Test-Retest Reliability of the Assessment of Fatty Liver Disease Using Low-Dose Computed Tomography in Cardiac Patients

Fatty Liver Grading Using Computed Tomography Hounsfield Unit Values and Correlating With Ultrasonography Grading

Introduction Fatty liver disease (FLD), characterized by excessive fat accumulation in hepatocytes, is a growing public health concern linked to obesity, diabetes mellitus, and metabolic syndrome. Accurate assessment of FLD severity is essential for early diagnosis, monitoring disease progression, and implementing appropriate therapeutic interventions. This study aimed to compare the grading of FLD using computed tomography (CT) Hounsfield unit (HU) values with ultrasonography (USG) grading to determine the correlation and diagnostic accuracy between these two imaging modalities. Methods A cross-sectional observational study was conducted at Sri Devaraj Urs Medical College, Tamaka, Kolar, involving 110 adult participants aged 18 years and older. Participants undergoing general health check-ups or presenting with non-specific abdominal pain were included, provided they had both abdominal CT and USG imaging within a one-week timeframe. Exclusion criteria encompassed significant liver diseases, recent liver-related interventions, contraindications to CT imaging, and pregnancy. USG was performed using a Philips EPIQ 5G machine (Philips Ultrasound Inc., Washington, USA), grading FLD on a scale from Grade 0 (normal) to Grade III (severe). Subsequently, unenhanced CT scans were conducted using a Siemens 128-slice dual-source CT scanner (Siemens Healthineers, Munich, Germany), measuring liver attenuation values in HU. Statistical analyses included Spearman's rank correlation, analysis of variance (ANOVA), and receiver operating characteristic (ROC) curve analysis using IBM SPSS Statistics for Windows, Version 25 (Released 2017; IBM Corp., Armonk, New York, United States). Results The study population consisted of 60 (54.5%) males and 50 (45.5%) females with a mean age of 45.3 years and a mean BMI of 27.8 kg/m². USG grading revealed Grade 0 in 20 cases (18.2%), Grade I in 40 cases (36.4%), Grade II in 35 cases (31.8%), and Grade III in 15 cases (13.6%) of FLD. Mean CT HU values inversely correlated with USG grades, ranging from 65.2 ± 5.3 HU in Grade 0 to 35.6 ± 5.5 HU in Grade III. A significant negative correlation was observed between CT HU values and USG grading (Spearman's rho = -0.65, p < 0.001). CT demonstrated high diagnostic accuracy, with sensitivity and specificity of 80% and 85% for Grade I, 90% and 90% for Grade II, and 95% and 95% for Grade III FLD, respectively. The ROC analysis yielded an AUC of 0.85 (95% CI: 0.78-0.92) at an optimal cut-off value of 40 HU, achieving 90% sensitivity and 80% specificity in diagnosing moderate to severe FLD. Subgroup analyses indicated substantial agreement between CT and USG grading (Kappa = 0.72) and a significant association between higher BMI and increased FLD severity. Conclusion CT HU values exhibit a strong inverse correlation with USG grading of FLD and demonstrate high diagnostic accuracy, particularly in detecting moderate to severe steatosis. These findings support the use of CT as a reliable quantitative tool for FLD assessment, complementing the qualitative nature of USG. Integrating CT measurements into the diagnostic workflow can enhance the accuracy of FLD grading, especially in cases where USG limitations are present, thereby improving patient management and outcomes.

Point-of-care β-hydroxybutyrate measurement predicts adequate glucose metabolism suppression in cardiac FDG-PET/CT

AIMS

The aims of our study were to evaluate whether point-of-care β-hydroxybutyrate (BHB) measurement can be used to identify patients with adequate cardiac glucose metabolism suppression for cardiac [18F]-fluoro-2-deoxy-d-glucose-positron emission tomography with computerized tomography (FDG-PET/CT) and to develop a pretest probability calculator of myocardial suppression using other metabolic factors attainable before imaging.

METHODS AND RESULTS

We recruited 193 patients with any clinical indication for whole body [18F]-FDG-PET/CT. BHB level was measured with a point-of-care device. Maximal myocardial standardized uptake value using lean body mass (SULmax) was measured from eight circular regions of interest with 1 cm circumference and background from left ventricular blood pool. Correlations SULmax and point-of-care measured BHB were analysed. The ability of BHB test to predict adequate suppression was evaluated with receiver operating characteristic analysis. Liver and spleen attenuation in computed tomography were measured to assess the presence of fatty liver. BHB level correlated with myocardial uptake and, using a cut-off value of 0.35 mmol/L to predict adequate myocardial suppression, we reached specificity of 90% and sensitivity of 56%. Other variables to predict adequate suppression were diabetes, obesity, ketogenic diet and fatty liver. Using information attainable before imaging, we created a pretest probability calculator of inadequate myocardial glucose metabolism suppression. The area under the curve for BHB test alone was 0.802 and was 0.857 for the pretest calculator (p = 0.319).

CONCLUSIONS

BHB level measured with a point-of-care device is useful in predicting adequate myocardial glucose metabolism suppression. More detailed assessment of other factors potentially contributing to cardiac metabolism is needed.

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.

Validation of a Semiautomatic Image Analysis Software for the Quantification of Musculoskeletal Tissues

Accurate quantification of bone, muscle, and their components is still an unmet need in the musculoskeletal field. Current methods to quantify tissue volumes in 3D images are expensive, labor-intensive, and time-consuming; thus, a reliable, valid, and quick application is highly needed. Tissue Compass is a standalone software for semiautomatic segmentation and automatic quantification of musculoskeletal organs. To validate the software, cross-sectional micro-CT scans images of rat femur (n = 19), and CT images of hip and abdomen (n = 100) from the Osteoporotic Fractures in Men (MrOS) Study were used to quantify bone, hematopoietic marrow (HBM), and marrow adipose tissue (MAT) using commercial manual software as a comparator. Also, abdominal CT scans (n = 100) were used to quantify psoas muscle volumes and intermuscular adipose tissue (IMAT) using the same software. We calculated Pearson's correlation coefficients, individual intra-class correlation coefficients (ICC), and Bland-Altman limits of agreement together with Bland-Altman plots to show the inter- and intra-observer agreement between Tissue Compass and commercially available software. In the animal study, the agreement between Tissue Compass and commercial software was r > 0.93 and ICC > 0.93 for rat femur measurements. Bland-Altman limits of agreement was - 720.89 (- 1.5e+04, 13,074.00) for MAT, 4421.11 (- 1.8e+04, 27,149.73) for HBM and - 6073.32 (- 2.9e+04, 16,388.37) for bone. The inter-observer agreement for QCT human study between two observers was r > 0.99 and ICC > 0.99. Bland-Altman limits of agreement was 0.01 (- 0.07, 0.10) for MAT in hip, 0.02 (- 0.08, 0.12) for HBM in hip, 0.05 (- 0.15, 0.25) for bone in hip, 0.02 (- 0.18, 0.22) for MAT in L1, 0.00 (- 0.16, 0.16) for HBM in L1, and 0.02 (- 0.23, 0.27) for bone in L1. The intra-observer agreement for QCT human study between the two applications was r > 0.997 and ICC > 0.99. Bland-Altman limits of agreement was 0.03 (- 0.13, 0.20) for MAT in hip, 0.05 (- 0.08, 0.18) for HBM in hip, 0.05 (- 0.24, 0.34) for bone in hip, - 0.02 (- 0.34, 0.31) for MAT in L1, - 0.14 (- 0.44, 0.17) for HBM in L1, - 0.29 (- 0.62, 0.05) for bone in L1, 0.03 (- 0.08, 0.15) for IMAT in psoas, and 0.02 (- 0.35, 0.38) for muscle in psoas. Compared to a conventional application, Tissue Compass demonstrated high accuracy and non-inferiority while also facilitating easier analyses. Tissue Compass could become the tool of choice to diagnose tissue loss/gain syndromes in the future by requiring a small number of CT sections to detect tissue volumes and fat infiltration.