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

AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B

BACKGROUND & AIMS

Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables.

METHODS

An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software. The derivation cohort (n = 5,585) was randomly divided into the training and internal validation sets at a 3:1 ratio. The external validation cohort included 2,883 patients. Six imaging biomarkers (i.e. abdominal visceral fat-total fat volume ratio, total fat-trunk volume ratio, spleen volume, liver volume, liver-spleen Hounsfield unit ratio, and muscle Hounsfield unit) and eight clinical variables were selected as the main variables of our model, PLAN-B-DF.

RESULTS

In the internal validation set (median follow-up duration = 7.4 years), PLAN-B-DF demonstrated an excellent predictive performance with a c-index of 0.91 and good calibration function (p = 0.78 by the Hosmer-Lemeshow test). In the external validation cohort (median follow-up duration = 4.6 years), PLAN-B-DF showed a significantly better discrimination function compared to previous models, including PLAN-B, PAGE-B, modified PAGE-B, and CU-HCC (c-index, 0.89 vs. 0.65-0.78; all p <0.001), and maintained a good calibration function (p = 0.42 by the Hosmer-Lemeshow test). When patients were classified into four groups according to the risk probability calculated by PLAN-B-DF, the 10-year cumulative HCC incidence was 0.0%, 0.4%, 16.0%, and 46.2% in the minimal-, low-, intermediate-, and high-risk groups, respectively.

CONCLUSION

This AI prediction model, integrating deep learning-based auto-segmentation of CT images, offers improved performance in predicting HCC risk among patients with CHB compared to previous models.

IMPACT AND IMPLICATIONS

The novel predictive model PLAN-B-DF, employing an automated computed tomography segmentation algorithm, significantly improves predictive accuracy and risk stratification for hepatocellular carcinoma in patients with chronic hepatitis B (CHB). Using a gradient-boosting algorithm and computed tomography metrics, such as visceral fat volume and myosteatosis, PLAN-B-DF outperforms previous models based solely on clinical and demographic data. This model not only shows a higher c-index compared to previous models, but also effectively classifies patients with CHB into different risk groups. This model uses machine learning to analyze the complex relationships among various risk factors contributing to hepatocellular carcinoma occurrence, thereby enabling more personalized surveillance for patients with CHB.

Three-dimensional organ segmentation-derived CT attenuation parameters for assessing hepatic steatosis in chronic hepatitis B patients

The utility of CT-derived parameters for hepatic steatosis assessment has primarily focused on non-alcoholic fatty liver disease. This study aimed to evaluate their applicability in chronic hepatitis B (CHB) through a retrospective analysis of 243 CHB patients. Using deep-learning-based 3D organ segmentation on abdominal CT scans at 100 kVp, the mean volumetric CT attenuation of the liver and spleen was automatically measured on pre-contrast (liver (L)_pre and spleen (S)_pre) and post-contrast (L_post and S_post) portal venous phase images. To identify mild, moderate, and severe steatosis (S1, S2, and S3 based on the controlled attenuation parameter), L_pre showed areas under the receiver operating characteristic curve (AUROCs) of 0.695, 0.779, and 0.795, significantly higher than L-S_pre (0.633, 0.691, and 0.732; Ps = 0.02, 0.003, and 0.03). Post-contrast parameters demonstrated slightly lower AUROCs than their pre-contrast counterparts (Ps = 0.15-0.81). Concomitant hepatic fibrosis influenced diagnostic performance, with CT parameters performing better in patients without severe fibrosis than those with (F3-4 on transient elastography), though statistical significance was only observed for L-S_post in severe steatosis (P = 0.037). In conclusion, CT attenuation-based parameters extracted through automated 3D analysis show promise as a tool for assessing hepatic steatosis in patients with CHB.

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.

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.

Opportunistic Assessment for Parathyroid Adenoma on CT: A Retrospective Cohort Study Evaluating Primary Hyperparathyroidism-Associated Morbidity Over 10 Years of Follow-Up

BACKGROUND. Primary hyperparathyroidism (PHPT) is underdiagnosed. Opportunistic imaging-based parathyroid gland assessment is a proposed strategy for identifying patients at increased risk of undiagnosed PHPT. However, whether this approach is likely to identify individuals with clinically significant disease is unknown. OBJECTIVE. This study's objective was to assess for associations of the presence of an enlarged parathyroid gland on contrast-enhanced CT with clinical outcomes causally related to PHPT. METHODS. This retrospective cohort study included patients 18 years old or older with at least one contrast-enhanced chest or neck CT examination performed from January 2012 to December 2012, at least one noncontrast CT examination covering the chest or neck region without a date restriction, and at least one clinical encounter in the health system from January 2022 to December 2022. A neuroradiologist reviewed the CT examinations to determine the presence versus absence of an enlarged parathyroid gland on the 2012 study. Patient demographics, serum calcium results, and diagnosis codes for clinical outcomes causally related to PHPT were extracted from the EHR. Calcium results and diagnosis codes were classified as preexisting if documented before and as incident if documented after the 2012 contrast-enhanced CT examination. RESULTS. The cohort included 1198 patients (593 men and 605 women; mean age, 51.6 years), of whom 43 (3.6%) were assessed as having an enlarged parathyroid gland on the 2012 contrast-enhanced CT examination. PHPT was diagnosed in 16.3% of patients with, versus 0.3% of patients without, an enlarged parathyroid gland (p < .001). After adjustment for age, sex, race, and ethnicity, the presence of an enlarged parathyroid gland on contrast-enhanced CT was associated with significantly increased odds of preexisting nephrolithiasis (OR = 2.71; p = .03), hypercalcemia (OR = 5.30; p < .001), and PHPT (OR = 12.59; p = .008) as well as increased odds of incident osteopenia or osteoporosis (OR = 2.78; p = .008), nephrolithiasis (OR = 4.95; p < .001), hypercalcemia (OR = 7.58; p < .001), and PHPT (OR = 148.01; p < .001). CONCLUSION. An enlarged parathyroid gland indicated increased risk of PHPT as well as increased risk of preexisting and incident clinical conditions causally related to PHPT. CLINICAL IMPACT. Opportunistic CT-based assessment is a promising strategy for identifying patients at increased risk of undiagnosed PHPT; such assessment could potentially prevent some PHPT-related complications through earlier diagnosis and treatment.

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.

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.

Non-invasively differentiate non-alcoholic steatohepatitis by visualizing hepatic integrin αvβ3 expression with a targeted molecular imaging modality

BACKGROUND

Non-invasive methods to diagnose non-alcoholic steatohepatitis (NASH), an inflammatory subtype of non-alcoholic fatty liver disease (NAFLD), are currently unavailable.

AIM

To develop an integrin αvβ3-targeted molecular imaging modality to differentiate NASH.

METHODS

Integrin αvβ3 expression was assessed in Human LO2 hepatocytes Scultured with palmitic and oleic acids (FFA). Hepatic integrin αvβ3 expression was analyzed in rabbits fed a high-fat diet (HFD) and in rats fed a high-fat, high-carbohydrate diet (HFCD). After synthesis, cyclic arginine-glycine-aspartic acid peptide (cRGD) was labeled with gadolinium (Gd) and used as a contrast agent in magnetic resonance imaging (MRI) performed on mice fed with HFCD.

RESULTS

Integrin αvβ3 was markedly expressed on FFA-cultured hepatocytes, unlike the control hepatocytes. Hepatic integrin αvβ3 expression significantly increased in both HFD-fed rabbits and HFCD-fed rats as simple fatty liver (FL) progressed to steatohepatitis. The distribution of integrin αvβ3 in the liver of NASH cases largely overlapped with albumin-positive staining areas. In comparison to mice with simple FL, the relative liver MRI-T1 signal value at 60 minutes post-injection of Gd-labeled cRGD was significantly increased in mice with steatohepatitis (P < 0.05), showing a positive correlation with the NAFLD activity score (r = 0.945; P < 0.01). Hepatic integrin αvβ3 expression was significantly upregulated during NASH development, with hepatocytes being the primary cells expressing integrin αvβ3.

CONCLUSION

After using Gd-labeled cRGD as a tracer, NASH was successfully distinguished by visualizing hepatic integrin αvβ3 expression with MRI.

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.

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.

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.

Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase

OBJECTIVE

Fully-automated CT-based algorithms for quantifying numerous biomarkers have been validated for unenhanced abdominal scans. There is great interest in optimizing the documentation and reporting of biophysical measures present on all CT scans for the purposes of opportunistic screening and risk profiling. The purpose of this study was to determine and adjust the effect of intravenous (IV) contrast on these automated body composition measures at routine portal venous phase post-contrast imaging.

METHODS

Final study cohort consisted of 1,612 older adults (mean age, 68.0 years; 594 women) all imaged utilizing a uniform CT urothelial protocol consisting of pre-contrast, portal venous, and delayed excretory phases. Fully-automated CT-based algorithms for quantifying numerous biomarkers, including muscle and fat area and density, bone mineral density, and solid organ volume were applied to pre-contrast and portal venous phases. The effect of IV contrast upon these body composition measures was analyzed. Regression analyses, including square of the Pearson correlation coefficient (r2), were performed for each comparison.

RESULTS

We found that simple, linear relationships can be derived to determine non-contrast equivalent values from the post-contrast CT biomeasures. Excellent positive linear correlation (r2 = 0.91-0.99) between pre- and post-contrast values was observed for all automated soft tissue measures, whereas moderate positive linear correlation was observed for bone attenuation (r2 = 0.58-0.76). In general, the area- and volume-based measurement require less adjustment than attenuation-based measures, as expected.

CONCLUSION

Fully-automated quantitative CT-biomarker measures at portal venous phase abdominal CT can be adjusted to a non-contrast equivalent using simple, linear relationships.

Metabolic-associated fatty liver voxel-based quantification on CT images using a contrast adapted automatic tool

BACKGROUND & AIMS

Metabolic-dysfunction associated fatty liver disease (MAFLD) is highly prevalent and can lead to liver complications and comorbidities, with non-invasive tests such as vibration-controlled transient elastography (VCTE) and invasive liver biopsies being used for diagnosis The aim of the present study was to develop a new fully automatized method for quantifying the percentage of fat in the liver based on a voxel analysis on computed tomography (CT) images to solve previously unconcluded diagnostic deficiencies either in contrast (CE) or non-contrast enhanced (NCE) assessments.

METHODS

Liver and spleen were segmented using nn-UNet on CE- and NCE-CT images. Radiodensity values were obtained for both organs for defining the key benchmarks for fatty liver assessment: liver mean, liver-to-spleen ratio, liver-spleen difference, and their average. VCTE was used for validation. A classification task method was developed for detection of suitable patients to fulfill maximum reproducibility across cohorts and highlight subjects with other potential radiodensity-related diseases.

RESULTS

Best accuracy was attained using the average of all proposed benchmarks being the liver-to-spleen ratio highly useful for CE and the liver-to-spleen difference for NCE. The proposed whole-organ automatic segmentation displayed superior potential when compared to the typically used manual region-of-interest drawing as it allows to accurately obtain the percent of fat in liver, among other improvements. Atypical patients were successfully stratified through a function based on biochemical data.

CONCLUSIONS

The developed method tackles the current drawbacks including biopsy invasiveness, and CT-related weaknesses such as lack of automaticity, dependency on contrast agent, no quantification of the percentage of fat in liver, and limited information on region-to-organ affectation. We propose this tool as an alternative for individualized MAFLD evaluation by an early detection of abnormal CT patterns based in radiodensity whilst abording detection of non-suitable patients to avoid unnecessary exposure to CT radiation. Furthermore, this work presents a surrogate aid for assessing fatty liver at a primary assessment of MAFLD using elastography data.

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.

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.

Detection of Moderate Hepatic Steatosis on Portal Venous Phase Contrast-Enhanced CT: Evaluation Using an Automated Artificial Intelligence Tool

BACKGROUND. Precontrast CT is an established means of evaluating for hepatic steatosis; postcontrast CT has historically been limited for this purpose. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of portal venous phase postcontrast CT in detecting at least moderate hepatic steatosis using liver and spleen attenuation measurements determined by an automated artificial intelligence (AI) tool. METHODS. This retrospective study included 2917 patients (1381 men, 1536 women; mean age, 56.8 years) who underwent a CT examination that included at least two series through the liver. Examinations were obtained from an AI vendor's data lake of data from 24 centers in one U.S. health care network and 29 centers in one Israeli health care network. An automated deep learning tool extracted liver and spleen attenuation measurements. The reference for at least moderate steatosis was precontrast liver attenuation of less than 40 HU (i.e., estimated liver fat > 15%). A radiologist manually reviewed examinations with outlier AI results to confirm portal venous timing and identify issues impacting attenuation measurements. RESULTS. After outlier review, analysis included 2777 patients with portal venous phase images. Prevalence of at least moderate steatosis was 13.9% (387/2777). Patients without and with at least moderate steatosis, respectively, had mean postcontrast liver attenuation of 104.5 ± 18.1 (SD) HU and 67.1 ± 18.6 HU (p < .001); a mean difference in postcontrast attenuation between the liver and the spleen (hereafter, postcontrast liver-spleen attenuation difference) of -7.6 ± 16.4 (SD) HU and -31.8 ± 20.3 HU (p < .001); and mean liver enhancement of 49.3 ± 15.9 (SD) HU versus 38.6 ± 13.6 HU (p < .001). Diagnostic performance for the detection of at least moderate steatosis was higher for postcontrast liver attenuation (AUC = 0.938) than for the postcontrast liver-spleen attenuation difference (AUC = 0.832) (p < .001). For detection of at least moderate steatosis, postcontrast liver attenuation had sensitivity and specificity of 77.8% and 93.2%, respectively, at less than 80 HU and 90.5% and 78.4%, respectively, at less than 90 HU; the postcontrast liver-spleen attenuation difference had sensitivity and specificity of 71.4% and 79.3%, respectively, at less than -20 HU and 87.0% and 62.1%, respectively, at less than -10 HU. CONCLUSION. Postcontrast liver attenuation outperformed the postcontrast liver-spleen attenuation difference for detecting at least moderate steatosis in a heterogeneous patient sample, as evaluated using an automated AI tool. Splenic attenuation likely is not needed to assess for at least moderate steatosis on postcontrast images. CLINICAL IMPACT. The technique could promote early detection of clinically significant nonalcoholic fatty liver disease through individualized or large-scale opportunistic evaluation.

CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease

Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.

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

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

Moderate to severe hepatic steatosis on computerized tomography imaging: Prevalence in obese Pacific Islanders and Asians

Background and Aim

Hepatic steatosis (HS) is common worldwide, but there is little data on the prevalence of HS in Pacific Islanders (PIs) and Asians within the United States. Our aim was to compare prevalence of HS in obese 18-50-year-olds of Asian and PI ethnicity who underwent computerized tomography (CT).

Methods

We performed a retrospective analysis of all members of an integrated healthcare system who self-identified as Asian or PI, were between the ages of 18 and 50 years, had a body mass index (BMI) ≥ 30, and underwent a CT scan that included the liver during 2021, resulting in 748 subjects. Imaging was analyzed using a method sensitive and specific for moderate to severe HS. Additionally, multiple binary logistic regression was performed to explore the relationship between HS and HbA1c, BMI, and age.

Results

Of the 748 patients, 311 (41.6%) had HS. We found no significant difference in the prevalence of HS between Asians and PIs (χ2 1 = 1.3, P = 0.25), between Asian and PI men (χ2 1 = 2.8, P = 0.096), or between Asian and PI women (χ2 1 = 0.053, P = 0.82). Higher odds of HS was associated with increasing BMI (OR = 1.08; 95% CI: 1.06-1.11; P < 0.001) and HbA1c (OR = 1.15; 95% CI: 1.04-1.26; P = 0.00489), but HS was not associated with age in this age range (OR = 0.993; 95% CI: 0.973-1.01; P = 0.46).

Conclusion

Moderate to severe HS is very common in obese Asian and PI adults, and occurs at similar rates in these ethnicities. Abdominal CT imaging presents an opportunity to diagnose HS and provides relevant information to patients and healthcare providers.

CT Number Accuracy and Association With Object Size: A Phantom Study Comparing Energy-Integrating Detector CT and Deep Silicon Photon-Counting Detector CT

BACKGROUND. Variable beam hardening based on patient size causes variation in CT numbers for energy-integrating detector (EID) CT. Photon-counting detector (PCD) CT more accurately determines effective beam energy, potentially improving CT number reliability. OBJECTIVE. The purpose of the present study was to compare EID CT and deep silicon PCD CT in terms of both the effect of changes in object size on CT number and the overall accuracy of CT numbers. METHODS. A phantom with polyethylene rings of varying sizes (mimicking patient sizes) as well as inserts of different materials was scanned on an EID CT scanner in single-energy (SE) mode (120-kV images) and in rapid-kilovoltage-switching dual-energy (DE) mode (70-keV images) and on a prototype deep silicon PCD CT scanner (70-keV images). ROIs were placed to measure the CT numbers of the materials. Slopes of CT number as a function of object size were computed. Materials' ideal CT number at 70 keV was computed using the National Institute of Standards and Technology XCOM Photon Cross Sections Database. The root mean square error (RMSE) between measured and ideal numbers was calculated across object sizes. RESULTS. Slope (expressed as Hounsfield units per centimeter) was significantly closer to zero (i.e., less variation in CT number as a function of size) for PCD CT than for SE EID CT for air (1.2 vs 2.4 HU/cm), water (-0.3 vs -1.0 HU/cm), iodine (-1.1 vs -4.5 HU/cm), and bone (-2.5 vs -10.1 HU/cm) and for PCD CT than for DE EID CT for air (1.2 vs 2.8 HU/cm), water (-0.3 vs -1.0 HU/cm), polystyrene (-0.2 vs -0.9 HU/cm), iodine (-1.1 vs -1.9 HU/cm), and bone (-2.5 vs -6.2 HU/cm) (p < .05). For all tested materials, PCD CT had the smallest RMSE, indicating CT numbers closest to ideal numbers; specifically, RMSE (expressed as Hounsfield units) for SE EID CT, DE EID CT, and PCD CT was 32, 44, and 17 HU for air; 7, 8, and 3 HU for water; 9, 10, and 4 HU for polystyrene; 31, 37, and 13 HU for iodine; and 69, 81, and 20 HU for bone, respectively. CONCLUSION. For numerous materials, deep silicon PCD CT, in comparison with SE EID CT and DE EID CT, showed lower CT number variability as a function of size and CT numbers closer to ideal numbers. CLINICAL IMPACT. Greater reliability of CT numbers for PCD CT is important given the dependence of diagnostic pathways on CT numbers.

Artificial Intelligence Applications in Hepatology

Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine learning, computers are now able to learn from complex and diverse clinical datasets to solve real-world medical problems with performance that surpasses that of physicians in certain areas. AI algorithms are currently being implemented in liver imaging, interpretation of liver histopathology, noninvasive tests, prediction models, and more. In this review, we provide a summary of the state of AI in hepatology and discuss current challenges for large-scale implementation including some ethical aspects. We emphasize to the readers that most AI-based algorithms that are discussed in this review are still considered in early development and their utility and impact on patient outcomes still need to be assessed in future large-scale and inclusive studies. Our vision is that the use of AI in hepatology will enhance physician performance, decrease the burden and time spent on documentation, and reestablish the personalized patient-physician relationship that is of utmost importance for obtaining good outcomes.

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.

Evaluation of Artificial Intelligence-Calculated Hepatorenal Index for Diagnosing Mild and Moderate Hepatic Steatosis in Non-Alcoholic Fatty Liver Disease

Background and Objectives: This study aims to evaluate artificial intelligence-calculated hepatorenal index (AI-HRI) as a diagnostic method for hepatic steatosis. Materials and Methods: We prospectively enrolled 102 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD). All patients had a quantitative ultrasound (QUS), including AI-HRI, ultrasound attenuation coefficient (AC,) and ultrasound backscatter-distribution coefficient (SC) measurements. The ultrasonographic fatty liver indicator (US-FLI) score was also calculated. The magnetic resonance imaging fat fraction (MRI-PDFF) was the reference to classify patients into four grades of steatosis: none < 5%, mild 5–10%, moderate 10–20%, and severe ≥ 20%. We compared AI-HRI between steatosis grades and calculated Spearman’s correlation (rs) between the methods. We determined the agreement between AI-HRI by two examiners using the intraclass correlation coefficient (ICC) of 68 cases. We performed a receiver operating characteristics (ROC) analysis to estimate the area under the curve (AUC) for AI-HRI. Results: The mean AI-HRI was 2.27 (standard deviation, ±0.96) in the patient cohort. The AI-HRI was significantly different between groups without (1.480 ± 0.607, p < 0.003) and with mild steatosis (2.155 ± 0.776), as well as between mild and moderate steatosis (2.777 ± 0.923, p < 0.018). AI-HRI showed moderate correlation with AC (rs = 0.597), SC (rs = 0.473), US-FLI (rs = 0.5), and MRI-PDFF (rs = 0.528). The agreement in AI-HRI was good between the two examiners (ICC = 0.635, 95% confidence interval (CI) = 0.411–0.774, p < 0.001). The AI-HRI could detect mild steatosis (AUC = 0.758, 95% CI = 0.621–0.894) with fair and moderate/severe steatosis (AUC = 0.803, 95% CI = 0.721–0.885) with good accuracy. However, the performance of AI-HRI was not significantly different (p < 0.578) between the two diagnostic tasks. Conclusions: AI-HRI is an easy-to-use, reproducible, and accurate QUS method for diagnosing mild and moderate hepatic steatosis.

Three segments sampling strategy for the assessment of liver steatosis using magnetic resonance imaging proton density fat fraction

PURPOSE

This research aims to determine the best liver segments representing the whole-liver fat fraction (FF) in magnetic resonance imaging (MRI)-based measurement of the proton density fat fraction (PDFF).

METHOD

This retrospective study included 989 adult subjects who underwent MRI-PDFF from March 2018 to January 2021. Three regions of interest (ROI) were measured and averaged for each hepatic segment and the volume-weighted hepatic FF was calculated. Intrahepatic fat variability was assessed by standard deviation between all ROIs. Univariate and multivariate linear regression analyses were done for the factors associated with intrahepatic fat variability among clinical characteristics, blood parameters and the volume-weighted FF. The arithmetic means of specific hepatic segments that were the closest to the volume-weighted FF were identified in all subjects and those with moderate or severe fatty liver.

RESULTS

The volume-weighted FF was 8.18% and variability was 1.33%. Volume-weighted FF was the only associated factor with intrahepatic variability. The arithmetic mean of segments V, VI, and IV was closest to the volume-weighted FF in all subjects and in subjects with moderate or severe fatty liver.

CONCLUSIONS

There was considerable heterogeneity in hepatic steatosis between each segment of the liver, and the variability was significantly affected by the volume-weighted FF. The mean hepatic FF from segments V, VI, and IV could be used to estimate the volume-weighted FF of the whole liver, not only in the general population but also in patients with moderate or severe fatty liver.

Hepatic Fat Quantification with the Multi-Material Decomposition Algorithm by Using Low-Dose Non-Contrast Material-Enhanced Dual-Energy Computed Tomography in a Prospectively Enrolled Cohort

The early diagnosis of hepatic steatosis is important. No study has assessed hepatic fat quantification by using low-dose dual-energy computed tomography (CT). We assessed the accuracy of hepatic fat quantification using the multi-material decomposition (MMD) algorithm with low-dose non-contrast material-enhanced dual-energy CT. We retrospectively reviewed 33 prospectively enrolled patients who had undergone low-dose non-contrast material-enhanced dual-energy CT and magnetic resonance image (MRI) proton density fat fraction (PDFF) on the same day. Percentage fat volume fraction (FVF) images were generated using the MMD algorithm on the low-dose dual-energy CT data. We assessed the correlation between FVFs and MRI-PDFFs by using Spearman's rank correlation. With a 5% cutoff value of MRI-PDFF for fatty liver, a receiver operating characteristic (ROC) curve analysis was performed to identify the optimal criteria of FVF for diagnosing fatty liver. CTDIvol of CT was 2.94 mGy. FVF showed a strong correlation with MRI-PDFF (r = 0.756). The ROC curve analysis demonstrated that FVF ≥ 4.61% was the optimal cutoff for fatty liver. With this cutoff value for diagnosing the fatty liver on low-dose dual-energy CT, the sensitivity, specificity, and area under the curve were 90%, 100%, and 0.987, respectively. The MMD algorithm using low-dose non-contrast material-enhanced dual-energy CT is feasible for quantifying hepatic fat.

Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM

OBJECTIVE

Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a 4-dimensional (4D) deep learning model based on 3D convolution and convolutional long short-term memory (C-LSTM) for hepatocellular carcinoma (HCC) lesion segmentation.

METHODS

The proposed deep learning model utilizes 4D information on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) images to assist liver tumor segmentation. Specifically, a shallow U-net based 3D CNN module was designed to extract 3D spatial domain features from each DCE phase, followed by a 4-layer C-LSTM network module for time domain information exploitation. The combined information of multi-phase DCE images and the manner by which tissue imaging features change on multi-contrast images allow the network to more effectively learn the characteristics of HCC, resulting in better segmentation performance.

RESULTS

The proposed model achieved a Dice score of 0.825± 0.077, a Hausdorff distance of 12.84± 8.14 mm, and a volume similarity of 0.891± 0.080 for liver tumor segmentation, which outperformed the 3D U-net model, RA-UNet model and other models in the ablation study in both internal and external test sets. Moreover, the performance of the proposed model is comparable to the nnU-Net model, which showed state-of-the-art performance in many segmentation tasks, with significantly reduced prediction time.

CONCLUSION

The proposed 3D convolution and C-LSTM based model can achieve accurate segmentation of HCC lesions.

CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation

CT-based body composition measures are well established in research settings as prognostic markers in oncologic patients. Numerous retrospective studies have shown the role of objective measurements extracted from abdominal CT images of skeletal muscle, abdominal fat, and bone mineral density in providing more accurate assessments of frailty and cancer cachexia in comparison with traditional clinical methods. Quantitative CT-based measurements of liver fat and aortic atherosclerotic calcification have received relatively less attention in cancer care but also provide prognostic information. Patients with cancer routinely undergo serial CT examinations for staging, treatment response, and surveillance, providing the opportunity for quantitative body composition assessment to be performed as part of routine clinical care. The emergence of fully automated artificial intelligence-based segmentation and quantification tools to replace earlier time-consuming manual and semiautomated methods for body composition analysis will allow these opportunistic measures to transition from the research realm to clinical practice. With continued investigation, the measurements may ultimately be applied to achieve more precise risk stratification as a component of personalized oncologic care.

Quantitative dual-energy CT techniques in the abdomen

Advances in dual-energy CT (DECT) technology and spectral techniques are catalyzing the widespread implementation of this technology across multiple radiology subspecialties. The inclusion of energy- and material-specific datasets has ushered overall improvements in CT image contrast and noise as well as artifacts reduction, leading to considerable progress in radiologists' ability to detect and characterize pathologies in the abdomen. The scope of this article is to provide an overview of various quantitative clinical DECT applications in the abdomen and pelvis. Several of the reviewed applications have not reached mainstream clinical use and are considered investigational. Nonetheless awareness of such applications is critical to having a fully comprehensive knowledge base to DECT and fostering future clinical implementation.

Hepatic Steatosis: CT-Based Prevalence in Adults in China and the United States and Associations With Age, Sex, and Body Mass Index

BACKGROUND. Calibrated CT fat fraction (FFCT) measurements derived from un-enhanced abdominal CT reliably reflect liver fat content, allowing large-scale population-level investigations of steatosis prevalence and associations. OBJECTIVE. The purpose of this study was to compare the prevalence of hepatic steatosis, as assessed by calibrated CT measurements, between population-based Chinese and U.S. cohorts, and to investigate in these populations the relationship of steatosis with age, sex, and body mass index (BMI). METHODS. This retrospective study included 3176 adults (1985 women and 1191 men) from seven Chinese provinces and 8748 adults (4834 women and 3914 men) from a single U.S. medical center, all drawn from previous studies. All participants were at least 40 years old and had undergone unenhanced abdominal CT in previous studies. Liver fat content measurements on CT were cross-calibrated to MRI proton density fat fraction measurements using phantoms and expressed as adjusted FFCT measurements. Mild, moderate, and severe steatosis were defined as adjusted FFCT of 5.0-14.9%, 15.0-24.9%, and 25.0% or more, respectively. The two cohorts were compared. RESULTS. In the Chinese and U.S. cohorts, the median adjusted FFCT for women was 4.7% and 4.8%, respectively, and that for men was 5.8% and 6.2%, respectively. In the Chinese and U.S. cohorts, steatosis prevalence for women was 46.3% and 48.7%, respectively, whereas that for men was 58.9% and 61.9%, respectively. Severe steatosis prevalence was 0.9% and 1.8% for women and 0.2% and 2.6% for men in the Chinese and U.S. cohorts, respectively. Adjusted FFCT did not vary across age decades among women or men in the Chinese cohort, although it increased across age decades among women and men in the U.S. cohort. Adjusted FFCT and BMI exhibited weak correlation (r = 0.312-0.431). Among participants with normal BMI, 36.8% and 38.5% of those in the Chinese and U.S. cohorts, respectively, had mild steatosis, and 3.0% and 1.5% of those in the Chinese and U.S. cohorts, respectively, had moderate or severe steatosis. Among U.S. participants with a BMI of 40.0 or greater, 17.7% had normal liver content. CONCLUSION. Steatosis and severe steatosis had higher prevalence in the U.S. cohort than in the Chinese cohort in both women and men. BMI did not reliably predict steatosis. CLINICAL IMPACT. The findings provide new information on the dependence of hepatic steatosis on age, sex, and BMI.

Value-added Opportunistic CT Screening: State of the Art

Opportunistic CT screening leverages robust imaging data embedded within abdominal and thoracic scans that are generally unrelated to the specific clinical indication and have heretofore gone largely unused. This incidental imaging information may prove beneficial to patients in terms of wellness, prevention, risk profiling, and presymptomatic detection of relevant disease. The growing interest in CT-based opportunistic screening relates to a confluence of factors: the objective and generalizable nature of CT-based body composition measures, the emergence of fully automated explainable AI solutions, the sheer volume of body CT scans performed, and the increasing emphasis on precision medicine and value-added initiatives. With a systematic approach to body composition and other useful CT markers, initial evidence suggests that their ability to help radiologists assess biologic age and predict future adverse cardiometabolic events rivals even the best available clinical reference standards. Emerging data suggest that standalone "intended" CT screening over an unorganized opportunistic approach may be justified, especially when combined with established cancer screening. This review will discuss the current status of opportunistic CT screening, including specific body composition markers and the various disease processes that may be impacted. The remaining hurdles to widespread clinical adoption include generalization to more diverse patient populations, disparate technical settings, and reimbursement.

Advances in liver US, CT, and MRI: moving toward the future

Over the past two decades, the epidemiology of chronic liver disease has changed with an increase in the prevalence of nonalcoholic fatty liver disease in parallel to the advent of curative treatments for hepatitis C. Recent developments provided new tools for diagnosis and monitoring of liver diseases based on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), as applied for assessing steatosis, fibrosis, and focal lesions. This narrative review aims to discuss the emerging approaches for qualitative and quantitative liver imaging, focusing on those expected to become adopted in clinical practice in the next 5 to 10 years. While radiomics is an emerging tool for many of these applications, dedicated techniques have been investigated for US (controlled attenuation parameter, backscatter coefficient, elastography methods such as point shear wave elastography [pSWE] and transient elastography [TE], novel Doppler techniques, and three-dimensional contrast-enhanced ultrasound [3D-CEUS]), CT (dual-energy, spectral photon counting, extracellular volume fraction, perfusion, and surface nodularity), and MRI (proton density fat fraction [PDFF], elastography [MRE], contrast enhancement index, relative enhancement, T1 mapping on the hepatobiliary phase, perfusion). Concurrently, the advent of abbreviated MRI protocols will help fulfill an increasing number of examination requests in an era of healthcare resource constraints.

Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features

Objectives

To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective.

Materials and Methods

In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively.

Results

Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively.

Conclusions

Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.

Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans

BACKGROUND

Abdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical.

PURPOSE

To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT.

MATERIALS AND METHODS

The training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans. The testing data set consisted of 922 CT colonography (CTC) scans, and 1207 paired noncontrast and postcontrast CT scans from renal donors from a second institution. Reference standard data included manual plaque segmentations in the 137 training scans and manual plaque burden measurements in the 922 CTC scans. The total Agatston score and group (0-3) was determined using fully-automated deep learning software. Performance was assessed by measures of agreement, linear regression, and paired evaluations.

RESULTS

On CTC scans, automated Agatston scoring correlated highly with manual assessment (R² = 0.94). On paired renal donor CT scans, automated Agatston scoring on postcontrast CT correlated highly with noncontrast CT (R² = 0.95). When plaque burden was expressed as a group score, there was excellent agreement for both the CTC (weighted kappa 0.80 ± 0.01 [95% confidence interval: 0.78-0.83]) and renal donor (0.83 ± 0.02 [0.79-0.86]) assessments.

CONCLUSION

Fully automated detection, segmentation, and scoring of abdominal aortic atherosclerotic plaques on both pre- and post-contrast CT was validated and may have application for population-based studies.

Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen

Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have achieved performance comparable to humans in less time for several organ segmentation tasks, and some lesion detection and classification tasks. This article introduces the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (n = 1960) or renal donor evaluation (n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Automated assessment of longitudinal biomarker changes at abdominal CT: correlation with subsequent cardiovascular events in an asymptomatic adult screening cohort

BACKGROUND

Cardiovascular (CV) disease is a major public health concern, and automated methods can potentially capture relevant longitudinal changes on CT for opportunistic CV screening purposes.

METHODS

Fully-automated and validated algorithms that quantify abdominal fat, muscle, bone, liver, and aortic calcium were retrospectively applied to a longitudinal adult screening cohort undergoing serial non-contrast CT examination between 2005 and 2016. Downstream major adverse events (MI/CVA/CHF/death) were identified via algorithmic EHR search. Logistic regression, ROC curve, and Cox survival analyses assessed for associations between changes in CT variables and adverse events.

RESULTS

Final cohort included 1949 adults (942 M/1007F; mean age, 56.2 ± 6.2 years at initial CT). Mean interval between CT scans was 5.8 ± 2.0 years. Mean clinical follow-up interval from initial CT was 10.4 ± 2.7 years. Major CV events occurred after follow-up CT in 230 total subjects (11.8%). Mean change in aortic calcium Agatston score was significantly higher in CV(+) cohort (591.6 ± 1095.3 vs. 261.1 ± 764.3), as was annualized Agatston change (120.5 ± 263.6 vs. 46.7 ± 143.9) (p < 0.001 for both). 5-year area under the ROC curve (AUC) for Agatston change was 0.611. Hazard ratio for Agatston score change > 500 was 2.8 (95% CI 1.5-4.0) relative to < 500. Agatston score change was the only significant univariate CT biomarker in the survival analysis. Changes in fat and bone measures added no meaningful prediction.

CONCLUSION

Interval change in automated CT-based abdominal aortic calcium load represents a promising predictive longitudinal tool for assessing cardiovascular and mortality risks. Changes in other body composition measures were less predictive of adverse events.

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.

Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ^©RSNA, 2021.