Introduction

Early detection and staging of liver steatosis is important for establishing prognosis of patients with fatty liver.
The aim of this study was to evaluate two new quantitative ultrasound (QUS) parameters, TSI (tissue scatter-distribution imaging) and TAI (tissue attenuation imaging) for steatosis assessment, as compared to controlled attenuation parameter (CAP).

Aims & Methods

A prospective study was conducted in which liver steatosis was assessed in the same session by QUS (Samsung Medison RS85, CA1-7A probe) and CAP (FibroScan Compact M 530, M/XL probes) in 275 patients [59%(162) males, mean age 55.9±12.2 years]. Reliable measurements were defined for CAP as the median value of 10 measurements with IQR/M<0.3. For QUS, five consecutive measurements of TAI and TSI were acquired by a color-coded map overlaid on B-mode ultrasound. TAI and TSI were automatically calculated and considered reliable when reliability index, R²>0.6. The CAP cut-off value to identify the presence of at least mild steatosis was 248 dB/m.

Results

Reliable measurements were obtained in 100% of cases both by CAP and TAI/TSI. Moderate correlations were observed between TSI vs. CAP r=0.56, and TSI vs. TAI, r=0.49. The best cut-off values to identify at least mild steatosis were: for TAI>0.73 (AUROC=0.87, p<0.0001, Se=67.6%, Sp=95.1%, PPV=96.9%, NPV=56.1%), for TSI>96.5 (AUROC=0.84, p<0.0001, Se=76.9%, Sp=82.9%, PPV=91.1%, NPV=61.3%).

Conclusion

TAI and TSI are feasible methods for assessing liver steatosis, with good accuracy to diagnose at least mild steatosis

References

[Karlas T, et al- J Hepatol. 2017 May;66(5):1022-1030].

Introduction

Liver steatosis is a growing global health concern with significant medical and economic implications. This study aimed to evaluate USFF (ultrasound fat fraction) for steatosis assessment as compared to controlled attenuation parameter (CAP).

Aims & Methods

A prospective study was conducted in which liver steatosis was assessed in the same session by QUS (Samsung Medison RS85, CA1-7S probe) and CAP (FibroScan Compact M 530, M/XL probes) in a cohort of 145 patients with mixed etiologies. For QUS, five consecutive measurements of TAI and TSI were acquired by a color-coded map overlaid on B-mode ultrasound, then fat fraction (%) was automatically calculated and displayed in the report. The CAP cut-off values for S1, S2 and S3 were 248, 268 and 280 dB/m. ROC analysis was done to identify the best cut-off values for USFF for each steatosis stages. The correlations were assessed using Pearson correlation coefficient

Results

From the 169 patients, 56.2% were men, 52.6% had at least moderate steatosis, with a mean age of 50.9 ± 16.3years. Moderate correlations were observed between TSI vs. CAP r=0.66, p<0.0001, TAI vs. CAP r=0.83, p<0.0001 and TSI vs. TAI r=0.68, p<0.0001, with good performance in diagnosing liver steatosis (TSI AUROC= 0.12, p<0.0001 and TAI AUROC=0.81, p< 0.0001). A very good correlation between USFF and CAP was also found, r=0.81, p<0.001.
The best USFF cut-off value to identify at least mild steatosis in all patients was >5% with very good performance (AUROC=0.90, p<0.0001, Se=86.7%, Sp=79.3%), for moderate steatosis the cut-off value was >7.5% (AUROC=0.92, p<0.0001, Se=83.8%, Sp=86.2%) and for severe steatosis, a cut-off of >10% (AUROC=0.91, p<0.0001, Se=70.1%, Sp=93.3%).

Conclusion

USFF seems to be a promising tool for diagnosing liver steatosis, with a very good performance, ready to be used in daily practice.

Introduction

The aim of this study was to evaluate and to establish cut-off values for two new quantitative ultrasound (QUS) parameters, TSI (tissue scatter-distribution imaging) and TAI (tissue attenuation imaging) in early diagnosis of liver steatosis (LS), as compared to controlled attenuation parameter (CAP).

Aims & Methods

A prospective study was conducted in which LS was assessed in the same session by QUS (Samsung Medison RS85,CA1-7A probe) and CAP (FibroScan Compact M530, M/XL probes). Reliable measurements were defined for CAP the median value of 10 measurements with IQR/M<0.3, and for QUS, five consecutive measurements of TAI and TSI acquired by a color-coded map. TAI and TSI were automatically calculated and considered reliable when reliability index, R²>0.6. The CAP cut-off value used for the whole cohort for at least mild steatosis (S1) were 248 dB/m[1]; for ALD (alcoholic liver disease) cohort, 268 dB/mm[2]; for NAFLD(non-alcoholic liver disease) cohort 294dB/m[2]. Demographic and health related datas were recorded.

Results

A total of 285 patients, with a mean age of 56.1±12.4, 114 female and 171 male, were included in the study. According to aetiology, 164 (57.5%) patients had NAFLD, 61(21.4%) ALD, and 60 (21.1%) were other etiologies (viral, cardiac, autoimmune). The obtained TSI and TAI cut-off values for the diagnosis of at least mild steatosis (S1) are presented in Table 1.
Table 1. Cut-off values of TAI and TSI for S1

Variable		Overall	NAFLD	ALD
TSI	S1	>96.2 AUC=0.74,p<0.0001, Se=87.9%,Sp=53.0%;	>96.5 AUC=0.73, p<0.0001, Se=90.9%, Sp=47.9%;	>94.9 AUC=0.70, p=0.003 Se=72.9%,Sp=75.0%;
TAI	S1	>0.73 AUC=0.82,p<0.0001, Se=78.3%,Sp=71.6%;	>0.75 AUC=0.81, p<0.0001, Se=57.5%, Sp=90.8%;	>0.66 AUC=0.74, p<0.0001, Se=84.8%,Sp=57.1%;

There were no differences between the performance of TAI and TSI according to the DeLong test, p=0.18 and p=0.24. A strong direct correlation was observed between TAI and CAP r=0.701, moderate between TSI and CAP r=0.56 for the all cohort, but for NAFLD and ALD subgroups moderate correlations were found between TAI and CAP r=0.66, and r=0.66 respectively and TSI and CAP r=0.56 and 0.66, respectively.
In univariate regression analysis, the factors associated with TSI were hypertension, Diabettes mellitus(DM), and obesity, and all p-values were <0.0001, but in multivariate analysis, only hypertension(p=0.001) and obesity(p=0.0005) were associated. For TAI, in univariate analysis, the same factors: hypertension(p=0.004), obesity(p=0.0006) and DM (p=0.002) were associated, but in multivariate analysis, DM was the only factor associated, p=0.02. Age and gender were not correlated with any method, TSI or TAI.

Conclusion

TAI and TSI are feasible non-invasive methods for screening and diagnosis of liver steatosis, with good accuracy. Patients with NAFLD presented highest cut-off values, probably by the presence of DM and obesity, conditions independently associated with TAI and TSI values.

References

1.Karlas T, Petroff D, Sasso M, Fan JG, Mi YQ, de Lédinghen Vet al. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis. J Hepatol. 2017 May;66(5):1022-1030. doi: 10.1016/j.jhep.2016.12.022. Epub 2016 Dec 28. PMID: 28039099.
2. Petroff D, Blank V, Newsome PN, Shalimar, Voican CS, Thiele M, et al. Assessment of hepatic steatosis by controlled attenuation parameter using the M and XL probes: an individual patient data meta-analysis. Lancet Gastroenterol Hepatol. 2021 Mar;6(3):185-198. doi: 10.1016/S2468-1253(20)30357-5. Epub 2021 Jan 16. PMID: 33460567.

Introduction

This research focuses on analyzing ultrasound-based management in percutaneous interventions for liver abscesses.

Aims & Methods

A retrospective analysis was carried out on patients diagnosed with liver abscesses between 2010 and 2024, in a tertiary-level Gastroenterology and Hepatology department. Information regarding demographics, existing risk factors, etiological factors of liver abscess, and patient outcomes was collected and examined.

Results

Among 187 cases, patients were categorized into three groups based on treatment approach: Group A (60/187, 32.1%): Percutaneous aspiration for bacteriological sampling and abscess evacuation. Group B (30/187, 16.0%): Continuous drainage via catheter insertion. Group C (46/187, 24.6%): Conservative management with empirical antibiotic therapy. Length of Antibiotic Therapy (days): A (15.78 ± 8.35) vs. B (18.36 ± 7.96) vs. C (15.8 ± 8.55), p = 0.01. Hospital Stay: A (14.89 ± 8.29) vs. B (18.36 ± 7.96) vs. C (15.3 ± 8.12), p = 0.01. Complication Rates (Acute Kidney Injury - AKI): A (5/64, 7.2%) vs. B (0%) vs. C (2/46, 4.3%), p = 0.5. Mean Abscess Size: B (9.3 ± 2.60) > A (6.05 ± 2.16) > C (4.47 ± 2.01), p < 0.001. Two-Week Follow-Up: Complete remission: A (39.0%) vs. B (20.0%) vs. C (56.5%), p < 0.001. Stationary cases: A (39.1%) vs. B (3.3%) vs. (50.0%), p = 0.005.

Conclusion

Percutaneous drainage proved to be the most effective approach for managing larger liver abscesses. While no significant differences were observed among the three groups regarding hospital stay and complication rates, conservative treatment showed a higher proportion of stationary cases during follow-up.

Introduction

Metabolic Associated Steatotic Liver Disease (MASLD) affects 25-30% of the European population and is projected to rise further. These patients are at increased risk for liver-related complications and higher overall mortality, emphasizing the need for early detection to improve outcomes. While abdominal ultrasound serves as the primary imaging modality for suspected liver disease, its sensitivity for diagnosing mild steatosis is inadequate, and gold-standard methods for quantifying liver steatosis infiltration, such as magnetic resonance imaging or liver biopsy are impractical for widespread use.

Aims & Methods

This study aimed to assess the diagnostic accuracy of the Attenuation Coefficient (ATT) as a complementary method to B-mode ultrasound for early detection of hepatic steatosis. Consecutive patients referred for transient elastography were prospectively enrolled. Liver steatosis was graded using a 4-point scale (normal, mild, moderate, or severe) in B-mode ultrasound for each patient, and ATT was measured in accordance with manufacturer quality criteria instructions (Fujifilm®). Additionally, the Hepato-Renal Index (HRI) was calculated for each patient, and Controlled Attenuation Parameter (CAP, Fibroscan®) was measured.

Results

A total of 128 patients, 61.7% male, with a mean age of 56.8±12.8 years, were included. Among them, 78.1% exhibited at least one cardiometabolic risk factor. The main referral indications were fibrosis assessment in viral hepatitis (42.2%), MASLD (21.9%), and alcoholic liver disease (11.7%). The median vibration-controlled elastography result was 5.8kPa. B-mode ultrasound steatosis assessment classified 44.5% as normal, 10.2% with mild steatosis, 21.1% with moderate steatosis, and 24.2% with severe steatosis. The area under the ROC curve for steatosis detection was 0.796 for HRI, 0.894 for ATT, and 0.896 for CAP, highlighting robust diagnostic performance, particularly for the latter two methods. There was a strong correlation between ATT and CAP (r=0.710). Using a CAP>248dB/m to define steatosis, the area under the ROC curve for ATT was 0.890. For an ATT cutoff value ≥0.66, Sensitivity, Specificity, Negative Predictive Value, and Positive Predictive Value were 82.7%, 77.3%, 72.3%, and 86.1%, respectively. The sensitivity of B-mode ultrasound for steatosis detection was 79.5%, increasing to 90.4% when combined with ATT measurement.

Conclusion

ATT demonstrated excellent accuracy for steatosis detection, comparable to CAP and superior to HRI. Importantly, combining B-mode ultrasound with ATT significantly improved the diagnostic sensitivity. These findings suggest that ATT has the potential to become a valuable non-invasive tool alongside ultrasound for early detection of hepatic steatosis, aiding in earlier intervention and improved patient outcomes.

References

Tamaki N, Koizumi Y, Hirooka M, et al. Novel quantitative assessment system of liver steatosis using a newly developed attenuation measurement method. Hepatol Res. 2018;48(10):821-828. doi:10.1111/hepr.13179
Bozic D, Podrug K, Mikolasevic I, Grgurevic I. Ultrasound Methods for the Assessment of Liver Steatosis: A Critical Appraisal. Diagnostics. 2022;12(10):2287. doi:10.3390/diagnostics12102287

Introduction

Multiparametric Ultrasound (MP-US) is emerging as a key tool for liver disease assessment and management, offering rapid, non-invasive insights into tissue characteristics, usually accessible only through liver histology^1,2. Steatosis and inflammation, main factors in fibrosis and cirrhosis progression, should be ideally detected and quantified early, aiding in the diagnosis and risk assessment of chronic liver disease (CLD)³. MP-US combines ultrasound wave propagation techniques: Shear Wave Dispersion (SWD) evaluates frequency-dependent changes in wave velocity reflecting inflammation-related viscosity; Shear Wave Elastography (SWE) estimates liver stiffness as indirect marker of fibrosis; Attenuation Ultrasound (USAT) and speed of sound (TSSI) assess hepatic fat content measuring energy loss and sound speed^3,4.

Aims & Methods

The study aimed to assess the accuracy of USAT/TSSI, SWE and SWD measured by a novel MP-US device (Resona A20, Mindray) compared to liver histology to evaluate steatosis, fibrosis and inflammation.
This single-centre observational, prospective study conducted from May to November 2024 included adult patients (≥18 years) with suspected CLD, referred for MP-US evaluation and percutaneous liver biopsy.
Parameters were obtained in a single session by placing a 2–3 cm diameter Region of Interest (ROI) at least 1.5 cm below the liver capsule.
Five valid measurements - defined by a Motion Stability Index (M-STB) of 5 and a high reproducibility (green zone on colorimetric map) - were acquired for each technique, collecting mean, median, interquartile range (IQR), IQR/median, standard deviation (SD) and SD/mean^4,5.
Histology was the reference standard for grading inflammation, fibrosis and steatosis using defined scoring systems.

Results

A total of 78 patients (46 women; mean age 51.6 years) were included in the study: 42 with MASLD, 20 with PSVD or nonspecific cholangiopathies, 9 with AIH and 7 with PBC.
USAT values increased progressively with steatosis severity, showing statistically significant differences across all stages (S0 vs S1, S1 vs S2 and S0 vs S1–3; p < 0.001). ROC curve analysis demonstrated high diagnostic accuracy with an AUC of 0.801 for S0 vs S1, 0.977 for S1 vs S2 and peak performance for S0–1 vs S2–3 (AUC 0.992, sensitivity 100%, specificity 95.1%). Optimal USAT cut-off values to classify mild, moderate and severe steatosis were 0.55, 0.67, and 0.75, respectively.
Conversely, TSSI values decreased with increasing steatosis grade. ROC analysis revealed moderate diagnostic performance of TSSI in distinguishing S0 from S1 (AUC 0.77) and S0–1 from S2–3 (AUC 0.76). A moderate inverse correlation was observed between TSSI and USAT values (ρ = –0.53, p < 0.001).
SWE values increased with fibrosis stage according to the METAVIR scoring system, demonstrating good performance in detecting advanced fibrosis (p = 0.024). However, most patients exhibited either no fibrosis (F0) or mild fibrosis (F1) on liver histology, limiting SWE overall diagnostic performance.
Viscosity values showed a non-significant upward trend with increasing inflammation. Standard liver function tests demonstrated only a weak correlation with histological inflammation. Notably, SWD approached statistical significance in differentiating the presence of at least mild inflammation from no inflammation (A0 vs A123).

Conclusion

MP-US offers valuable non-invasive insights into liver tissue characteristics reflecting histological findings. Further studies are warranted to support the non-invasive assessment of key pathological features, potentially replacing liver biopsy.

References

1. Grgurevic, I., Tjesic Drinkovic, I. & Pinzani, M. Multiparametric ultrasound in liver diseases: an overview for the practising clinician. Postgrad Med J 95, 425–432 (2019).
2. Sumida, Y., Nakajima, A. & Itoh, Y. Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. World J Gastroenterol 20, 475–485 (2014).
3. Garcovich, M. et al. Shear Wave Dispersion in Chronic Liver Disease: From Physical Principles to Clinical Usefulness. Journal of Personalized Medicine 2023, Vol. 13, Page 945 13, 945 (2023).
4. Ferraioli, G. et al. WFUMB Guidelines/Guidance on Liver Multiparametric Ultrasound. Part 2: Guidance on Liver Fat Quantification. Ultrasound Med Biol 50, 1088–1098 (2024).
5. Ferraioli, G. et al. WFUMB Guideline/Guidance on Liver Multiparametric Ultrasound: Part 1. Update to 2018 Guidelines on Liver Ultrasound Elastography. Ultrasound Med Biol 50, 1071–1087 (2024).

Introduction

Endoscopic ultrasound (EUS) has made significant strides in recent decades, whose current application in liver is mainly concentrated in liver parenchymal diseases, vascular diseases and portal hypertension. Incorporating EUS into the hepatic examination process is advantageous, which requires the endoscopist to have a good grasp of the anatomy of the liver. However, the learning curve for EUS-guided operations is well known to be steep. Thus, in the evaluation of EUS liver, it is necessary to have a system assisting the endoscopist to complete the scan at each station. Based on the anatomical markers in the liver, we construct a system which can perform a comprehensive inspection of the liver segments through the scanning imaging of 8 stations.

Aims & Methods

EUS of the liver can be done from the stations as follows: Station 1: Left hepatic vein (LHV); Station 2: Umbilical potion of portal vein (PV); Station 3: PV trunk and common bile duct (CBD); Station 4: Middle hepatic vein (MHV) and venous ligament; Station 5: Inferior vena cava (IVC) and right hepatic vein (RHV); Station 6: Gallbladder (GB) and left and right branches of PV; Station 7: Anterior and posterior branches of right portal vein; Station 8: Right kidney.
The system was internally validated in images, and subsequently compared with the performance of EUS endoscopists. We retrospectively collected 2743 images from Hospital A for internal validation, and an independent dataset containing 340 images for human-machine competitions. To filter out the white light images, we apply a DCNN1 model primarily. The output ultrasound images are then fed into DCNN2. In this model, they are classified into standard and non-standard categories. And the DCNN3 is then activated with standard images. DCNN3 identifies certain features in the image and groups them into eight stations. We use accuracy (defined as the number of correctly classified images divided by the total number of images) to evaluate the performance of the model. In addition, a T-test with a significance level of 0.05 was performed to assess significant differences between endoscopists and the model.

Results

The accuracy of DCNN1 in white light image and EUS image classification reached 100% [200/200, 95%CI]. The accuracy of DCNN2 in standard and non-standard image classification reached 81.24%. For the classification of standard station images, the overall accuracy of DCNN3 on the internal image verification set was 92.52% [2572/2743, 95%CI]. In the man-machine competition, the system correctly classified the liver standard stations with an accuracy of 88.53% [301/340, 95%CI]. The average accuracy of the two specialists and ten endoscopists was 84.56% [575/680, 95%CI] and 74.99% [2548/3398, 95%CI], respectively. The P-value for this analysis was less than 0.0001, indicating a statistically significant difference between endoscopists and the model.

Conclusion

In this study, we have constructed an intelligence-assisted linear EUS system based on deep learning algorithms. The system allows for real-time localization of liver standard sub-stations and provides valuable guidance to endoscopists during procedures. The system's effectiveness has been extensively validated through internal validations using images and. Moreover, in a competition with experienced endoscopists, our system has demonstrated superior accuracy, comparable to that of EUS specialists. The result shows that the system is expected to overcome the difficulties of ultrasonographic interpretation among trainees in liver ultrasound endoscopy.