 3.1, p < 0.001). Multivariate analysis identified LSN < 2.5 and normal-sized or enlarged segment IV as independently associated with PSVD. Combination of these two features had a specificity of 90% for PSVD and a diagnostic accuracy of 84%. Even better results were obtained in an independent multicenter validation cohort including 53 patients with PSVD and 106 control patients with cirrhosis (specificity 94%, diagnostic accuracy 87%).

CONCLUSIONS

This study that included a total of 103 patients with PSVD and 206 patients with cirrhosis demonstrates that LSN < 2.5 combined with normal-sized or enlarged segment IV strongly suggests PSVD in patients with signs of portal hypertension.

Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram

AIM

To investigate the performance of a generative adversarial network (GAN) model for staging liver fibrosis and its radiomics-based nomogram for predicting cirrhosis.

MATERIALS AND METHODS

This two-centre retrospective study included 434 patients for whom input data of ultrasound images and histopathological data (obtained within 1 month of ultrasound examinations) were assigned to the training cohort (249 patients), the internal cohort (92 patients), and the external (93 patients) cohort. A data augmentation method based on a GAN model was used. The discriminative performance was evaluated for classifying fibrosis of S4 and ≥S3. Deep-learning radiomics features were extracted for the prediction of cirrhosis (S4). To perform feature reduction and selection, the least absolute shrinkage and selection operator (LASSO) algorithm was applied. Radiomics scores, along with clinical factors, were incorporated into a nomogram using multivariable logistic regression analysis. The performance of the models was estimated with respect to discrimination power, calibration, and clinical benefits.

RESULTS

The areas under the receiver operating characteristic curve (AUCs) values of the GAN were 0.832/0.762 (≥S3), and 0.867/0.835 (S4) for internal/external test sets, respectively. The radiomics nomogram that intergrated radiomics scores and clinical factors showed good calibration and discrimination ability of 0.922 (AUC) in the training dataset, 0.896 in the internal dataset, and 0.861 in the external dataset. Decision curve analysis (DCA) demonstrated that the nomogram outperformed radiologist and haematological indices in terms of the most clinical benefits.

CONCLUSIONS

The GAN model could be applied to discriminate fibrosis stages, and a favourable predictive accuracy for diagnosing cirrhosis was achieved using a deep-learning radiomics nomogram.

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.

Development and validation of a management system and dataset quality assessment tool for the Radiology Common Data Model (R_CDM): A case study in liver disease

BACKGROUND

The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM), a distributed research network, has low clinical data coverage. Radiological data are valuable, but imaging metadata are often incomplete, and a standardized recording format in the OMOP-CDM is lacking. We developed a web-based management system and data quality assessment (RQA) tool for a radiology_CDM (R_CDM) and evaluated the feasibility of clinically applying this dataset.

METHODS

We designed an R_CDM with Radiology_Occurrence and Radiology_Image tables. This was seamlessly linked to the OMOP-CDM clinical data. We adopted the standardized terminology using the RadLex playbook and mapped 5,753 radiology protocol terms to the OMOP vocabulary. An extract, transform, and load (ETL) process was developed to extract detailed information that was difficult to extract from metadata and to compensate for missing values. Image-based quantification was performed to measure liver surface nodularity (LSN), using customized Wonkwang abdomen and liver total solution (WALTS) software.

RESULTS

On a PACS, 368,333,676 DICOM files (1,001,797 cases) were converted to R_CDM chronic liver disease (CLD) data (316,596 MR images, 228 cases; 926,753 CT images, 782 cases) and uploaded to a web-based management system. Acquisition date and resolution were extracted accurately, but other information, such as "contrast administration status" and "photography direction", could not be extracted from the metadata. Using WALTS, 9,609 pre-contrast axial-plane abdominal MR images (197 CLD cases) were assigned LSN scores by METAVIR fibrosis grades, which differed significantly by ANOVA (p < 0.001). The mean RQA score (83.5) indicated good quality.

CONCLUSION

This study developed a web-based system for management of the R_CDM dataset, RQA tool, and constructed a CLD R_CDM dataset, with good quality for clinical application. Our management system and R_CDM CLD dataset would be useful for multicentric and image-based quantification researches.

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.

Noncontrast-enhanced MRI-based Noninvasive Score for Portal Hypertension (CHESS1802): An International Multicenter Study

BACKGROUND AND AIMS

This study aimed to determine the performance of the non-invasive score using noncontrast-enhanced MRI (CHESS-DIS score) for detecting portal hypertension in cirrhosis.

METHODS

In this international multicenter, diagnostic study (ClinicalTrials.gov, NCT03766880), patients with cirrhosis who had hepatic venous pressure gradient (HVPG) measurement and noncontrast-enhanced MRI were prospectively recruited from four university hospitals in China (n=4) and Turkey (n=1) between December 2018 and April 2019. A cohort of patients was retrospectively recruited from a university hospital in Italy between March 2015 and November 2017. After segmentation of the liver on fat-suppressed T1-weighted MRI maps, CHESS-DIS score was calculated automatically by an in-house developed code based on the quantification of liver surface nodularity.

RESULTS

A total of 149 patients were included, of which 124 were from four Chinese hospitals (training cohort) and 25 were from two international hospitals (validation cohort). A positive correlation between CHESS-DIS score and HVPG was found with the correlation coefficients of 0.36 (p<0.0001) and 0.55 (p<0.01) for the training and validation cohorts, respectively. The area under the receiver operating characteristic curve of CHESS-DIS score in detection of clinically significant portal hypertension (CSPH) was 0.81 and 0.9 in the training and validation cohorts, respectively. The intraclass correlation coefficients for assessing the inter- and intra-observer agreement were 0.846 and 0.841, respectively.

CONCLUSIONS

A non-invasive score using noncontrast-enhanced MRI was developed and proved to be significantly correlated with invasive HVPG. Besides, this score could be used to detect CSPH in patients with cirrhosis.

Combination of FIB-4 with ultrasound surface nodularity or elastography as predictors of histologic advanced liver fibrosis in chronic liver disease

Reliable and available non-invasive methods for hepatic fibrosis assessment are important in chronic liver disease (CLD). Our aim was to compare stepwise algorithms combining standard ultrasound with serum markers and transient elastography (TE) for detecting advanced fibrosis (F3-4) and cirrhosis. Retrospective single center study between 2012 and 2018 of CLD patients with biopsy, TE, blood tests, and liver ultrasound parameters of surface nodularity (SN), lobar redistribution, and hepatic vein nodularity. Our cohort included 157 patients (51.6% males), mean age 47.6 years, predominantly non-alcoholic fatty liver disease and viral hepatitis (61%), with F3-4 prevalence of 60.5%. Area under the curve for F3-4 was 0.89 for TE ≥ 9.6 kPa and 0.80 for FIB-4 > 3.25. In multivariate modeling, TE ≥ 9.6 kPa (OR 21.78) and SN (OR 3.81) had independent association with F3-4; SN (OR 5.89) and TE ≥ 10.2 kPa (OR 15.73) were independently associated with cirrhosis. Two stepwise approaches included FIB-4 followed by SN or TE; sensitivity and specificity of stepwise SN were 0.65 and 1.00, and 0.89 and 0.33 for TE ≥ 9.6 kPa, respectively. Ultrasound SN and TE were independently predictive of F3-4 and cirrhosis in our cohort. FIB-4 followed by SN had high specificity for F3-4.

Liver surface nodularity on non-contrast MRI identifies advanced fibrosis in patients with NAFLD

OBJECTIVES

To evaluate the diagnostic performance of liver surface nodularity (LSN) for the assessment of advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD).

METHODS

We retrospectively analysed patients with pathologically proven NAFLD who underwent liver MRI. Demographic, clinical, and laboratory data (including FIB-4 scores) were gathered. The SAF score was used to assess NAFLD. MRI-proton density fat fraction (PDFF) and LSN were determined on pre-contrast MR sequences. ROC curve analysis was performed to evaluate the diagnostic performance of MRI-LSN for the diagnosis of advanced (F3-F4) liver fibrosis.

RESULTS

The final population included 142 patients. Sixty-seven (47%) patients had non-alcoholic steatohepatitis (NASH), and 52 (37%) had advanced fibrosis. The median MRI-PDFF increased with the grades of steatosis: 8.1%, 18.1%, and 31% in S1, S2, and S3 patients, respectively (p < 0.001). The area under the ROC curve (AUC) of MRI-LSN ≥ 2.50 was 0.838 (95%CI 0.767-0.894, sensitivity 67.3%, specificity 88.9%, positive and negative predictive values 77.8% and 82.5%, respectively) for the diagnosis of advanced fibrosis. Combining FIB-4 and MRI-LSN correctly classified 103/142 (73%) patients. This was validated in an external cohort of 75 patients.

CONCLUSIONS

MRI-LSN has good diagnostic performance in diagnosis of advanced fibrosis in NAFLD patients. A combination of FIB-4 and MRI-LSN derived from pre-contrast MRI could be helpful to detect advanced fibrosis.

KEY POINTS

• MRI-LSN ≥ 2.5 was accurate for the diagnosis of advanced hepatic fibrosis in NAFLD patients. • The combination of FIB-4 and MRI-LSN improved the detection of advanced fibrosis. • MRI-LSN can be easily derived by unenhanced MRI sequences that are routinely acquired.

EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis - 2021 update

Non-invasive tests are increasingly being used to improve the diagnosis and prognostication of chronic liver diseases across aetiologies. Herein, we provide the latest update to the EASL Clinical Practice Guidelines on the use of non-invasive tests for the evaluation of liver disease severity and prognosis, focusing on the topics for which relevant evidence has been published in the last 5 years.

Non-invasive assessment of cirrhosis using multiphasic dual-energy CT iodine maps: correlation with model for end-stage liver disease score

PURPOSE

To determine whether multiphasic dual-energy (DE) CT iodine quantitation correlates with the severity of chronic liver disease.

METHODS

We retrospectively included 40 cirrhotic and 28 non-cirrhotic patients who underwent a multiphasic liver protocol DECT. All three phases (arterial, portal venous (PVP), and equilibrium) were performed in DE mode. Iodine (I) values (mg I/ml) were obtained by placing regions of interest in the liver, aorta, common hepatic artery, and portal vein (PV). Iodine slopes (λ) were calculated as follows: (I_equilibrium-I_arterial)/time and (I_equilibrium-I_PVP)/time. Spearman correlations between λ and MELD scores were evaluated, and the area under the curve of the receiver operating characteristic (AUROC) was calculated to distinguish cirrhotic and non-cirrhotic patients.

RESULTS

Cirrhotic and non-cirrhotic patients had significantly different λ_{equilibrium-arterial} [IQR] for the caudate (λ = 2.08 [1.39-2.98] vs 1.46 [0.76-1.93], P = 0.007), left (λ = 2.05 [1.50-2.76] vs 1.51 [0.59-1.90], P = 0.002) and right lobes (λ = 1.72 [1.12-2.50] vs 1.13 [0.41-0.43], P = 0.003) and for the PV (λ = 3.15 [2.20-5.00] vs 2.29 [0.85-2.71], P = 0.001). λ_{equilibrium-PVP} were significantly different for the right (λ = 0.11 [- 0.45-1.03] vs - 0.44 [- 0.83-0.12], P = 0.045) and left lobe (λ = 0.30 [- 0.25-0.98] vs - 0.10 [- 0.35-0.24], P = 0.001). Significant positive correlations were found between MELD scores and λ_{equilibrium-arterial} for the caudate lobe (ρ = 0.34, P = 0.004) and λ_{equilibrium-PVP} for the caudate (ρ = 0.26, P = 0.028) and right lobe (ρ = 0.33, P = 0.007). AUROC in distinguishing cirrhotic and non-cirrhotic patients were 0.72 (P = 0.002), 0.71 (P = 0.003), and 0.75 (P = 0.001) using λ_{equilibrium-arterial} for the left lobe, right lobe, and PV, respectively. The λ_{equilibrium-PVP} AUROC of the right lobe was 0.73 (P = 0.001).

CONCLUSION

Multiphasic DECT iodine quantitation over time is significantly different between cirrhotic and non-cirrhotic patients, correlates with the MELD score, and it could potentially serve as a non-invasive measure of cirrhosis and disease severity with acceptable diagnostic accuracy.

Quantification of Pancreas Surface Lobularity on CT: A Feasibility Study in the Normal Pancreas

Objective

To assess the feasibility and reproducibility of pancreatic surface lobularity (PSL) quantification derived from abdominal computed tomography (CT) in a population of patients free from pancreatic disease.

Materials and Methods

This retrospective study included 265 patients free from pancreatic disease who underwent contrast-enhanced abdominal CT between 2017 and 2019. A maximum of 11 individual PSL measurements were performed by two abdominal radiologists (head [5 measurements], body, and tail [3 measurements each]) using dedicated software. The influence of age, body mass index (BMI), and sex on PSL was assessed using the Pearson correlation and repeated measurements. Inter-reader agreement was assessed using the intraclass correlation coefficient (ICC) and Bland Altman (BA) plots.

Results

CT images of 15 (6%) patients could not be analyzed. A total of 2750 measurements were performed in the remaining 250 patients (143 male [57%], mean age 45 years [range, 18–91]), and 2237 (81%) values were obtained in the head 951/1250 (76%), body 609/750 (81%), and tail 677/750 (90%). The mean ± standard deviation PSL was 6.53 ± 1.37. The mean PSL was significantly higher in male than in female (6.89 ± 1.30 vs. 6.06 ± 1.31, respectively, p < 0.001). PSL gradually increased with age (r = 0.32, p < 0.001) and BMI (r = 0.32, p < 0.001). Inter-reader agreement was excellent (ICC 0.82 [95% confidence interval 0.72–0.85], with a BA bias of 0.30 and 95% limits of agreement of −1.29 and 1.89).

Conclusion

CT-based PSL quantification is feasible with a high success rate and inter-reader agreement in subjects free from pancreatic disease. Significant variations were observed according to sex, age, and BMI. This study provides a reference for future studies.

Assessment of Liver Fibrosis Stage Using Integrative Analysis of Hepatic Heterogeneity and Nodularity in Routine MRI with FIB-4 Index as Reference Standard

Image-based quantitative methods for liver heterogeneity (L_Het) and nodularity (L_Nod) provide helpful information for evaluating liver fibrosis; however, their combinations are not fully understood in liver diseases. We developed an integrated software for assessing L_Het and L_Nod and compared L_Het and L_Nod according to fibrosis stages in chronic liver disease (CLD). Overall, 111 CLD patients and 16 subjects with suspected liver disease who underwent liver biopsy were enrolled. The procedures for quantifying L_Het and L_Nod were bias correction, contour detection, liver segmentation, and L_Het and L_Nod measurements. L_Het and L_Nod scores among fibrosis stages (F0–F3) were compared using ANOVA with Tukey’s test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristics (AUROC) curve. The mean L_Het scores of F0, F1, F2, and F3 were 3.49 ± 0.34, 5.52 ± 0.88, 6.80 ± 0.97, and 7.56 ± 1.79, respectively (p < 0.001). The mean L_Nod scores of F0, F1, F2, and F3 were 0.84 ± 0.06, 0.91 ± 0.04, 1.09 ± 0.08, and 1.15 ± 0.14, respectively (p < 0.001). The combined L_Het × L_Nod scores of F0, F1, F2, and F3 were 2.96 ± 0.46, 5.01 ± 0.91, 7.30 ± 0.89, and 8.48 ± 1.34, respectively (p < 0.001). The AUROCs of L_Het, L_Nod, and L_Het × L_Nod for differentiating F1 vs. F2 and F2 vs. F3 were 0.845, 0.958, and 0.954; and 0.619, 0.689, and 0.761, respectively. The combination of L_Het and L_Nod scores derived from routine MR images allows better differential diagnosis of fibrosis subgroups in CLD.

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.

Similar performance of liver stiffness measurement and liver surface nodularity for the detection of portal hypertension in patients with hepatocellular carcinoma

BACKGROUND & AIMS

We compare the performance of liver surface nodularity (LSN) and liver stiffness measurements (LSM) using transient elastography (TE) for the detection of clinically significant portal hypertension (CSPH) in patients with cirrhosis and hepatocellular carcinoma (HCC).

METHODS

All patients with cirrhosis and HCC who underwent computed tomography, LSM and hepatic venous pressure gradient (HVPG) measurements within 30 days between 2015 and 2018 were included. The estimation of CSPH by LSN and LSM, and the LSM-spleen-size-to-platelet ratio score (LSPS) were evaluated and compared.

RESULTS

In total, 140 patients were included (109 men [78%], mean age 63 ± 9 years old), including 39 (28%) with CSPH. LSN measurements were valid in 130 patients (93%) and significantly correlated with HVPG (r = 0.68; p <0.001). Patients with CSPH had higher LSN measurements compared with those without [3.1 ± 0.4 vs. 2.5 ± 0.3, p <0.001; area under the receiver operating characteristic (AUROC): 0.87 ± 0.31]. LSM and LSPS were valid in 132 patients (94%) and significantly correlated with HVPG (r = 0.75, p <0.001; AUROC 0.87 ± 0.04 and r = 0.68, p <0.001; AUROC 0.851 ± 0.04, respectively). There was no significant difference in the diagnostic performance between LSN and LSM-LSPS (DeLong, p = 0.28, 0.37, and 0.65, respectively) in patients with both valid tests (n = 122). LSN <2.50 had a 100% negative predictive value for CSPH. A 2-step algorithm combining LSN and LSPS for the diagnosis of CSPH classified 108/140 patients (77%) with an 8% error.

CONCLUSIONS

The diagnostic performance and feasibility of LSN measurements were similar to those of LSM for the detection of CSPH in patients with compensated cirrhosis and HCC. Combining LSN and LSPS accurately detected CSPH in >75% of patients. Such a combination could be useful in centres where the HVPG measurement is unavailable.

LAY SUMMARY

The diagnostic performance and feasibility of liver surface nodularity was similar to that of liver stiffness measurement (LSM) for the detection of clinically significant portal hypertension in patients with compensated cirrhosis. Thus, liver surface nodularity could be an option for the preoperative detection of clinically significant portal hypertension in patients with hepatocellular carcinoma. Combining liver surface nodularity with LSM-spleen-size-to-platelet ratio score resulted in the accurate detection of clinically significant portal hypertension in >75% of patients, thus limiting the need for HVPG measurements.

Computed Tomography-Derived Liver Surface Nodularity and Sarcopenia as Prognostic Factors in Patients with Resectable Metabolic Syndrome-Related Hepatocellular Carcinoma

OBJECTIVE

The aim of this study was to assess the prognostic value of liver surface nodularity (LSN) and sarcopenia from preoperative computed tomography (CT) in patients with resectable metabolic syndrome (MS)-related hepatocellular carcinoma (HCC).

METHODS

Patients with MS undergoing hepatectomy for HCC between 2006 and 2018 at a single center were retrospectively analyzed. LSN and sarcopenia were assessed on preoperative CT scans, and their association with severe (Clavien-Dindo grade 3-5) postoperative complications was analyzed on multivariate analysis. The influence of LSN and sarcopenia on overall survival (OS) and recurrence-free survival (RFS) was assessed.

RESULTS

Overall, 110 patients (92 men [84%], mean 67.7 ± 7.7 years of age) were analyzed. Severe postoperative complications occurred in 34/110 (31%) patients. Patients with severe complications had a significantly higher LSN score (area under the receiver operating characteristic curve 0.68 ± 0.05, optimal cut-off > 2.50) and were more frequently sarcopenic (47% vs. 13% without major complications, p < 0.001). Multivariate analysis identified sarcopenia (odds ratio [OR] 6.51, 95% confidence interval [CI] 2.08-20.39; p < 0.001), LSN > 2.50 (OR 7.05, 95% CI 2.13-23.35; p < 0.001), and preoperative portal vein embolization (PVE; OR 6.06, 95% CI 1.71-21.48; p = 0.005) as independent predictors of severe complications. LSN and sarcopenia had no influence on OS. Stratification according to a combination of LSN > 2.50 and sarcopenia predicted the risk of severe postoperative complications from 7% (no sarcopenia and LSN ≤2.50) to 71% (sarcopenia and LSN > 2.50; p < 0.001), as well as RFS from 61 months (95% CI 40-82) to 17 months (95% CI 9-25; p = 0.033). Results remained significant in 52 patients without advanced fibrosis.

CONCLUSIONS

The combination of LSN and sarcopenia derived from routine preoperative CT seems to help predict severe postoperative complications and stratification of RFS in patients with MS and resectable HCC.

Staging of Liver Fibrosis by Means of Semiautomatic Measurement of Liver Surface Nodularity in MRI

OBJECTIVE. The purposes of this study were to evaluate the accuracy of a semiautomatic method of measuring liver surface nodularity (LSN) on contrast-enhanced MR images and to compare the LSN score with pathologic fibrosis stage. MATERIALS AND METHODS. This retrospective study included patients who had undergone gadoxetate disodium-enhanced liver MRI 6 months before or after histopathologic investigation including percutaneous parenchymal biopsy and surgical biopsy for staging of chronic liver disease between January 2010 and December 2018. Semiautomated LSN quantification software was developed to measure LSN at MRI. Aspartate aminotransferase to platelet ratio index and fibrosis-4 index were derived from serum laboratory test results. The reference standard for staging of liver fibrosis was Metavir score. The accuracy of LSN score for staging of liver fibrosis was evaluated with AUC, and the optimal cutoff value was calculated by Youden index. Spearman correlation coefficient was used for correlation analysis. RESULTS. The study included 132 patients (93 men, 39 women). LSN score was evaluated without technical failure. There was high correlation between LSN score and Metavir score (Spearman ρ = 0.713, p < 0.001). The AUCs of LSN score for distinguishing Metavir score were 0.93 for F0-F1 versus F2-F4 (95% CI, 0.88-0.97; p < 0.001), 0.98 for F0-F2 vs F3-F4 (95% CI, 0.95-1.00; p < 0.001), and 0.83 for F0-F3 versus F4 (95% CI, 0.76-0.90; p < 0.001). The optimal cutoff value for differentiating F0-F2 from F3-F4 was 0.850 with 100% sensitivity and 85.4% specificity. CONCLUSION. LSN score calculated semiautomatically from MR images of the liver has high accuracy and correlates directly with the pathologic fibrosis stage.

CT-based liver surface nodularity for the detection of clinically significant portal hypertension: defining measurement quality criteria

PURPOSE

To establish measurement quality criteria for the noninvasive assessment of clinically significant portal hypertension (CSPH) in patients with cirrhosis using CT-based liver surface nodularity (LSN) measurements.

METHODS

Seventy-four consecutive patients with cirrhosis (mean 62 ± 13 years), including 30 with CSPH (41%), underwent CT and hepatic venous pressure gradient measurements. Three independent readers performed 15 LSN measurements/patient using dedicated software. LSN was computed based on the median and means of one to 15 measurements. Accuracy for diagnosing CSPH was assessed using receiver operating characteristic (ROC) curve analysis. Variability was assessed by the intra-class correlation coefficient (ICC) and the Bland-Altman plot (BA). Quality criteria were identified to maximize the accuracy of LSN and minimize variability.

RESULTS

The area under the (AU) ROCs of mean and median LSN measurements based on one to 15 measurements ranged from 0.79 ± 0.05 to 0.91 ± 0.04 and 0.86 ± 0.04 to 0.91 ± 0.03, respectively, with no difference on pair-wise comparisons (all p > 0.05). AUROCs of LSN increased from one to eight and leveled off between eight and 15 measurements. Inter- and intra-reader variability decreased from one to 15 measurements, with only slight improvement after more than eight measurements. Intra- and inter-observer agreements were excellent with eight measurements (ICC = 0.90 [95%CI 0.84-0.94], and ICC = 0.93 [95%CI 0.89-0.95], respectively), and variability for intra-observer and inter-observer agreement was low (BA bias 4.2% (95% limits of agreement [LoA] [- 15.3; + 23.7%]) and 4.8% LoA [ - 17.5; + 27.1%], respectively).

CONCLUSIONS

CT-based LSN measurement is highly reproducible and accurate. We suggest using at least 8 valid measurements to determine the mean LSN value for the detection of CSPH.

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).

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

ACR Appropriateness Criteria® Chronic Liver Disease

The liver fibrosis stage is the most important clinical determinate of morbidity and mortality in patients with chronic liver diseases. With newer therapies, liver fibrosis can be stabilized and possibly reversed, thus accurate diagnosis and staging of liver fibrosis are clinically important. Ultrasound, CT, and conventional MRI can be used to establish the diagnosis of advanced fibrosis/cirrhosis but have limited utility for assessing earlier stages of fibrosis. Elastography-based ultrasound and MRI techniques are more useful for assessment of precirrhotic hepatic fibrosis. In patients with advanced fibrosis at risk for hepatocellular carcinoma (HCC), ultrasound is the surveillance modality recommended by international guidelines in nearly all circumstances. However, in patients in whom ultrasound does not assess the liver well, including those with severe steatosis or obesity, multiphase CT or MRI may have a role in surveillance for HCC. Both multiphase CT and MRI can be used for continued surveillance in patients with a history of HCC, and contrast-enhanced ultrasound may have an emerging role in this setting. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

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.

Development of liver surface nodularity quantification program and its clinical application in nonalcoholic fatty liver disease

The liver morphological changes in relation to fibrosis stage in nonalcoholic fatty liver disease (NAFLD) have not yet been clearly understood. This study was to develop a liver surface nodularity (LSN) quantification program and to compare the fibrosis grades in simple steatosis (SS) and nonalcoholic steatohepatitis (NASH). Thirty subjects (7 normal controls [NC], 12 SS and 11 NASH) were studied. LSN quantification procedure was bias correction, boundary detection, segmentation and LSN measurement. LSN scores among three groups and fibrosis grades compared using Kruskal–Wallis H test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristics (ROC) curve. Mean LSN scores were NC 1.30 ± 0.09, SS 1.54 ± 0.21 and NASH 1.59 ± 0.23 (p = 0.008). Mean LSN scores according to fibrosis grade (F) were F0 1.30 ± 0.09, F1 1.45 ± 0.17 and F2&F3 1.67 ± 0.20 (p = 0.001). The mean LSN score in F2&F3 is significantly higher than that in F1 (p = 0.019). The AUROC curve to distinguish F1 and F2&F3 was 0.788 (95% CI 0.595–0.981, p = 0.019) at a cut-off LSN score greater than 1.48, and its diagnostic accuracy had 0.833 sensitivity and 0.727 specificity. This study developed LSN program and its clinical application demonstrated that the quantitative LSN scores can help to differentially diagnose fibrosis stage in NAFLD.

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.

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network

Purpose

To assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.

Methods

We trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.

Results

Dice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).

Conclusions

Utilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization.