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

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).

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.

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).

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

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

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.

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).

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.

Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterprise. The authors developed an AI orchestrator to facilitate the deployment and use of AI tools in a large multi-site university healthcare system and used it to conduct opportunistic screening for hepatic steatosis. During the 60-day study period, 991 abdominal CTs were processed at multiple different physical locations with an average turnaround time of 2.8 min. Quality control images and AI results were fully integrated into the existing clinical workflow. All input into and output from the server was in standardized data formats. The authors describe the methodology in detail; this framework can be adapted to integrate any clinical AI algorithm.

Recently, a 1-h PG value of ≥ 8.6 mmol/L, a more sensitive predictor of diabetes mellitus-related long-term cardiovascular complications than routine glucose markers, has been recommended as an additional diagnostic criterion for diabetes in the International Diabetes Federation Position Statement. However, its value in MASLD remains uncertain.

Consecutive participants with imaging assessments of fatty liver and a 75-g oral glucose tolerance test, including 1154 participants with MASLD, 161 fulfilling the nonalcoholic fatty liver disease but not the MASLD diagnostic criteria (NAFLD-non-MASLD) and 1026 subjects with non-fatty liver, were retrospectively enrolled from June 2009 to May 2024.

Patients with MASLD or NAFLD-non-MASLD had higher 1-h PG levels than those with non-fatty liver (p < 0.001). In patients with MASLD or NAFLD-non-MASLD, 1-h PG ≥ 8.6 mmol/L was associated with the risk of moderate-to-severe steatosis (p < 0.001), ALT elevation (p < 0.001), advanced fibrosis (p = 0.03), and cardiovascular diseases (p < 0.001). Furthermore, NAFLD-non-MASLD patients with 1-h PG ≥ 8.6 mmol/L showed a higher prevalence of advanced fibrosis than MASLD patients with or without 1-h PG ≥ 8.6 mmol/L (p < 0.05).

NAFLD-non-MASLD patients with 1-h PG ≥ 8.6 mmol/L are still at high risk of poor clinical outcomes. These findings support including 1-h PG ≥ 8.6 mmol/L as a component of the metabolic dysfunction definition.

Hepatic steatosis (HS) and 10-year atherosclerotic cardiovascular disease (ASCVD) risk ≥ 7.5% are associated with increased risk for cardiovascular events.

To assess underlying coronary artery disease (CAD) and major adverse cardiovascular event (MACE) among those with and without HS at different ASCVD risk.

We evaluated stable chest pain patients receiving coronary computed tomography (CT) in the PROMISE trial. HS and CAD endpoints were defined on coronary CT. MACE was defined as unstable angina, non-fatal myocardial infarction, and all-cause death. Multivariable Cox regression, adjusting for CAD characteristics, assessed the association of HS with MACE for ASCVD < 7.5%.

One thousand two hundred and four of 3702 (32.5%) patients were at ASCVD < 7.5% and 20.3% (244/1204) of them had HS. Individuals with HS were younger (54.3 ± 5.2 vs. 55.8 ± 5.2; p < 0.001), more often males (40.2% [98/244] vs. 27.1% [260/960]; p < 0.001), had more risk factors/person (2.06 ± 0.89 vs. 1.93 ± 0.91; p = 0.047). CAD characteristics were similar between HS vs. non-HS patients at ASCVD < 7.5% and ASCVD ≥ 7.5% (all p > 0.05). Patients with HS had greater MACE rate compared to non-HS patients (ASCVD < 7.5%: 3.75%[9/244] vs. 1.5% [14/960]; p = 0.027 and ASCVD ≥ 7.5%: 4.7% [33/696] vs. 3.1% [56/1802]; p = 0.043). In patients without HS, MACE rate was higher in the ASCVD ≥ 7.5% vs. < 7.5% (3.1% [56/1802] vs. 1.5% [14/960]; p = 0.011). In patients with HS, MACE rates were not significantly different between ASCVD ≥ 7.5% vs. < 7.5% (4.7% [33/696] vs. 3.7% [9/244]; p = 0.484). In ASCVD < 7.5%, HS predicted MACE (aHR:2.34, 95%CI:1.01-5.43; p = 0.048), independent of CAD characteristics.

Individuals with HS at ASCVD < 7.5% risk had similar CAD characteristics as patients without HS at < 7.5% ASCVD risk, yet experienced comparable MACE rates as those at ASCVD ≥ 7.5%.

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.

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.

Background: The 1-h post-load plasma glucose was proposed to replace the current OGTT criteria for diagnosing prediabetes/diabetes. However, it remains unclear whether it is superior in identifying progressive metabolic dysfunction-associated steatotic liver disease (MASLD), and thus we aimed to clarify this issue. Methods: Consecutive Asian participants (non-MASLD, n = 1049; MASLD, n = 1165) were retrospectively enrolled between June 2012 and June 2024. CT was used to quantify liver steatosis, while the serum liver fibrotic marker was used to evaluate liver fibrosis. Results: Compared with those with normal levels of both 1-h post-glucose (1hPG) and 2-h post-glucose (2hPG), patients with MASLD showed a significant positive association between elevated 1hPG levels and moderate to severe liver steatosis (odds ratio [OR] = 2.19, 95% confidence interval [CI]: 1.13-4.25, p = 0.02]. Elevated levels of both 1hPG and 2hPG were associated with an increased risk of liver injury (OR = 2.03, 95% CI: 1.44-2.86, p < 0.001). Elevated 2hPG levels with or without elevated 1hPG levels were associated with liver fibrosis (OR = 1.99, 95% CI: 1.15-3.45, p < 0.001; OR = 2.72, 95% CI: 1.79-4.11, p < 0.001, respectively). Additionally, either 1hPG or 2hPG levels were associated with atherosclerosis, revealing significant dose-dependent associations between glucose status and atherosclerosis risk (OR = 2.77, 95% CI: 1.55-4.96, p < 0.001 for elevated 1hPG; OR = 2.98, 95% CI = 1.54-5.78, p = 0.001 for elevated 2hPG; OR = 2.41, 95% CI = 1.38-4.21, p = 0.001 for elevated levels of both 1hPG and 2hPG). The areas under the ROC for predicting steatosis, liver injury, liver fibrosis, and atherosclerosis were 0.64, 0.58, 0.58, and 0.64 for elevated 1hPG (all p < 0.05) and 0.50, 0.60, 0.56, and 0.62 for elevated 2hPG (all p < 0.05), respectively. Conclusions: These findings underscore the necessity for clinicians to acknowledge that the screening and management of MALSD requires the monitoring of 1hPG levels.

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.

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.

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.

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).

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.

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.

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.

Spectral imaging of photon-counting detector CT (PCD-CT) scanners allows for generating virtual non-contrast (VNC) reconstruction. By analyzing 12 abdominal organs, we aimed to test the reliability of VNC reconstructions in preserving HU values compared to real unenhanced CT images.

Our study included 34 patients with pancreatic cystic neoplasm (PCN). The VNC reconstructions were generated from unenhanced, arterial, portal, and venous phase PCD-CT scans using the Liver-VNC algorithm. The observed 11 abdominal organs were segmented by the TotalSegmentator algorithm, the PCNs were segmented manually. Average densities were extracted from unenhanced scans (HUunenhanced), postcontrast (HUpostcontrast) scans, and VNC reconstructions (HUVNC). The error was calculated as HUerror=HUVNC-HUunenhanced. Pearson's or Spearman's correlation was used to assess the association. Reproducibility was evaluated by intraclass correlation coefficients (ICC).

Significant differences between HUunenhanced and HUVNC[unenhanced] were found in vertebrae, paraspinal muscles, liver, and spleen. HUVNC[unenhanced] showed a strong correlation with HUunenhanced in all organs except spleen (r = 0.45) and kidneys (r = 0.78 and 0.73). In all postcontrast phases, the HUVNC had strong correlations with HUunenhanced in all organs except the spleen and kidneys. The HUerror had significant correlations with HUunenhanced in the muscles and vertebrae; and with HUpostcontrast in the spleen, vertebrae, and paraspinal muscles in all postcontrast phases. All organs had at least one postcontrast VNC reconstruction that showed good-to-excellent agreement with HUunenhanced during ICC analysis except the vertebrae (ICC: 0.17), paraspinal muscles (ICC: 0.64-0.79), spleen (ICC: 0.21-0.47), and kidneys (ICC: 0.10-0.31).

VNC reconstructions are reliable in at least one postcontrast phase for most organs, but further improvement is needed before VNC can be utilized to examine the spleen, kidneys, and vertebrae.

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.

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.

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).

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.

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.

• 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.

Skeletal muscle (SM) is a key factor in cancer treatment. However, it is unclear whether pretreatment SM change affects the outcome of immune checkpoint inhibitors (ICIs) therapy in gastric cancer (GC).

Advanced GCs treated with ICIs were retrospectively investigated. SM evaluated by psoas muscle area at the third lumbar vertebra was measured on CT acquired within 1 month from the start of ICIs therapy (CT-1), and on CT acquired 2.8 ± 0.84 months before CT-1. Monthly change rate of SM (MCR-SM) was defined as the change rate of SMs between those two CTs divided by the period between those CTs (month). Monthly change rate of body weight (MCR-BW) during the same period was also calculated. They were compared with disease-specific survival (DSS) and progression-free survival (PFS). MCR-SM was compared with pretreatment markers including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), C-reactive protein (CRP), and liver-to-spleen CT attenuation ratio (LSR) as a marker of liver lipid metabolism.

This study enrolled eighty-three GC patients. MCR-SM significantly correlated with DSS and PFS (P < 0.0001, 0.001, respectively), whereas MCR-BW did not. Kaplan-Meier analyses demonstrated that higher MCR-SM (MCR-SM ≥ -0.7185%) significantly associated with better DSS and PFS (P = 0.0002, 0.03, respectively). Patients with positive MCR-SM showed significantly lower NLR, MLR, and CRP than those with negative (P = 0.01, 0.006, 0.003, respectively). MCR-SM showed a significant positive correlation with LSR (P = 0.007, R = 0.30).

Pretreatment SM loss, associated with high systemic inflammation and hepatic fat accumulation, related to poor outcome of ICIs therapy in GC.

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.

Fat deposition is an important marker of many metabolic diseases. As a noninvasive and convenient examination method, CT has been widely used for fat quantification. With the clinical application of photon-counting detector (PCD)-CT, we aimed to investigate the accuracy, stability, and dose level of PCD-CT using various scan settings for fat quantification.

Eleven agar-based lipid-containing phantoms (vials with different fat fractions [FFs]; range: 0 %-100 %) were scanned using PCD-CT. Three scanning types (sequence scan, regular spiral scan with a pitch of 0.8, and high-pitch spiral scan with a pitch of 3.2), four tube voltages (90, 120, 140, and 100 kV with a tin filter), and three image quality (IQ) levels (IQ levels of 20, 40, and 80) were alternated, and each scan setting was used twice. For each scan, a 70-keV image was generated using the same reconstruction parameters. A regular spiral scan at 120 kV with IQ80 was used to transfer the CT numbers of all scans to the FF. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were implemented for accuracy and agreement evaluation, and group differences were compared using analysis of variance.

Excellent agreement and accuracy of FF derived by PCD-CT with all scan settings was demonstrated by high ICCs (>0.9; range: 0.929-0.998, p < 0.017) and low bias (<5% range: -2.9 %-5%). The root mean square error (RMSE) between the PCD-CT-acquired FF and the reference standard ranged from 1.0 % to 5.0 %, among which the high-pitch scan at 120 kV with IQ20 accounted for the lowest RMSE (1.0 %). The spiral scan at 120 kV with IQ20 and IQ80 yielded the lowest bias (mean value: 1.19 % and 1.23 %, respectively).

Fat quantification using PCD-CT reconstructed at 70 keV was accurate and stable under various scan settings. PCD-CT has great potential for fat quantification using ultralow radiation doses.

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.

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.

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.

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.

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.

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.

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.

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.

To determine the relationship between lipoprotein particle size/number with hepatic steatosis (HS), given its association with traditional lipoproteins and coronary atherosclerosis.

Individuals with available CT data and blood samples enrolled in the PROMISE trial were studied. HS was defined based on CT attenuation. Lipoprotein particle size/number were measured by nuclear magnetic resonance spectroscopy. Principal components analysis (PCA) was used for dimensionality reduction. The association of PCA factors and individual lipoprotein particle size/number with HS were assessed in multivariable regression models. Associations were validated in an independent cohort of 59 individuals with histopathology defined HS.

Individuals with HS (n=410/1,509) vs those without (n=1,099/1,509), were younger (59±8 vs 61±8 years) and less often females (47.6 % vs 55.9 %). All PCA factors were associated with HS: factor 1 (OR:1.36, 95 %CI:1.21-1.53), factor 3 (OR:1.75, 95 %CI:1.53-2.02) and factor 4 (OR:1.49; 95 %CI:1.32-1.68) were weighted heavily with small low density lipoprotein (LDL) and triglyceride-rich (TRL) particles, while factor 2 (OR:0.86, 95 %CI:0.77-0.97) and factor 5 (OR:0.74, 95 %CI:0.65-0.84) were heavily loaded with high density lipoprotein (HDL) and larger LDL particles. These observations were confirmed with the analysis of individual lipoprotein particles in PROMISE. In the validation cohort, association between HS and large TRL (OR: 8.16, 95 %CI:1.82-61.98), and mean sizes of TRL- (OR: 2.82, 95 %CI:1.14-9.29) and HDL (OR:0.35, 95 %CI:0.13-0.72) were confirmed.

Large TRL, mean sizes of TRL-, and HDL were associated with radiographic and histopathologic HS. The use of lipoprotein particle size/number could improve cardiovascular risk assessment in HS.

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.

Background: Steatotic liver disease (SLD) has been linked to more exacerbated inflammatory responses in various scenarios. The relationship between SLD and COVID-19 prognosis remains unclear. Our aim was to investigate the impact of SLD on the outcome of COVID-19. Methods: Patients hospitalized with confirmed COVID-19 and who underwent laboratory tests and chest CT scans were included. SLD was assessed by measuring the attenuation coefficient on CT scans. The relationship between SLD, the severity of COVID-19 clinical presentation and in-hospital mortality were assessed. Results: A total of 610 patients were included (mean age 62 ± 16 years, 64% male). The prevalence of SLD was 30%, and the overall in-hospital mortality rate was 19%. Patients with SLD were younger (58 ± 13 vs. 64 ± 16 years, p < 0.001) and had a higher BMI (32 ± 5 vs. 28 ± 4 kg/m2, p = 0.014). Admission AST values were higher in patients with SLD (82 ± 339 vs. 50 ± 37, p = 0.02), while D-dimer (1112 ± 2147 vs. 1959 ± 8509, p = 0.07), C-reactive protein (12 ± 9 vs. 11 ± 8, p = 0.27), ALT (67 ± 163 vs. 47 ± 90, p = 0.11), ALP (83 ± 52 vs. 102 ± 125, p = 0.27), and GGT (123 ± 125 vs. 104 ± 146, p = 0.61) did not significantly differ compared to patients without SLD. No difference was observed regarding lung parenchyma involvement >50% (20% vs. 17%, p = 0.25), hospital length of stay (14 ± 19 vs. 16 ± 23 days, p = 0.20), hemodialysis support (14% vs. 16%, p = 0.57), use of mechanical ventilation (20% vs. 20%, p = 0.96), and in-hospital mortality (17% vs. 20%, p = 0.40) when comparing patients with and without SLD. Conclusions: SLD showed no significant association with morbidity and mortality in patients with COVID-19.

Pericoronary adipose tissue (PCAT) CT attenuation of right coronary artery (RCA) and non-alcoholic fatty liver disease (NAFLD) have prognostic value for major adverse cardiovascular events (MACE) in patients with coronary artery disease. However, the superior prognostic value between RCA PCAT CT attenuation and NAFLD remains unclear in patients with acute chest pain. This study is to evaluate the prognostic value of NAFLD for MACE, and further assess the incremental prognostic value of NAFLD over PCAT CT attenuation.

Between January 2011 and December 2021, all consecutive emergency patients with acute chest pain referred for coronary CT angiography (CCTA) were retrospectively enrolled. MACE included unstable angina requiring hospitalization, coronary revascularization, non-fatal myocardial infarction, and all-cause death. Patients' baseline and CCTA characteristics, RCA PCAT CT attenuation, and the presence of NAFLD were used to evaluate risk factors of MACE using multivariable Cox regression analysis. The prognostic value of NAFLD compared to RCA PCAT CT attenuation was analyzed.

A total of 514 patients were enrolled (mean age, 58.36 ± 13.05 years; 310 men). During a median follow-up of 31 months, 60 patients (11.67%) experienced MACE. NAFLD (HR = 2.599, 95% CI: 1.207, 5.598, P = 0.015) and RCA PCAT CT attenuation (HR = 1.026, 95% CI: 1.001, 1.051, P = 0.038) were independent predictors of MACE. The global Chi-square analysis showed that NAFLD improved the risk of MACE more than that using clinical risk factors and CCTA metrics (59.51 vs 54.44, P = 0.024) or combined with RCA PCAT CT attenuation (63.75 vs 59.51, P = 0.040).

NAFLD and RCA PCAT CT attenuation were predictors of MACE. NAFLD had an incremental prognostic value beyond RCA PCAT CT attenuation for MACE in patients with acute chest pain. Adding CT-FFR into the risk prediction of patients with acute chest pain is worth considering.

Evaluation of the influence of intrinsic and extrinsic conditions on ablation zone volumes (AZV) after microwave ablation (MWA).

Retrospective analysis of 38 MWAs of therapy-naïve liver tumours performed with the NeuWave PR probe. Ablations were performed either in the 'standard mode' (65 W, 10 min) or in the 'surgical mode' (95 W, 1 min, then 65 W, 10 min). AZV measurements were obtained from contrast-enhanced computed tomography immediately post-ablation.

AZVs in the 'standard mode' were smaller than predicted by the manufacturer (length 3.6 ± 0.6 cm, 23% below 4.7 cm; width 2.7 ± 0.6, 23% below 3.5 cm). Ablation zone past the tip was limited to 6 mm in 28/32 ablations. Differences in AZV between the 'surgical mode' and 'standard mode' were not significant (15.6 ± 7.8 mL vs. 13.9 ± 8.8 mL, p = 0.6). AZVs were significantly larger in case of hepatocellular carcinomas (HCCs) (n = 19) compared to metastasis (n = 19; 17.8 ± 9.9 mL vs. 10.1 ± 5.1 mL, p = 0.01) and in non-perivascular tumour location (n = 14) compared to perivascular location (n = 24, 18.7 ± 10.4 mL vs. 11.7 ± 6.1 mL, p = 0.012), with both factors remaining significant in two-way analysis of variance (HCC vs. metastasis: p = 0.02; perivascular vs. non-perivascular tumour location: p = 0.044).

Larger AZVs can be expected in cases of HCCs compared with metastases and in non-perivascular locations. Using the 'surgical mode' does not increase AZV significantly.

Long pentraxin-3 (PTX-3) is an acute phase protein associated with cardiovascular disease, lung injury, and mortality. We evaluated the association between computed tomography (CT)-measurements of adipose tissue and plasma levels of PTX-3.

We performed a cross-sectional analysis of community-dwelling adults enrolled in the multi-center Multiethnic Study of Atherosclerosis who underwent cardiac or abdominal CT and had available PTX-3 measurements.

There was a U-shaped association between pericardial adipose tissue volume (PAT), abdominal visceral adipose tissue area (VAT), hepatic attenuation, and PTX-3 levels, with extremes of adiposity associated with greater PTX-3 levels. Using multivariable-adjusted piecewise regression models, among participants with low PAT, every 1% increase in PAT volume was associated with a 13.8% decrease in PTX-3 (95% confidence interval [CI] -21.6 to -6.0); among participants with high PAT, every 1% increase in PAT volume was associated with a 6.0% increase in PTX-3 (95% CI -0.4 to 12.5). Results were similar for abdominal VAT and hepatic attenuation.

In a cohort of community-dwelling adults, we demonstrated a "U-shaped" association between pericardial, abdominal visceral, and hepatic adiposity with PTX3 levels, suggesting that extreme adiposity is associated with greater circulating levels of PTX3. Further work is required to identify the mechanisms linking adiposity and PTX-3.

We aimed to test whether computed tomography (CT)-diagnosed Non-Alcoholic Fatty Liver Disease (NAFLD) is a risk factor for cerebrovascular symptoms in patients with suspected atherosclerotic disease. A total of 550 patients (mean age 65.2 ± 8.8 years, 370 males) with carotid plaques who underwent carotid computed tomographic angiography (CTA) and unenhanced abdominal CT were retrospectively analyzed. NAFLD was diagnosed by abdominal CT. Carotid CTA assessed the presence of carotid artery stenosis or plaque. The relationship between NAFLD and cerebrovascular symptoms was analyzed using generalized estimating equations and receiver operating characteristic (ROC) analysis. The prevalence of NAFLD was significantly higher in symptomatic patients (76.5 vs 9.8%; P < .001). After adjusting for several confounding factors (e.g., hypertension and hyperlipidemia), univariate and multivariate logic regression analysis revealed that NAFLD was still strongly associated with cerebrovascular symptoms (odds ratio, 22.81; 95% CI 13.03-39.93; P < .001). ROC analysis showed that the area under the curve for discriminating symptomatic and asymptomatic plaques using NAFLD measurements was 0.833, with a sensitivity of 76.5% and a specificity of 90.2%. NAFLD is strongly associated with an increased risk of cerebrovascular symptoms. It may be an important predictor of symptomatic carotid plaque and cerebrovascular symptoms.

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.

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).

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).

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

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).

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.

Hepatic steatosis, including nonalcoholic fatty liver disease (NAFLD), is common among people with HIV (PWH). We present baseline steatosis prevalence and cardiometabolic characteristics among REPRIEVE substudy participants.

REPRIEVE is an international, primary cardiovascular disease prevention, randomized, controlled trial of pitavastatin calcium vs. placebo among 7769 PWH ages 40-75 years on antiretroviral therapy (ART) and with low-to-moderate cardiovascular risk. A subset of participants underwent noncontrast computed tomography, with hepatic steatosis defined as mean hepatic attenuation less than 40 HU or liver/spleen ratio less than 1.0, and NAFLD defined as steatosis in the absence of frequent alcohol use or viral hepatitis.

Of 687 evaluable persons, median age was 51 years, BMI 27 kg/m 2 , CD4 + T-cell count 607 cells/μl; 17% natal female sex, 36% Black, 24% Hispanic, and 98% HIV-1 RNA less than 400 copies/ml. Hepatic steatosis prevalence was 22% (149/687), and NAFLD 21% (96/466). Steatosis/NAFLD prevalence was higher in men and with older age, non-Black race, and higher BMI and waist circumference. Both were associated with BMI greater than 30 kg/m 2 , metabolic syndrome components, higher atherosclerotic cardiovascular disease (ASCVD) risk score, HOMA-IR, LpPLA-2 and hs-CRP, and lower high-density lipoprotein cholesterol. Of HIV-specific/ART-specific characteristics, only history of an AIDS-defining illness was more common among persons with steatosis/NAFLD. After adjusting for age, sex and race/ethnicity, BMI greater than 30 kg/m 2 , HOMA-IR greater than 2.0, Metabolic syndrome and each of its components were associated with NAFLD prevalence.

In this cohort with controlled HIV and low-to-moderate cardiovascular risk, hepatic steatosis and NAFLD were common and associated with clinically relevant metabolic and inflammatory disturbances but not current HIV-related or ART-related factors.

To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients.

In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test.

The final cohort included 1669 patients (age 67.5 [58.5-77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88-95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001).

Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients.

In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification.

• In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death. • A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection. • Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients' risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.

Patient positioning is known to impact CT number and patient dose. The aim of this study was to report current practice and the accuracy of the knowledge of radiographers in Australia regarding inappropriate patient centring during CT.

A survey of members of the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) was undertaken. The survey consisted of 36 questions about patient off-centring, CT number accuracy, and radiation dose. The survey was open from May to August 2021 and directed toward diagnostic radiographers with CT experience. Responses were evaluated as correct or incorrect based on evidence-based knowledge and theoretical definitions.

Among 186 participants, a majority of 75.7 % followed the manufacturer's recommended guidelines for localiser radiographs direction. Despite higher radiation dose to anteriorly located radiosensitive organs, 63.64 % preferred the anterior-posterior (AP) localiser. Only 21.93 % agreed that localiser direction would definitely affect radiation dose if the patient was inappropriately centred. A knowledge-practice disparity is evident as 45.77 % rarely or never reviewed CT numbers post-image acquisition, even though 92.8 % acknowledged their significance on prognosis and clinical decisions. Significant discrepancies in the accuracy of responses were identified; inaccuracies were dependent on CT qualifications (p = 0.0199), experience (p = 0.0397), and workload (p = 0.0360). Knowledge gaps existed regarding factors affecting patient centring and its impact on radiation dose and CT numbers.

This study highlights inconsistent CT practices and knowledge gaps among Australian radiographers. Given the impact of inaccurate positioning on radiation dose and CT number, engagement with continuous professional development programs that focus on the importance of patient positioning and the utilisation of the latest advancements in dose management tools is urgently needed.

Where radiographers do not possess the necessary theoretical knowledge and understanding to maximise patient positioning accuracy to optimise dose and CT number accuracy, then patients may receive an unnecessary dose. Inaccurate CT number accuracy may lead to misdiagnosis or inaccurate delivery of treatment.

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).

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.

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).

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.

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.

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

(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.

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.

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.

Nonalcoholic fatty liver disease (NAFLD) is the most common form of liver disease worldwide. There are limited biomarkers that can detect progression from simple steatosis to nonalcoholic steatohepatitis (NASH). The purpose of our study was to utilize CT texture analysis to distinguish steatosis from NASH.

16 patients with NAFLD (38% male, median (interquartile range): age 57 (48-64) years, BMI 37.5 (35.0-46.8) kg/m2) underwent liver biopsy and abdominal non-contrast CT. CT texture analysis was performed to quantify gray-level tissue summaries (e.g., entropy, kurtosis, skewness, and attenuation) using commercially available software (TexRad, Cambridge England). Logistic regression analyses were performed to quantify the association between steatosis/NASH status and CT texture. ROC curve analysis was performed to determine sensitivity, specificity, AUC, 95% CIs, and cutoff values of texture parameters to differentiate steatosis from NASH.

By histology, 6/16 (37%) of patients had simple steatosis and 10/16 (63%) had NASH. Patients with NASH had lower entropy (median, interquartile range (IQR): 4.3 (4.1, 4.8) vs. 5.0 (4.9, 5.2), P = 0.013) and lower mean value of positive pixels (MPP) (34.4 (21.8, 52.2) vs. 66.5 (57.0, 70.7), P = 0.009) than those with simple steatosis. Entropy values below 4.73 predict NASH with 100% (95%CI: 67-100%) specificity and 80% (50-100%) sensitivity, AUC: 0.88. MPP values below 54.0 predict NASH with 100% (67-100%) specificity and 100% (50-100%) sensitivity, AUC 0.90.

Our study provides preliminary evidence that CT texture analysis may serve as a novel imaging biomarker for disease activity in NAFLD and the discrimination of steatosis and NASH.

Fatty liver is associated with increased risk of developing type 2 diabetes. We aimed to evaluate whether the severity of hepatic steatosis is associated with incident diabetes.

We conducted a longitudinal analysis using data from 1,798 participants who underwent a comprehensive health checkup and abdominal computed tomography (CT). We assessed the association between baseline liver attenuation value on non-contrast CT images and risk of incident diabetes. All the participants were categorized into three groups based on the baseline liver attenuation value on non-contrast CT images: without hepatic steatosis (>57 Hounsfield unit [HU]), mild hepatic steatosis (41-57 HU), and moderate to severe hepatic steatosis (≤40 HU).

During a median follow-up period of 5 years, 6.0% of the study participants progressed to diabetes. The incidence of diabetes was 17.3% in the moderate to severe hepatic steatosis group, 9.0% in the mild steatosis group, and 2.9% in those without hepatic steatosis. In a multivariate adjustment model, as compared with participants without hepatic steatosis, those with moderate to severe steatosis had a hazard ratio (HR) of 3.24 (95% confidence interval [CI], 1.64 to 4.2) for the development of diabetes, and those in the mild steatosis group had a HR of 2.33 (95% CI, 1.42 to 3.80). One standard deviation decrease in mean CT attenuation values of the liver was associated with a 40% increase in the development of diabetes (multivariate adjusted HR, 1.40; 95% CI, 1.2 to 1.63).

We found a positive association between severity of hepatic steatosis and risk of incident diabetes. Greater severity of steatosis was associated with a higher risk of incident diabetes.

This study evaluated the effect of pitch on 256-slice helical computed tomography (CT) scans. Cylindrical water phantoms (CWP) were measured using axial and helical scans with various pitch values. The surface dose distributions of CWP were measured, and reconstructed images were obtained using filtered back-projection (FBP) and iterative model reconstruction (IMR). The image noise in each reconstructed image was decomposed into a baseline component and another component that varied along the z-axis. The baseline component of the image noise was highest at the center of the reconstructed image and decreased toward the edges. The normalized 2D power spectra for each pitch were almost identically distributed. Furthermore, the ratios of the 2D power spectra for IMR and FBP at different pitch values were obtained. The magnitudes of the components varying along the z-axis were smallest at the center of the reconstructed image and increased toward the edge. The ratios of the 3D power spectra on the f_x axis for IMR and FBP at different pitch values were obtained. The results showed that the effect of the pitch was related to the component that varied along the z-axis. Furthermore, the pitch had a smaller effect on IMR than on FBP.

Nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of cardiovascular events. Although the association between NAFLD and aortic valve sclerosis has been described, the prevalence and prognostic implications of NAFLD among patients with severe aortic stenosis (AS) have not been described. In addition, the effect of the presence of severe tricuspid regurgitation (TR) on the prevalence of NAFLD remains unexplored. Accordingly, we investigated the prognostic implications of NAFLD among patients with severe AS with and without concomitant significant TR. A total of 538 patients (aged 80 ± 7 y, 49.6% men) who underwent noncontrast computed tomography before transcatheter aortic valve implantation (TAVI) between 2007 and 2019 were included. NAFLD was defined as a liver-to-spleen attenuation ratio <1.0 on noncontrast computed tomography. NAFLD was present in 118 patients (21.9%). There were no significant differences in pulmonary arterial pressure, right atrial pressure, or the prevalence of significant TR between patients with and without NAFLD. During a median follow-up of 47 months (interquartile range 20 to 70 months), 224 patients (41.6%) died. Univariate Cox regression analysis demonstrated that NAFLD was not significantly associated with all-cause death among patients treated with TAVI (hazard ratio 1.32, 95% confidential interval 0.97 to 1.79, p = 0.07). In conclusion, among patients with severe AS who underwent TAVI, the prevalence of significant TR and the clinical outcomes were similar in patients with and without NAFLD.

The aim of this study was to evaluate whether patients with nonalcoholic fatty liver disease (NAFLD) have more myocardial malperfusion on CT myocardial perfusion imaging (CT-MPI), as well as to further assess if NAFLD is a predictor of myocardial ischemia independently.

A total of 310 consecutive patients were included for analysis. All patients were divided into two groups according to the presence or absence of NAFLD, which was diagnosed by noncontrast cardiac CT partially covered liver and spleen. Clinical characteristics as well as imaging features including coronary artery calcium score, CCTA, and CT-MPI findings were analyzed. Univariable and multivariable logistic regression analyses were used to find out the relationship between NAFLD and myocardial ischemia.

NAFLD (unadjusted hazard ratio [HR]: 2.4, 95% confidence interval [CI]: 1.2 to 4.4, p = 0.008), male (HR: 2.6, 95% CI: 1.5 to 4.5, p = 0.001), obstructive CAD (HR: 2.3, 95% CI: 1.3 to 4.2, p = 0.004), and FAI ≥ -70.1 HU (HR: 3.1, 95% CI: 1.8 to 5.5, p < 0.001) were associated with myocardial ischemia in univariable analysis. After adjusting for traditional CAD risk factors and CT characteristics in the multivariable regression analysis, NAFLD (HR: 2.3, 95% CI: 1.2 to 4.4, p = 0.016) was an independent predictor of myocardial ischemia.

Our data suggest that myocardial ischemia was more prevalent in patients with NAFLD, and NAFLD is a predictor of myocardial ischemia independent of traditional cardiovascular risk factors and CCTA characteristics.

• NAFLD patients had higher calcium score, incidence of obstructive coronary artery disease, grade of CAD-RADS, quantitative plaque characteristics, and incidence of fat attenuation index ≥ -70.1 HU. • NAFLD patients had a higher incidence of myocardial ischemia, myocardial hypoperfusion, and hypoperfusion myocardial segments ratio. • NAFLD was a predictor of myocardial ischemia, independent of traditional cardiovascular risk factors, and CCTA characteristics.