Quantification of Liver Fat Content With Unenhanced MDCT: Phantom and Clinical Correlation With MRI Proton Density Fat Fraction

Exploring the correlation between high-risk coronary plaque and hepatic fat fraction in non-alcoholic fatty liver disease using spectral computed tomography (CT)

AIM

To quantitatively assess the fat volume fraction (FVF) in nonalcoholic fatty liver disease (NAFLD) using the spectral computed tomography (CT) multimaterial decomposition (MMD) algorithm and to investigate its association with high-risk coronary plaques (HRP).

MATERIALS AND METHODS

This retrospective study included patients diagnosed with coronary artery disease (CAD) from August 2023 to August 2024 who underwent coronary CT angiography and abdominal enhanced spectral CT imaging. Patients were categorised into three groups based on HRP imaging features (positive remodelling, low-density plaques, spotty calcification, and napkin ring sign): no plaque (n = 57), non-HRP (n = 54), and HRP (n = 48) groups. FVF was measured using the spectral CT MMD algorithm to quantify liver fat content. Clinical characteristics, biochemical markers, and imaging differences among the groups were analysed. Univariate and multivariate logistic regression analyses were performed to determine the association between FVF and HRP.

RESULTS

FVF values were significantly higher in the HRP group (13.2%) compared to the non-HRP group (9.2%) and the no plaque group (6.5%) (P<0.001). Multivariate binary logistic regression analysis identified FVF as an independent risk factor for HRP (odds ratio [OR]: 2.55, P<0.001), along with high-sensitivity C-reactive protein (hs-CRP) (OR: 1.94, P=0.025) and diabetes mellitus (OR: 9.83, P=0.002). Additionally, FVF correlated epicardial and pericoronary adipose tissue (PCAT) volume and CT attenuation (P<0.001).

CONCLUSION

The spectral CT MMD algorithm enables quantitative assessment of FVF, which is independently associated with coronary HRP formation in NAFLD patients. Elevated FVF serves as a risk factor for CAD in patients with NAFLD.

AI-enabled opportunistic measurement of liver steatosis in coronary artery calcium scans predicts cardiovascular events and all-cause mortality: an AI-CVD study within the Multi-Ethnic Study of Atherosclerosis (MESA)

INTRODUCTION

About one-third of adults in the USA have some grade of hepatic steatosis. Coronary artery calcium (CAC) scans contain more information than currently reported. We previously reported new artificial intelligence (AI) algorithms applied to CAC scans for opportunistic measurement of bone mineral density, cardiac chamber volumes, left ventricular mass, and other imaging biomarkers collectively referred to as AI-cardiovascular disease (CVD). In this study, we investigate a new AI-CVD algorithm for opportunistic measurement of liver steatosis.

METHODS

We applied AI-CVD to CAC scans from 5702 asymptomatic individuals (52% female, age 62±10 years) in the Multi-Ethnic Study of Atherosclerosis. Liver attenuation index (LAI) was measured using the percentage of voxels below 40 Hounsfield units. We used Cox proportional hazards regression to examine the association of LAI with incident CVD and mortality over 15 years, adjusted for CVD risk factors and the Agatston CAC score.

RESULTS

A total of 751 CVD and 1343 deaths accrued over 15 years. Mean±SD LAI in females and males was 38±15% and 43±13%, respectively. Participants in the highest versus lowest quartile of LAI had greater incidence of CVD over 15 years: 19% (95% CI 17% to 22%) vs 12% (10% to 14%), respectively, p<0.0001. Individuals in the highest quartile of LAI (Q4) had a higher risk of CVD (HR 1.43, 95% CI 1.08 to 1.89), stroke (HR 1.77, 95% CI 1.09 to 2.88), and all-cause mortality (HR 1.36, 95% CI 1.10 to 1.67) compared with those in the lowest quartile (Q1), independent of CVD risk factors.

CONCLUSION

AI-enabled liver steatosis measurement in CAC scans provides opportunistic and actionable information for early detection of individuals at elevated risk of CVD events and mortality, without additional radiation.

Incremental predictive value of liver fat fraction based on spectral detector CT for major adverse cardiovascular events in T2DM patients with suspected coronary artery disease

BACKGROUND

The purpose of this study was to explore the incremental predictive value of liver fat fraction (LFF) in forecasting major adverse cardiovascular events (MACE) among patients with type 2 diabetes mellitus (T2DM).

METHODS

We prospectively enrolled 265 patients with T2DM who presented to our hospital with symptoms of chest distress and pain suggestive of coronary artery disease (CAD) between August 2021 and August 2022. All participants underwent both coronary computed tomography angiography (CCTA) and upper abdominal dual-layer spectral detector computed tomography (SDCT) examinations within a 7-day interval. Detailed clinical data, CCTA imaging features, and LFF determined by SDCT multi-material decomposition algorithm were meticulously recorded. MACE was defined as the occurrence of cardiac death, acute coronary syndrome (ACS), late-phase coronary revascularization procedures, and hospital admissions due to heart failure.

RESULTS

Among 265 patients (41% male), 51 cases of MACE were documented during a median follow-up of 30 months. The LFF in T2DM patients who experienced MACE was notably higher compared to those without MACE (p < 0.001). The LFF was divided into tertiles using the cutoffs of 4.10 and 8.30. Kaplan-Meier analysis indicated that patients with higher LFF were more likely to develop MACE, regardless of different subgroups in framingham risk score (FRS) or coronary artery calcium score (CACS). The multivariate Cox regression results indicated that, compared with patients in the lowest tertile, those in the second tertile (hazard ratio [HR] = 3.161, 95% confidence interval [CI] 1.163-8.593, P = 0.024) and third tertile (HR = 4.372, 95% CI 1.591-12.014, P = 0.004) had a significantly higher risk of MACE in patients with T2DM. Even after adjusting for early revascularization, both LFF tertile and CACS remained independently associated with MACE. Moreover, compared with the traditional FRS model, the model that included LFF, CACS, and FRS showed stable clinical net benefit and demonstrated better predictive performance, with a C-index of 0.725, a net reclassification improvement (NRI) of 0.397 (95% CI 0.187-0.528, P < 0.01), and an integrated discrimination improvement (IDI) of 0.100 (95% CI 0.043-0.190, P < 0.01).

CONCLUSIONS

The elevated LFF emerged as an independent prognostic factor for MACE in patients with T2DM. Incorporating LFF with FRS and CACS provided incremental predictive power for MACE in patients with T2DM.

RESEARCH INSIGHTS

WHAT IS CURRENTLY KNOWN ABOUT THIS TOPIC?: T2DM is associated with increased MACE rates, underscoring the need for improved risk prediction. CACS is a well-established tool for MACE risk assessment but may not capture all risk factors. Hepatic steatosis is a common comorbidity in metabolic syndrome and T2DM. WHAT IS THE KEY RESEARCH QUESTION?: Does the incorporation of LFF derived from SDCT into existing risk prediction models enhance the accuracy of MACE forecasting in patients with T2DM? WHAT IS NEW?: SDCT-LFF measurement introduces a more accurate method for assessing hepatic steatosis. LFF as an independent predictor of MACE in T2DM patients is a novel finding. The study presents LFF as an additional tool for risk stratification, complementing FRS and CACS. HOW MIGHT THIS STUDY INFLUENCE CLINICAL PRACTICE?: Study findings may guide personalized prevention for T2DM patients at higher MACE risk.

Diagnosis of moderate-to-severe hepatic steatosis using deep learning-based automated attenuation measurements on contrast-enhanced CT

PURPOSE

To evaluate the utility of deep learning-based automated attenuation measurements on contrast-enhanced CT (CECT) for diagnosing moderate-to-severe hepatic steatosis (HS), using histology as reference standard.

METHODS

This retrospective study included 3,620 liver donors (2,393 men and 1,227 women; mean age, 31.7 ± 9.4 years), divided into the development (n = 2,714) and test (n = 906) cohorts. Attenuation values of the liver and spleen on CECT were measured both manually and using a deep learning algorithm (before and after radiologists' correction of segmentation errors). Performance of: (1) liver attenuation and (2) liver-spleen attenuation difference for diagnosing moderate-to-severe HS (> 33%) was assessed using the area under the receiver operating characteristic curve (AUC). Three different criteria targeting 95% sensitivity, 95% specificity, and the maximum Youden's index, respectively, for diagnosing moderate-to-severe HS, were developed and validated.

RESULTS

The performance of deep learning-based measurements did not differ significantly, with or without radiologists' corrections (p = 0.13). Liver-spleen attenuation difference outperformed liver attenuation alone in diagnosing moderate-to-severe HS in both deep learning-based (AUC, 0.868 vs. 0.821; p = 0.001) and manual (AUC, 0.871 vs. 0.823; p = 0.001) measurements. In the test cohort, the criterion targeting 95% sensitivity for diagnosing moderate-to-severe HS (liver-spleen attenuation difference ≤ 2.8 HU) yielded 92.0% (69/75) sensitivity and 48.5% (403/831) specificity. The criterion targeting 95% specificity (liver-spleen attenuation difference ≤ -18.8 HU) yielded 53.3% (40/75) sensitivity and 95.7% (795/831) specificity. The criterion targeting the maximum Youden's index (liver-spleen attenuation difference ≤ -8.2 HU) yielded 82.7% (62/75) sensitivity and 80.7% (671/831) specificity.

CONCLUSION

Deep learning-based automated measurements of liver and spleen attenuation on CECT can be used reliably to detect moderate-to-severe HS.

Using Incidental Radiologic Findings of Hepatic Steatosis to Improve the Diagnosis of Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common cause of liver disease worldwide. In patients with MASLD, liver fibrosis stage is the most significant predictor of mortality; therefore, early identification of patients at the greatest risk of advanced fibrosis is essential. Noninvasive tests predict advanced fibrosis and are recommended for use in primary care settings to determine which patients would benefit most from specialty care. The adoption of these tools is not widespread, and several studies have reported underrecognition of cirrhosis in patients with MASLD and diabetes. The finding of hepatic steatosis on imaging performed for evaluation of nonliver conditions may present an avenue for opportunistic screening to identify more patients with MASLD. This article will review recommendations for when hepatic steatosis is found on imaging and noninvasive tests that can be used to help predict fibrosis staging. This is a significant area of research because a new treatment for MASLD has been approved, and other treatments may follow.

Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals

BACKGROUND

Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.

PURPOSE

To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).

MATERIAL AND METHODS

We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.4 ± 5.0 years; 41.2% female) at baseline and on the 1-year follow-up scan if no cancer was detected. SLD was defined as hepatic fat fraction (HFF) ≥5% derived from Hounsfield unit measures of the segmented liver. Participants with SLD were categorized as lean (body mass index [BMI] < 25 kg/m2) and overweight (BMI ≥ 25 kg/m2). The primary outcome was all-cause mortality. Cox proportional hazard regression assessed the association between (1) SLD and mortality at baseline and (2) the association between a change in HFF and mortality within 1 year.

RESULTS

There were 5.1% (1000/19,760) all-cause deaths over a median follow-up of 6 (range, 0.8-6) years. At baseline, SLD was associated with increased mortality in lean but not in overweight/obese participants as compared to participants without SLD (hazard ratio [HR] adjusted for risk factors: 1.93 [95% confidence interval 1.52-2.45]; p = 0.001). Individuals with an increase in HFF within 1 year had a significantly worse outcome than participants with stable HFF (HR adjusted for risk factors: 1.29 [1.01-1.65]; p = 0.04).

CONCLUSION

SLD is an independent predictor for long-term mortality in heavy smokers beyond known clinical risk factors.

Triglyceride to High-Density Lipoprotein Cholesterol Ratio as a Marker of Subclinical Coronary Atherosclerosis and Hepatic Steatosis in Familial Hypercholesterolemia

OBJECTIVE

Features of the cardiometabolic syndrome are prevalent in patients with familial hypercholesterolemia (FH). Triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, a surrogate marker of insulin resistance, may be a robust predictor of cardiac events in the general population. We explored the association between TG/HDL-C ratio and high-risk coronary artery plaque (HRP) and hepatic steatosis (HS) in asymptomatic patients with FH.

METHODS

We conducted a cross-sectional study of 290 patients (mean age = 49 years, 44% male) who underwent computed tomography coronary angiography for cardiovascular risk assessment. HRP and HS were assessed from computed tomography coronary angiography, and TG/HDL-C ratio was derived from the fasting lipid panel collected around time of scanning. Associations were assessed using binary logistic and Kaplan-Meier analysis.

RESULTS

TG/HDL-C ratio was significantly associated with HRP (odds ratio, 1.27; 95% CI, 1.04-1.56; P = .020) and HS (odds ratio, 1.71; 95% CI, 1.17-2.51; P = .005) after adjusting for age, body mass index, smoking, and coronary calcium score. TG/HDL-C ratio was associated with HRP in patients treated with lipid-lowering medications (P = .042) and inclusion in a predictive model outperformed the FH-Risk-Score (area under receiver operating characteristic 0.74 vs 0.63; P = .004). An elevated TG/HDL-C ratio predicted myocardial infarction or coronary revascularization over a median follow-up of 91 months with 10 cardiac events recorded (P = .043). TG/HDL-C ratio was strongly positively correlated (P < .001 for all) with markers of cardiometabolic dysfunction: lipid accumulation product (r = 0.81), visceral adiposity index (r = 0.96), and triglyceride-glucose index (r = 0.91).

CONCLUSION

TG/HDL-C ratio was strongly associated with HRP, HS, and cardiac events in patients with FH treated with long-term cholesterol-lowering therapy.

Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT

PURPOSE

This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver.

MATERIALS AND METHODS

The study retrospectively enrolled 840 individuals who underwent non-contrast abdominal CT and quantitative CT (QCT) examinations at the First Affiliated Hospital of Zhengzhou University from July 2022 to May 2023. Subsequently, these participants were divided into a training set (n = 539) and a testing set (n = 301) in a 9:5 ratio. The liver fat content measured by experienced radiologists using QCT technology served as the reference standard. The liver images from the non-contrast abdominal CT scans were then segmented as regions of interest (ROI) from which radiomics features were extracted. Two-dimensional (2D) and three-dimensional (3D) radiomics models, as well as 2D and 3D deep learning models, were developed, and machine learning models based on clinical data were constructed for the four-category diagnosis of fatty liver. The characteristic curves for each model were plotted, and area under the receiver operating characteristic curve (AUC) were calculated to assess their efficacy in the classification and diagnosis of fatty liver.

RESULTS

A total of 840 participants were included (mean age 49.1 years ± 11.5 years [SD]; 581 males), of whom 610 (73%) had fatty liver. Among the patients with fatty liver, there were 302 with mild fatty liver (CT fat fraction of 5%-14%), 155 with moderate fatty liver (CT fat fraction of 14%-28%), and 153 with severe fatty liver (CT fat fraction >28%). Among all models used for diagnosing fatty liver, the 2D radiomics model based on the random forest algorithm achieved the highest AUC (0.973), while the 2D radiomics model based on the Bagging decision tree algorithm showed the highest sensitivity (0.873), specificity (0.939), accuracy (0.864), precision (0.880), and F1 score (0.876).

CONCLUSION

A systematic comparison was conducted on the performance of 2D and 3D radiomics models, as well as deep learning models, in the diagnosis of four-category fatty liver. This comprehensive model comparison provides a broader perspective for determining the optimal model for liver fat diagnosis. It was found that the 2D radiomics models based on the random forest and Bagging decision tree algorithms show high consistency with the QCT-based classification diagnosis of fatty liver used by experienced radiologists.

Advanced Imaging Techniques for Assessing Fat, Iron, and Fibrosis in Chronic Liver Disease

Imaging plays a critical role in the management of chronic liver disease (CLD) because it is a safe and painless method to assess liver health. The widely used imaging techniques include ultrasound, computed tomography and magnetic resonance imaging. These techniques allow the measurement of fat deposition, iron content, and fibrosis, replacing invasive liver biopsies in many cases. Early detection and treatment of fibrosis are crucial, as the disease can be reversed in its early stages. Imaging also aids in guiding treatment decisions and monitoring disease progression. In this review, we describe the most common imaging manifestations of liver disease and the current state-of-the-art imaging techniques for the evaluation of liver fat, iron, and fibrosis.

Noninvasive Quantitative CT for Diffuse Liver Diseases: Steatosis, Iron Overload, and Fibrosis

Chronic diffuse liver disease continues to increase in prevalence and represents a global health concern. Noninvasive detection and quantification of hepatic steatosis, iron overload, and fibrosis are critical, especially given the many relative disadvantages and potential risks of invasive liver biopsy. Although MRI techniques have emerged as the preferred reference standard for quantification of liver fat, iron, and fibrosis, CT can play an important role in opportunistic detection of unsuspected disease and is performed at much higher volumes. For hepatic steatosis, noncontrast CT provides a close approximation to MRI-based proton-density fat fraction (PDFF) quantification, with liver attenuation values less than or equal to 40 HU signifying at least moderate steatosis. Liver fat quantification with postcontrast CT is less precise but can generally provide categorical assessment (eg, mild vs moderate steatosis). Noncontrast CT can also trigger appropriate assessment for iron overload when increased parenchymal attenuation values are observed (eg, >75 HU). A variety of morphologic and functional CT features indicate the presence of underlying hepatic fibrosis and cirrhosis. Beyond subjective assessment, quantitative CT methods for staging fibrosis can provide comparable performance to that of elastography. Furthermore, quantitative CT assessment can be performed retrospectively, since prospective techniques are not required. Many of these CT quantitative measures are now fully automated via artificial intelligence (AI) deep learning algorithms. These retrospective and automated advantages have important implications for longitudinal clinical care and research. Ultimately, regardless of the indication for CT, opportunistic detection of steatosis, iron overload, and fibrosis can result in appropriate clinical awareness and management. ©RSNA, 2024 See the invited commentary by Yeh in this issue.

Diffuse and focal liver fat: advanced imaging techniques and diagnostic insights

The fatty liver disease represents a complex, multifaceted challenge, requiring a multidisciplinary approach for effective management and research. This article uses conventional and advanced imaging techniques to explore the etiology, imaging patterns, and quantification methods of hepatic steatosis. Particular emphasis is placed on the challenges and advancements in the imaging diagnostics of fatty liver disease. Techniques such as ultrasound, CT, MRI, and elastography are indispensable for providing deep insights into the liver's fat content. These modalities not only distinguish between diffuse and focal steatosis but also help identify accompanying conditions, such as inflammation and fibrosis, which are critical for accurate diagnosis and management.

CT assessment of liver fat fraction and abdominal fat composition can predict postoperative liver metastasis of colorectal cancer

OBJECTIVE

The aim of this study is to investigate the clinical value of liver fat fraction assessed by CT(CT-LFF) and abdominal fat components. We focus on predicting liver metastasis (LM) after colorectal cancer (CRC) surgery.

METHODS

Clinical and imaging data from 79 patients who underwent radical CRC surgery between January 2019 and December 2021 were retrospectively collected. Semi-automatic software was used to quantify the area of different body tissues at the level of the third lumbar vertebra, and liver fat fraction was calculated based on the CT values. Patients were grouped according to BMI, tumor grade, T stage, N stage, vascular invasion (VI), perineural invasion (PNI), and preoperative levels of CEA and CA199. A multivariate logistic regression model was used to identify independent risk factors for early LM after surgery. The diagnostic performance was assessed using the receiver operating characteristic analysis with 5-fold cross-validation. The Kaplan-Meier method was used to draw survival curves, and Log-Rank test was used for survival analysis.

RESULTS

The study found that the occurrence of LM after CRC surgery was significantly associated with CA199 positivity, VI, PNI, N1-2 stage, CT-LFF, VAT index (VATI). Multivariate logistic regression analysis showed that CA199 positivity (OR = 7.659), N1-2 stage (OR = 6.394), CT-LFF (OR = 1.271), VATI (OR = 1.043) were independent risk factors for predicting LM after CRC surgery. The multivariate logistic regression model, constructed using these independent risk factors, demonstrated robust predictive performance across 5-fold cross-validations, with an average AUC of 0.898 (95 % CI: 0.828-0.969). Survival analysis showed a significant difference in liver metastasis-free survival rates between the high-risk and low-risk groups (P < 0.001).

CONCLUSION

CT-LFF and VATI assessed by CT are independent risk factors for predicting LM after CRC surgery. The multivariate prediction model combining CA199 and N stage shows high predictive performance.

Diagnostic Accuracy of CT for the Detection of Hepatic Steatosis: A Systematic Review and Meta-Analysis

Background CT plays an important role in the opportunistic identification of hepatic steatosis. CT performance for steatosis detection has been inconsistent across various studies, and no clear guidelines on optimum thresholds have been established. Purpose To conduct a systematic review and meta-analysis to assess CT diagnostic accuracy in hepatic steatosis detection and to determine reliable cutoffs for the commonly mentioned measures in the literature. Materials and Methods A systematic search of the PubMed, Embase, and Scopus databases (English-language studies published from September 1977 to January 2024) was performed. Studies evaluating the diagnostic accuracy of noncontrast CT (NCCT), contrast-enhanced (CECT), and dual-energy CT (DECT) for hepatic steatosis detection were included. Reference standards included biopsy, MRI proton density fat fraction (PDFF), or NCCT. In several CECT and DECT studies, NCCT was used as the reference standard, necessitating subgroup analysis. Statistical analysis included a random-effects meta-analysis, assessment of heterogeneity with use of the I2 statistic, and meta-regression to explore potential sources of heterogeneity. When available, mean liver attenuation, liver-spleen attenuation difference, liver to spleen attenuation ratio, and the DECT-derived fat fraction for hepatic steatosis diagnosis were assessed. Results Forty-two studies (14 186 participants) were included. NCCT had a sensitivity and specificity of 72% and 88%, respectively, for steatosis (>5% fat at biopsy) detection and 82% and 94% for at least moderate steatosis (over 20%-33% fat at biopsy) detection. CECT had a sensitivity and specificity of 66% and 90% for steatosis detection and 68% and 93% for at least moderate steatosis detection. DECT had a sensitivity and specificity of 85% and 88% for steatosis detection. In the subgroup analysis, the sensitivity and specificity for detecting steatosis were 80% and 99% for CECT and 84% and 93% for DECT. There was heterogeneity among studies focusing on CECT and DECT. Liver attenuation less than 40-45 HU, liver-spleen attenuation difference less than -5 to 0 HU, and liver to spleen attenuation ratio less than 0.9-1 achieved high specificity for detection of at least moderate steatosis. Conclusion NCCT showed high performance for detection of at least moderate steatosis. © RSNA, 2024 Supplemental material is available for this article.

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.

Positron emission tomography combined with serum biomarkers detects fibrotic MASH

Metabolic dysfunction-associated steatohepatitis (MASH) is a rising global disease signaling the urgent need for non-invasive tests (NITs). Recent work demonstrated that dynamic 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) imaging can identify MASH by measuring liver glucose transport rate, K1, and liver CT attenuation. By combining dynamic PET/CT with the serum-based fibrosis-4 (FIB-4) test, we were able to better distinguish clinical MASH from fibrotic subtypes, enabling determination of the core tenets of MASH: steatosis, inflammation, and fibrosis. Future studies using FDG-PET technology can further enable concomitant prediction of MASH severity and extrahepatic comorbidities such as cardiovascular disease.

Utilizing fully-automated 3D organ segmentation for hepatic steatosis assessment with CT attenuation-based parameters

OBJECTIVES

To investigate the clinical utility of fully-automated 3D organ segmentation in assessing hepatic steatosis on pre-contrast and post-contrast CT images using magnetic resonance spectroscopy (MRS)-proton density fat fraction (PDFF) as reference standard.

MATERIALS AND METHODS

This retrospective study analyzed 362 adult potential living liver donors with abdominal CT scans and MRS-PDFF. Using a deep learning-based tool, mean volumetric CT attenuation of the liver and spleen were measured on pre-contrast (liver(L)_pre and spleen(S)_pre) and post-contrast (L_post and S_post) images. Agreements between volumetric and manual region-of-interest (ROI)-based measurements were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. Diagnostic performances of volumetric parameters (L_pre, liver-minus-spleen (L-S)_pre, L_post, and L-S_post) were evaluated for detecting MRS-PDFF ≥ 5% and ≥ 10% using receiver operating characteristic (ROC) curve analysis and compared with those of ROI-based parameters.

RESULTS

Among the 362 subjects, 105 and 35 had hepatic steatosis with MRS-PDFF ≥ 5% and ≥ 10%, respectively. Volumetric and ROI-based measurements revealed ICCs of 0.974, 0.825, 0.992, and 0.962, with mean differences of -4.2 HU, -3.4 HU, -1.2 HU, and -7.7 HU for L_pre, S_pre, L_post, and S_post, respectively. Volumetric L_pre, L-S_pre, L_post, and L-S_post yielded areas under the ROC curve of 0.813, 0.813, 0.734, and 0.817 for MRS-PDFF ≥ 5%; and 0.901, 0.915, 0.818, and 0.868 for MRS-PDFF ≥ 10%, comparable with those of ROI-based parameters (0.735-0.818; and 0.816-0.895, Ps = 0.228-0.911).

CONCLUSION

Automated 3D segmentation of the liver and spleen in CT scans can provide volumetric CT attenuation-based parameters to detect and grade hepatic steatosis, applicable to pre-contrast and post-contrast images.

CLINICAL RELEVANCE STATEMENT

Volumetric CT attenuation-based parameters of the liver and spleen, obtained through automated segmentation tools from pre-contrast or post-contrast CT scans, can efficiently detect and grade hepatic steatosis, making them applicable for large population data collection.

KEY POINTS

• Automated organ segmentation enables the extraction of CT attenuation-based parameters for the target organ. • Volumetric liver and spleen CT attenuation-based parameters are highly accurate in hepatic steatosis assessment. • Automated CT measurements from pre- or post-contrast imaging show promise for hepatic steatosis screening in large cohorts.

Photon-counting Detector CT for Liver Fat Quantification: Validation across Protocols in Metabolic Dysfunction-associated Steatotic Liver Disease

Background Traditional energy-integrating detector CT has limited utility in accurately quantifying liver fat due to protocol-induced CT value shifts, but this limitation can be addressed by using photon-counting detector (PCD) CT, which allows for a standardized CT value. Purpose To develop and validate a universal CT to MRI fat conversion formula to enhance fat quantification accuracy across various PCD CT protocols relative to MRI proton density fat fraction (PDFF). Materials and Methods In this prospective study, the feasibility of fat quantification was evaluated in phantoms with various nominal fat fractions. Five hundred asymptomatic participants and 157 participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) were enrolled between September 2023 and March 2024. Participants were randomly assigned to six groups with different CT protocols regarding tube voltage (90, 120, or 140 kVp) and radiation dose (standard or low). Of the participants in the 120-kVp standard-dose asymptomatic group, 51% (53 of 104) were designated as the training cohort, with the rest of the asymptomatic group serving as the validation cohort. A CT to MRI fat quantification formula was derived from the training cohort to estimate the CT-derived fat fraction (CTFF). CTFF agreement with PDFF and its error were evaluated in the asymptomatic validation cohort and subcohorts stratified by tube voltage, radiation dose, and body mass index, and in the MASLD cohort. The factors influencing CTFF error were further evaluated. Results In the phantoms, CTFF showed excellent agreement with nominal fat fraction (intraclass correlation coefficient, 0.98; mean bias, 0.2%). A total of 412 asymptomatic participants and 122 participants with MASLD were included. A CT to MRI fat conversion formula was derived as follows: MRI PDFF (%) = -0.58 · CT (HU) + 43.1. Across all comparisons, CTFF demonstrated excellent agreement with PDFF (mean bias values < 1%). CTFF error was not influenced by tube voltage, radiation dose, body mass index, or PDFF. Agreement between CTFF and PDFF was also found in the MASLD cohort (mean bias, -0.2%). Conclusion Standardized CT value from PCD CT showed a robust and remarkable agreement with MRI PDFF across various protocols and may serve as a precise alternative for liver fat quantification. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Wildman-Tobriner in this issue.

Phantoms for Quantitative Body MRI: a review and discussion of the phantom value

In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.

Automated Comprehensive CT Assessment of the Risk of Diabetes and Associated Cardiometabolic Conditions

Background CT performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored. Purpose To evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities. Materials and Methods This retrospective study included Korean adults (age ≥ 25 years) who underwent health screening with fluorine 18 fluorodeoxyglucose PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral and subcutaneous fat, muscle, bone density, liver fat, all normalized to height (in meters squared), and aortic calcification. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively. Results The cross-sectional and cohort analyses included 32166 (mean age, 45 years ± 6 [SD], 28833 men) and 27 298 adults (mean age, 44 years ± 5 [SD], 24 820 men), respectively. Diabetes prevalence and incidence was 6% at baseline and 9% during the 7.3-year median follow-up, respectively. Visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUC of 0.70 (95% CI: 0.68, 0.71) for men and 0.82 (95% CI: 0.78, 0.85) for women and C-index of 0.68 (95% CI: 0.67, 0.69) for men and 0.82 (95% CI: 0.77, 0.86) for women, respectively. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance, yielding C-indexes of 0.69 (95% CI: 0.68, 0.71) for men and 0.83 (95% CI: 0.78, 0.87) for women. The AUC for visceral fat index in identifying metabolic syndrome was 0.81 (95% CI: 0.80, 0.81) for men and 0.90 (95% CI: 0.88, 0.91) for women. CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores greater than 100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95. Conclusion Automated multiorgan CT analysis identified individuals at high risk of diabetes and other cardiometabolic comorbidities. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Pickhardt in this issue.

Grading of Fatty Liver Based on Computed Tomography Hounsfield Unit Values versus Ultrasonography Grading

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) ranges from hepatic steatosis to nonalcoholic steatohepatitis and may lead to liver cirrhosis. This study aimed to assess the feasibility of numerical grading MASLD using noncontrast computed tomography (NCCT). Methods: In a retrospective study of 166 patients diagnosed with MASLD between June 2020 and January 2024, MASLD was graded by ultrasonography, and liver density was measured on NCCT. The MASLD grades and NCCT densities were compared. Results: The MASLD grades were distributed as follows: grade 0 (n = 79, 47.6%), grade 2 (n = 48, 28.9%), grade 1 (n = 25, 15.1%), and grade 3 (n = 14, 8.4%). The mean liver density was 57.75 Hounsfield units (HU) ± 6.18 (range: 48.9–78.2), 51.1 HU ± 4.7 (range: 41.4–59.7), 39.3 ± 6.4 (range: 21.4–48.9), and 22.87 ± 7.5 (range: 12–36.4) in the grade 0, grade 1, grade 2, and grade 3 patients, respectively. An analysis of variance test showed significant variance in the distribution of mean liver density in the different MASLD grades (p < 0.001). Conclusions: After ultrasonography diagnosis of MASLD, NCCT offers an objective, numerical, and calculable method for MASLD grading that is available for radiologists, radiologic technologists, and interested physicians away from experience dependence. NCCT determined that grade 2 had a specific density from 36.4 to 41.4 HU that significantly overlapped with grade 1 (41.4–48.9) HU and with grade 3 (21.4–36.4 HU). Grade 1 showed a significant overlap with the normal liver (48.9–59.7 HU).

Automated hepatic steatosis assessment on dual-energy CT-derived virtual non-contrast images through fully-automated 3D organ segmentation

PURPOSE

To evaluate the efficacy of volumetric CT attenuation-based parameters obtained through automated 3D organ segmentation on virtual non-contrast (VNC) images from dual-energy CT (DECT) for assessing hepatic steatosis.

MATERIALS AND METHODS

This retrospective study included living liver donor candidates having liver DECT and MRI-determined proton density fat fraction (PDFF) assessments. Employing a 3D deep learning algorithm, the liver and spleen were automatically segmented from VNC images (derived from contrast-enhanced DECT scans) and true non-contrast (TNC) images, respectively. Mean volumetric CT attenuation values of each segmented liver (L) and spleen (S) were measured, allowing for liver attenuation index (LAI) calculation, defined as L minus S. Agreements of VNC and TNC parameters for hepatic steatosis, i.e., L and LAI, were assessed using intraclass correlation coefficients (ICC). Correlations between VNC parameters and MRI-PDFF values were assessed using the Pearson's correlation coefficient. Their performance to identify MRI-PDFF ≥ 5% and ≥ 10% was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Of 252 participants, 56 (22.2%) and 16 (6.3%) had hepatic steatosis with MRI-PDFF ≥ 5% and ≥ 10%, respectively. LVNC and LAIVNC showed excellent agreement with LTNC and LAITNC (ICC = 0.957 and 0.968) and significant correlations with MRI-PDFF values (r = - 0.585 and - 0.588, Ps < 0.001). LVNC and LAIVNC exhibited areas under the ROC curve of 0.795 and 0.806 for MRI-PDFF ≥ 5%; and 0.916 and 0.932, for MRI-PDFF ≥ 10%, respectively.

CONCLUSION

Volumetric CT attenuation-based parameters from VNC images generated by DECT, via automated 3D segmentation of the liver and spleen, have potential for opportunistic hepatic steatosis screening, as an alternative to TNC images.

Cross-sectional imaging after pancreatic surgery: The dialogue between the radiologist and the surgeon

Pancreatic surgery is nowadays considered one of the most complex surgical approaches and not unscathed from complications. After the surgical procedure, cross-sectional imaging is considered the non-invasive reference standard to detect early and late compilations, and consequently to address patients to the best management possible. Contras-enhanced computed tomography (CECT) should be considered the most important and useful imaging technique to evaluate the surgical site. Thanks to its speed, contrast, and spatial resolution, it can help reach the final diagnosis with high accuracy. On the other hand, magnetic resonance imaging (MRI) should be considered as a second-line imaging approach, especially for the evaluation of biliary findings and late complications. In both cases, the radiologist should be aware of protocols and what to look at, to create a robust dialogue with the surgeon and outline a fitted treatment for each patient.

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT

Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.

Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders

BACKGROUND

Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders.

METHODS

A multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease.

RESULTS

For metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77).

CONCLUSIONS

This study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.

Can absolute arterial phase hyperenhancement improve sensitivity of detection of hepatocellular carcinoma in indeterminate nodules on CT?

OBJECTIVES

To determine if quantitative assessment of relative (R) and absolute (A) arterial phase hyperenhancement (APHE) and washout (WO) applied to indeterminate nodules on CT would improve the overall sensitivity of detection of hepatocellular carcinoma (HCC).

METHODS

One-hundred and fourteen patients (90 male; mean age, 65 years) with 210 treatment-naïve HCC nodules (190 HCCs, 20 benign) who underwent 4-phase CT were included in this retrospective study. Four radiologists independently assigned a qualitative LR (LI-RADS) category per nodule. LR-3/4 nodules were then quantitatively analyzed by the 4 readers, placing ROIs within nodules and adjacent liver parenchyma. A/R-APHE and WO were calculated, and per-reader sensitivity and specificity updated. Interobserver agreement and AUCs were calculated per reader.

RESULTS

Qualitative readers 1-4 categorized 57, 69, 57, and 63 nodules as LR-3/4 respectively with moderate to substantial agreement in LR category (kappa 0.56-0.69, p < 0.0001); their diagnostic performances in the detection of HCC were 80%, 73.2%, 77.4%, and 77.4% sensitivity, and 100%, 95%, 70%, and 100% specificity, respectively. A threshold of ≥ 20 HU for A-APHE increased overall sensitivity of HCC detection by 0.5-3.1% without changing specificity for the subset of nodules APHE - /WO + on qualitative read, with 2, 6, 6, and 1 additional HCC detected by readers 1-4. Relative and various A-WO formulae and thresholds all increased sensitivity, but with a drop in specificity for some/all readers.

CONCLUSION

Quantitatively assessed A-APHE showed potential to increase sensitivity and maintain specificity of HCC diagnosis when selectively applied to indeterminate nodules demonstrating WO without subjective APHE. Quantitatively assessed R and A-WO increased sensitivity, however reduced specificity.

CLINICAL RELEVANCE STATEMENT

A workflow using selective quantification of absolute arterial enhancement is routinely employed in the CT assessment of renal and adrenal nodules. Quantitatively assessed absolute arterial enhancement is a simple tool which may be used as an adjunct to help increase sensitivity and maintain specificity of HCC diagnosis in indeterminate nodules demonstrating WO without subjective APHE.

KEY POINTS

• In indeterminate nodules categorized as LI-RADS 3/4 due to absent subjective arterial phase hyperenhancement, a cut-off for absolute arterial phase hyperenhancement of ≥ 20 HU may increase the overall sensitivity of detection of HCC by 0.5-3.1% without affecting specificity. • Relative and various absolute washout formulae and cut-offs increased sensitivity of HCC detection, but with a drop in specificity for some/all readers.

Detection of moderate hepatic steatosis on contrast-enhanced dual-source dual-energy CT: Role and accuracy of virtual non-contrast CT

PURPOSE

To investigate diagnostic accuracy of virtual non contrast (VNC) images, based on dual-source dual-energy CT (dsDECT), for detection of at least moderate steatosis and to define a threshold value to make this diagnosis on VNC.

METHODS

This single-institution retrospective study included patients who had multi-phasic protocol dsDECT. Regions of interests were placed in different segments of the liver and spleen on true non-contrast (TNC), VNC, and portal-venous phase (PVP) images. At least moderate steatosis was defined as liver attenuation (LHU) < 40 HU on TNC. Diagnostic performance of VNC to detect steatosis was determined and the new threshold was tested in a validation cohort.

RESULTS

236 patients were included in training cohort. Mean liver attenuation values were 51.3 ± 10.8 HU and 58.1 ± 11.5 HU for TNC and VNC (p < 0.001), with a mean difference (VNC - TNC) of 6.8 ± 6.9 HU. Correlation between TNC and VNC was strong (r = 0.81, p < 0.001). The AUCs of LHU on VNC for detection of hepatic steatosis were 0.92 (95 % Cl: 0.86-0.98), 0.92 (95 % Cl: 0.87-0.97), 0.92 (95 % Cl: 0.86-0.99), 0.91 (95 % Cl: 0.84-0.97), and 0.87 (95 % Cl: 0.80-0.95) for entire liver, left lateral, left medial, right anterior, and right posterior segments, respectively. VNC had sensitivity/specificity of 100 % /42 % when using a threshold of 40 HU; they were 69 % and 95 %, respectively, when using optimized threshold of 46 HU. This threshold showed similar performance in validation cohort (n = 80).

CONCLUSIONS

Hepatic attenuation on VNC has promising performance for detection of at least moderate steatosis. Proposed threshold of 46 HU provides high specificity and moderate sensitivity to detect steatosis.

Liver fat volume fraction measurements based on multi-material decomposition algorithm in patients with nonalcoholic fatty liver disease: the influences of blood vessel, location, and iodine contrast

BACKGROUND

In recent years, spectral CT-derived liver fat quantification method named multi-material decomposition (MMD) is playing an increasingly important role as an imaging biomarker of hepatic steatosis. However, there are various measurement ways with various results among different researches, and the impact of measurement methods on the research results is unknown. The aim of this study is to evaluate the reproducibility of liver fat volume fraction (FVF) using MMD algorithm in nonalcoholic fatty liver disease (NAFLD) patients when taking blood vessel, location, and iodine contrast into account during measurement.

METHODS

This retrospective study was approved by the institutional ethics committee, and the requirement for informed consent was waived because of the retrospective nature of the study. 101 patients with NAFLD were enrolled in this study. Participants underwent non-contrast phase (NCP) and two-phase enhanced CT scanning (late arterial phase (LAP) and portal vein phase (PVP)) with spectral mode. Regions of interest (ROIs) were placed at right posterior lobe (RPL), right anterior lobe (RAL) and left lateral lobe (LLL) to obtain FVF values on liver fat images without and with the reference of enhanced CT images. The differences of FVF values measured under different conditions (ROI locations, with/without enhancement reference, NCP and enhanced phases) were compared. Friedman test was used to compare FVF values among three phases for each lobe, while the consistency of FVF values was assessed between each two phases using Bland-Altman analysis.

RESULTS

Significant difference was found between FVF values obtained without and with the reference of enhanced CT images. There was no significant difference about FVF values obtained from NCP images under the reference of enhanced CT images between any two lobes or among three lobes. The FVF value increased after the contrast injection, and there were significant differences in the FVF values among three scanning phases. Poor consistencies of FVF values between each two phases were found in each lobe by Bland-Altman analysis.

CONCLUSION

MMD algorithm quantifying hepatic fat was reproducible among different lobes, while was influenced by blood vessel and iodine contrast.

Metabolic syndrome: imaging features and clinical outcomes

Metabolic syndrome, which affects around a quarter of adults worldwide, is a group of metabolic abnormalities characterized mainly by insulin resistance and central adiposity. It is strongly correlated with cardiovascular and all-cause mortality. Early identification of the changes induced by metabolic syndrome in target organs and timely intervention (eg, weight reduction) can decrease morbidity and mortality. Imaging can monitor the main components of metabolic syndrome and identify early the development and progression of its sequelae in various organs. In this review, we discuss the imaging features across different modalities that can be used to evaluate changes due to metabolic syndrome, including fatty deposition in different organs, arterial stiffening, liver fibrosis, and cardiac dysfunction. Radiologists can play a vital role in recognizing and following these target organ injuries, which in turn can motivate lifestyle modification and therapeutic intervention.

Photon-counting CT using multi-material decomposition algorithm enables fat quantification in the presence of iron deposits

PURPOSE

A new generation of CT detectors were recently developed with the ability to measure individual photon's energy and thus provide spectral information. The aim of this work was to assess the performance of simultaneous fat and iron quantification using a clinical photon-counting CT (PCCT) and its comparison to dual-energy CT (DECT), MRS and MRI at 3 T.

METHODS

Two 3D printed cylindrical phantoms with 32 samples (n = 12 fat fractions between 0 % and 100 %, n = 20 with mixtures of fat and iron) were scanned with PCCT and DECT scanners for comparison. A three-material decomposition approach was used to estimate the volume fractions of fat (FF), iron and soft tissue. The same phantoms were examined by MRI (6-echo DIXON, a.k.a. Q-DIXON) and MRS (multi-echo STEAM, a.k.a. HISTO) at 3 T for comparison.

RESULTS

PCCT, DECT, MRI and MRS computed FFs showed correlation with reference fat fraction values in samples with no iron (r > 0.98). PCCT decomposition showed slightly weaker correlation with FFref in samples with added iron (r = 0.586) compared to MRI (r = 0.673) and MRS (r = 0.716) methods. On the other hand, it showed no systematic over- or underestimation. Surprisingly, DECT decomposition-derived FF showed strongest correlation (r = 0.758) in these samples, however systematic overestimation was observed. FF values computed by three-material PCCT decomposition, DECT decomposition, MRI and MRS were unaffected by iron concentration.

CONCLUSIONS

This in-vitro study shows for the first time that photon-counting computed tomography may be used for quantification of fat content in the presence of iron deposits.

Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information

The past two decades have seen a significant increase in the use of CT, with a corresponding rise in the mean population radiation dose. This rise in CT use has caused improved diagnostic certainty in conditions that were not previously routinely evaluated using CT, such as headaches, back pain, and chest pain. Unused data, unrelated to the primary diagnosis, embedded within these scans have the potential to provide organ-specific measurements that can be used to prognosticate or risk-profile patients for a wide variety of conditions. The recent increased availability of computing power, expertise and software for automated segmentation and measurements, assisted by artificial intelligence, provides a conducive environment for the deployment of these analyses into routine use. Data gathering from CT has the potential to add value to examinations and help offset the public perception of harm from radiation exposure. We review the potential for the collection of these data and propose the incorporation of this strategy into routine clinical practice.

Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis

The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥  - 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients-lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.

Quantification of ectopic fat storage in the liver and pancreas using six-point Dixon MRI and its association with insulin sensitivity and β-cell function in patients with central obesity

OBJECTIVES

To assess the association of ectopic fat deposition in the liver and pancreas quantified by Dixon magnetic resonance imaging (MRI) with insulin sensitivity and β-cell function in patients with central obesity.

MATERIALS AND METHODS

A cross-sectional study of 143 patients with central obesity with normal glucose tolerance (NGT), prediabetes (PreD), and untreated type 2 diabetes mellitus (T2DM) was conducted between December 2019 and March 2022. All participants underwent routine medical history taking, anthropometric measurements, and laboratory tests, including a standard glucose tolerance test to quantify insulin sensitivity and β-cell function. The fat content in the liver and pancreas was measured with MRI using the six-point Dixon technique.

RESULTS

Patients with T2DM and PreD had a higher liver fat fraction (LFF) than those with NGT, while those with T2DM had a higher pancreatic fat fraction (PFF) than those with PreD and NGT. LFF was positively correlated with homeostatic model assessment of insulin resistance (HOMA-IR), while PFF was negatively correlated with homeostatic model assessment of insulin secretion (HOMA-β). Furthermore, using a structured equation model, we found LFF and PFF to be positively associated with glycosylated hemoglobin via HOMA-IR and HOMA-β, respectively.

CONCLUSIONS

In patients with central obesity, the effects of LFF and PFF on glucose metabolism. were associated with HOMA-IR and HOMA-β, respectively. Ectopic fat storage in the liver and pancreas quantified by MR Dixon imaging potentially plays a notable role in the onset ofT2DM.

CLINICAL RELEVANCE STATEMENT

We highlight the potential role of ectopic fat deposition in the liver and pancreas in the development of type 2 diabetes in patients with central obesity, providing valuable insights into the pathogenesis of the disease and potential targets for intervention.

KEY POINTS

• Ectopic fat deposition in the liver and pancreas is associated with T2DM. • T2DM and prediabetes patients had higher liver and pancreatic fat fractions than normal individuals. • The results provide valuable insights into pathogenesis of T2DM and potential targets for intervention.

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.

Changes in Hepatic Fat Content by CT 1 Year After Sleeve Gastrectomy in Adolescents and Young Adults With Obesity

CONTEXT

Obesity is associated with nonalcoholic fatty liver disease (NAFLD). Sleeve gastrectomy (SG) is an effective means of weight loss and improvement of NAFLD in adults; however, data regarding the efficacy of SG in the early stages of pediatric NAFLD are sparse.

OBJECTIVE

To assess the impact of SG on hepatic fat content 1 year after SG in youth with obesity compared with nonsurgical controls with obesity (NS).

DESIGN

A 12-month prospective study in 52 participants (mean age, 18.2 ± .36 years) with obesity, comprising 25 subjects who underwent SG (84% female; median body mass index [BMI], 44.6 [42.1-47.9] kg/m2) and 27 who were NS (70% female; median BMI, 42.2 [38.7-47.0] kg/m2).

MAIN OUTCOME MEASURES

Hepatic fat content by computed tomography (liver/spleen ratio), abdominal fat by magnetic resonance imaging.

RESULTS

Mean 12-month decrease in BMI was greater in SG vs NS (-12.5 ± .8 vs -.2 ± .5 kg/m2, P < .0001). There was a within-group increase in the liver-to-spleen (L/S) ratio in SG (.13 ± .05, P = .014) but not NS with a trend for a difference between groups (P = .055). All SG participants with an L/S ratio <1.0 (threshold for the diagnosis of NAFLD) before surgery had a ratio of >1.0 a year after surgery, consistent with resolution of NAFLD. Within SG, the 12-month change in L/S ratio was negatively associated with 12-month change in visceral fat (ρ = -.51 P = .016).

CONCLUSIONS

Hepatic fat content as assessed by noncontrast computed tomography improved after SG over 1 year in youth with obesity with resolution of NAFLD in all subjects. This was associated with decreases in visceral adiposity.

Correcting statistical CT number biases without access to raw detector counts: Applications to high spatial resolution photon counting CT imaging

BACKGROUND

Due to the nonlinear nature of the logarithmic operation and the stochastic nature of photon counts (N), sinogram data of photon counting detector CT (PCD-CT) are intrinsically biased, which leads to statistical CT number biases. When raw counts are available, nearly unbiased statistical estimators for projection data were developed recently to address the CT number bias issue. However, for most clinical PCD-CT systems, users' access to raw detector counts is limited. Therefore, it remains a challenge for end users to address the CT number bias issue in clinical applications.

PURPOSE

To develop methods to correct statistical biases in PCD-CT without requiring access to raw PCD counts.

METHODS

(1) The sample variance of air-only post-log sinograms was used to estimate air-only detector counts,N ¯ 0 $\bar{N}_0$ . (2) If the post-log sinogram data, y, is available, then N of each detector pixel was estimated usingN = N ¯ 0 e - y $N = \bar{N}_0 \, \mathrm{e}^{-y}$ . Once N was estimated, a closed-form analytical bias correction was applied to the sinogram. (3) If a patient's post-log sinogram data are not archived, a forward projection of the bias-contaminated CT image was used to perform a first-order bias correction. Both the proposed sinogram domain- and image domain-based bias correction methods were validated using experimental PCD-CT data.

RESULTS

Experimental results demonstrated that both sinogram domain- and image domain-based bias correction methods enabled reduced-dose PCD-CT images to match the CT numbers of reference-standard images within [-5, 5] HU. In contrast, uncorrected reduced-dose PCD-CT images demonstrated biases ranging from -25 to 55 HU, depending on the material. No increase in image noise or spatial resolution degradation was observed using the proposed methods.

CONCLUSIONS

CT number bias issues can be effectively addressed using the proposed sinogram or image domain method in PCD-CT, allowing PCD-CT acquired at different radiation dose levels to have consistent CT numbers desired for quantitative imaging.

Survival of Patients with Hepatitis B-Related Hepatocellular Carcinoma with Concomitant Metabolic Associated Fatty Liver Disease

Purpose

Metabolic associated fatty liver disease is a novel concept defined as fatty liver associated with metabolic disorders. We investigated the effect of metabolic associated fatty liver disease on hepatocellular carcinoma patient mortality.

Patients and Methods

A total of 624 patients with hepatocellular carcinoma between 2012 and 2020 were enrolled in this retrospective study. Hepatic steatosis was diagnosed using computed tomography or magnetic resonance imaging. Metabolic associated fatty liver disease was defined based on the proposed criteria in 2020. Propensity score matching was performed for patients with metabolic associated fatty liver disease and those without the condition. A Cox proportional hazards regression model was used to evaluate the association between metabolic associated fatty liver disease and hepatocellular carcinoma patient outcomes.

Results

Patients with hepatocellular carcinoma and metabolic associated fatty liver disease tended to achieve better outcomes than did those without metabolic associated fatty liver disease after matching (p<0.001). Metabolic associated fatty liver disease was significantly associated with better prognosis in patients with concurrent hepatitis B infection (p<0.001). Moreover, high levels of hepatitis B viral DNA in serum samples was associated with a significantly increased risk of death in patients without non-metabolic associated fatty liver disease (p=0.045). Additionally, the association between metabolic associated fatty liver disease and survival in hepatitis B virus-related hepatocellular carcinoma was similar in all subgroups based on metabolic traits.

Conclusion

Metabolic associated fatty liver disease increases the survival rate of patients with hepatocellular carcinoma and hepatitis B virus infection. The potential interaction of steatosis and virus replication should be considered for future research and clinical treatment strategies.

Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms

Quantitative magnetic resonance imaging (MRI) techniques are emerging as non-invasive alternatives to biopsy for assessment of diffuse liver diseases of iron overload, steatosis and fibrosis. For testing and validating the accuracy of these techniques, phantoms are often used as stand-ins to human tissue to mimic diffuse liver pathologies. However, currently, there is no standardization in the preparation of MRI-based liver phantoms for mimicking iron overload, steatosis, fibrosis or a combination of these pathologies as various sizes and types of materials are used to mimic the same liver disease. Liver phantoms that mimic specific MR features of diffuse liver diseases observed in vivo are important for testing and calibrating new MRI techniques and for evaluating signal models to accurately quantify these features. In this study, we review the liver morphology associated with these diffuse diseases, discuss the quantitative MR techniques for assessing these liver pathologies, and comprehensively examine published liver phantom studies and discuss their benefits and limitations.

US Quantification of Liver Fat: Past, Present, and Future

Fatty liver disease has a high and increasing prevalence worldwide, is associated with adverse cardiovascular events and higher long-term medical costs, and may lead to liver-related morbidity and mortality. There is an urgent need for accurate, reproducible, accessible, and noninvasive techniques appropriate for detecting and quantifying liver fat in the general population and for monitoring treatment response in at-risk patients. CT may play a potential role in opportunistic screening, and MRI proton-density fat fraction provides high accuracy for liver fat quantification; however, these imaging modalities may not be suited for widespread screening and surveillance, given the high global prevalence. US, a safe and widely available modality, is well positioned as a screening and surveillance tool. Although well-established qualitative signs of liver fat perform well in moderate and severe steatosis, these signs are less reliable for grading mild steatosis and are likely unreliable for detecting subtle changes over time. New and emerging quantitative biomarkers of liver fat, such as those based on standardized measurements of attenuation, backscatter, and speed of sound, hold promise. Evolving techniques such as multiparametric modeling, radiofrequency envelope analysis, and artificial intelligence-based tools are also on the horizon. The authors discuss the societal impact of fatty liver disease, summarize the current state of liver fat quantification with CT and MRI, and describe past, currently available, and potential future US-based techniques for evaluating liver fat. For each US-based technique, they describe the concept, measurement method, advantages, and limitations. © RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

AI-based CT Body Composition Identifies Myosteatosis as Key Mortality Predictor in Asymptomatic Adults

Background Body composition data have been limited to adults with disease or older age. The prognostic impact in otherwise asymptomatic adults is unclear. Purpose To use artificial intelligence-based body composition metrics from routine abdominal CT scans in asymptomatic adults to clarify the association between obesity, liver steatosis, myopenia, and myosteatosis and the risk of mortality. Materials and Methods In this retrospective single-center study, consecutive adult outpatients undergoing routine colorectal cancer screening from April 2004 to December 2016 were included. Using a U-Net algorithm, the following body composition metrics were extracted from low-dose, noncontrast, supine multidetector abdominal CT scans: total muscle area, muscle density, subcutaneous and visceral fat area, and volumetric liver density. Abnormal body composition was defined by the presence of liver steatosis, obesity, muscle fatty infiltration (myosteatosis), and/or low muscle mass (myopenia). The incidence of death and major adverse cardiovascular events were recorded during a median follow-up of 8.8 years. Multivariable analyses were performed accounting for age, sex, smoking status, myosteatosis, liver steatosis, myopenia, type 2 diabetes, obesity, visceral fat, and history of cardiovascular events. Results Overall, 8982 consecutive outpatients (mean age, 57 years ± 8 [SD]; 5008 female, 3974 male) were included. Abnormal body composition was found in 86% (434 of 507) of patients who died during follow-up. Myosteatosis was found in 278 of 507 patients (55%) who died (15.5% absolute risk at 10 years). Myosteatosis, obesity, liver steatosis, and myopenia were associated with increased mortality risk (hazard ratio [HR]: 4.33 [95% CI: 3.63, 5.16], 1.27 [95% CI: 1.06, 1.53], 1.86 [95% CI: 1.56, 2.21], and 1.75 [95% CI: 1.43, 2.14], respectively). In 8303 patients (excluding 679 patients without complete data), after multivariable adjustment, myosteatosis remained associated with increased mortality risk (HR, 1.89 [95% CI: 1.52, 2.35]; P < .001). Conclusion Artificial intelligence-based profiling of body composition from routine abdominal CT scans identified myosteatosis as a key predictor of mortality risk in asymptomatic adults. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Tong and Magudia in this issue.

Non-Invasive Imaging Methods to Evaluate Non-Alcoholic Fatty Liver Disease with Fat Quantification: A Review

Hepatic steatosis without specific causes (e.g., viral infection, alcohol abuse, etc.) is called non-alcoholic fatty liver disease (NAFLD), which ranges from non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH), fibrosis, and NASH-related cirrhosis. Despite the usefulness of the standard grading system, liver biopsy has several limitations. In addition, patient acceptability and intra- and inter-observer reproducibility are also concerns. Due to the prevalence of NAFLD and limitations of liver biopsies, non-invasive imaging methods such as ultrasonography (US), computed tomography (CT), and magnetic resonance imaging (MRI) that can reliably diagnose hepatic steatosis have developed rapidly. US is widely available and radiation-free but cannot examine the entire liver. CT is readily available and helpful for detection and risk classification, significantly when analyzed using artificial intelligence; however, it exposes users to radiation. Although expensive and time-consuming, MRI can measure liver fat percentage with magnetic resonance imaging proton density fat fraction (MRI-PDFF). Specifically, chemical shift-encoded (CSE)-MRI is the best imaging indicator for early liver fat detection. The purpose of this review is to provide an overview of each imaging modality with an emphasis on the recent progress and current status of liver fat quantification.

Liver fat quantification in photon counting CT in head to head comparison with clinical MRI - First experience

PURPOSE

To compare liver fat quantification between MRI and photon-counting CT (PCCT).

METHOD

A cylindrical phantom with inserts containing six concentrations of oil (0, 10, 20, 30, 50 and 100%) and oil-iodine mixtures (0, 10, 20, 30 and 50% fat +3 mg/mL iodine) was imaged with a PCCT (NAEOTOM Alpha) and a 1.5 T MRI system (MR 450w, IDEAL-IQ sequence), using clinical parameters. An IRB-approved prospective clinical evaluation included 12 obese adult patients with known fatty liver disease (seven women, mean age: 61.5 ± 13 years, mean BMI: 30.3 ± 4.7 kg/m²). Patients underwent a same-day clinical MRI and PCCT of the abdomen. Liver fat fractions were calculated for four segments (I, II, IVa and VII) using in- and opposed-phase on MRI ((Mean_in - Mean_opp)/2*Mean_in) and iodine-fat, tissue decomposition analysis in PCCT (Syngo.Via VB60A). CT and MRI Fat fractions were compared using two-sample t-tests with equal variance. Statistical analysis was performed using RStudio (Version1.4.1717).

RESULTS

Phantom results showed no significant differences between the known fat fractions (P = 0.32) or iodine (P = 0.6) in comparison to PCCT-measured concentrations, and no statistically significant difference between known and MRI-measured fat fractions (P = 0.363). In patients, the mean fat signal fraction measured on MRI and PCCT was 13.1 ± 9.9% and 12.0 ± 9.0%, respectively, with an average difference of 1.1 ± 1.9% between the modalities (P = 0.138).

CONCLUSION

First experience shows promising accuracy of liver fat fraction quantification for PCCT in obese patients. This method may improve opportunistic screening for CT in the future.

Molecular imaging of liver inflammation using an anti-VCAM-1 nanobody

To date, a biopsy is mandatory to evaluate parenchymal inflammation in the liver. Here, we evaluated whether molecular imaging of vascular cell adhesion molecule-1 (VCAM-1) could be used as an alternative non-invasive tool to detect liver inflammation in the setting of chronic liver disease. To do so, we radiolabeled anti-VCAM-1 nanobody (99mTc-cAbVCAM1-5) and used single-photon emission computed tomography (SPECT) to quantify liver uptake in preclinical models of non-alcoholic fatty liver disease (NAFLD) with various degree of liver inflammation: wild-type mice fed a normal or high-fat diet (HFD), FOZ fed a HFD and C57BL6/J fed a choline-deficient or -supplemented HFD. 99mTc-cAbVCAM1-5 uptake strongly correlates with liver histological inflammatory score and with molecular inflammatory markers. The diagnostic power to detect any degree of liver inflammation is excellent (AUROC 0.85-0.99). These data build the rationale to investigate 99mTc-cAbVCAM1-5 imaging to detect liver inflammation in patients with NAFLD, a largely unmet medical need.

Relative Enhancement in Gadoxetate Disodium-Enhanced Liver MRI as an Imaging Biomarker in the Diagnosis of Non-Alcoholic Fatty Liver Disease in Pediatric Obesity

Relative enhancement (RE) in gadoxetate disodium (Gd-EOB-DTPA)-enhanced MRI is a reliable, non-invasive method for the evaluation and differentiation between simple steatosis and non-alcoholic steatohepatitis in adults. This study evaluated the diagnostic accuracy of RE in Gd-EOB-DTPA-enhanced liver MRI and hepatic fat fraction (HFF) in unenhanced liver MRI and ultrasound (US) for non-alcoholic fatty liver disease (NAFLD) screening in pediatric obesity. Seventy-four liver US and MRIs from 68 pediatric patients (13.07 ± 2.95 years) with obesity (BMI > BMI-for-age + 2SD) were reviewed with regard to imaging biomarkers (liver size, volume, echogenicity, HFF, and RE in Gd-EOB-DTPA-enhanced MRIs, and spleen size), blood biomarkers, and BMI. The agreement between the steatosis grade, according to HFF in MRI and the echogenicity in US, was moderate. Alanine aminotransferase correlated better with the imaging biomarkers in MRI than with those in US. BMI correlated better with liver size and volume on MRI than in US. In patients with RE < 1, blood biomarkers correlated better with RE than those in the whole sample, with a significant association between gamma-glutamyltransferase and RE (p = 0.033). In conclusion, the relative enhancement and hepatic fat fraction can be considered as non-invasive tools for the screening and follow-up of NAFLD in pediatric obesity, superior to echogenicity on ultrasound.

Quantitative Analysis of Liver Iron Deposition Based on Dual-Energy CT in Thalassemia Patients

Background

To explore the feasibility and accuracy of liver iron deposition based on dual-energy CT in thalassemia patients.

Materials and methods

105 thalassemia patients were examined with dual-energy CT and MR liver scanning. Dual-energy CT was performed to measure CT values on 80kVp, 140kVp, and virtual iron content (VIC) imaging; ΔH was figured out by the difference in CT values between 80kVp and 140kVp. Using the liver iron concentration (LIC) obtained by FerriScan as a gold standard, the correlation between CT measurements and LIC was evaluated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance for dual-energy CT in liver iron quantification and stratification.

Results

The correlation analysis between CT measurements and LIC showed that 80kVp, 140kVp, VIC, and ΔH all had a high positive correlation with LIC (P<0.001). The correlation analysis among different degree groups of VIC, ΔH, and LIC showed that the normal, moderate, and severe groups of VIC and ΔH had moderate or high positive correlations with that of LIC (P<0.01), but the mild group had no correlation (P>0.05). ROC analysis revealed that the corresponding optimal cutoff value of VIC was -2.8, 6.3,11.9 HU (corresponds to 3.2,7.0,15.0 mg/g dry weight) respectively, while the ΔH were 5.1, 8.4, 17.8HU, respectively. The area under the receiver operating characteristic curves (AUCs) for both VIC and ΔH increased with LIC thresholds.

Conclusion

Dual-energy CT can accurately quantify and stratify liver iron deposition, contributing to predicting the status of liver iron deposition in thalassemia patients.

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study

BACKGROUND

Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death.

METHODS

DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity.

FINDINGS

The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001).

INTERPRETATION

The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment.

FUNDING

For a full list of funding bodies, please see the Acknowledgements.

Sugar-Sweetened Beverage, Diet Soda, and Nonalcoholic Fatty Liver Disease Over 6 Years: The Framingham Heart Study

BACKGROUND & AIMS

Nonalcoholic fatty liver disease (NAFLD) is associated with sugar-sweetened beverage (SSB) consumption in cross-sectional studies. In a prospective cohort, we examined the association of beverage consumption (SSB and diet soda) with incident NAFLD and changes in hepatic fat in the Framingham Heart Study (FHS).

METHODS

We conducted a prospective observational study of participants from the FHS Third Generation and Offspring cohorts who participated in computed tomography sub-studies. Participants were classified according to their average SSB or diet soda consumption, which was derived from baseline and follow-up food frequency questionnaires: non-consumers (0-<1/month), occasional consumers (1/month-<1/week), and frequent consumers (≥1/week-≥1/day). Hepatic fat was quantified by the liver fat attenuation measurements on computed tomography scan. The primary dependent variable was incident NAFLD; secondarily, we investigated change in liver fat.

RESULTS

The cohorts included 691 Offspring (mean age, 62.8 ± 8.2 years; 57.7% women) and 945 Third Generation participants (mean age, 48.4 ± 6.3 years; 46.6% women). In the Offspring cohort, there was a dose-response relationship with SSB consumption and incident NAFLD. Frequent SSB consumers had 2.53 times increased odds of incident NAFLD compared with non-consumers (95% confidence interval, 1.36-4.7) after multivariable analysis. For Offspring cohort participants, occasional and frequent consumers of SSB had a more adverse increase in liver fat compared with non-consumers.

CONCLUSIONS

Higher average SSB intake is associated with increase in liver fat over 6 years of follow-up and increased odds of incident NAFLD especially among the older cohort, whereas no consistent association was observed for the younger Third Generation cohort.