Reader agreement and accuracy of ultrasound features for hepatic steatosis

Hong, Cheng William; Marsh, Austin; Wolfson, Tanya; Paige, Jeremy S; Dekhordy, Soudabeh Fazeli; Schlein, Alexandra; Housman, Elise; Deiranieh, Lisa H.; Li, Charles Q.; Wasnik, Ashish P.; Jang, Hyun‐Jung; Dietrich, Christoph F.; Piscaglia, Fabio; Casola, Giovanna; O'Boyle, M K; Richman, Katherine; Valasek, Mark A.; André, Michael P.; Loomba, Rohit; Sirlin, Claude B.

doi:10.1007/s00261-018-1683-0

Cited by 18 publications

(19 citation statements)

References 37 publications

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“…This ratio is reported to be useful in diagnosing cases of ≥30% steatosis [14]. The USG criteria were the presence of liver brightness and posterior attenuation, with stronger echoes in the hepatic parenchyma in comparison to the renal parenchyma and vessel blurring [15]. USG is also an accurate, reliable tool for detecting ≥20–30% steatosis, with a sensitivity and specificity of 84.8% and 93.6%, respectively [16].…”

Section: Methodsmentioning

confidence: 99%

Fatty liver disease in living liver donors: a single‐institute experience of 220 donors

et al. 2021

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Summary We retrospectively reviewed 220 living liver donors, with a focus on the development of postoperative fatty liver. Data regarding demographics, comorbidities, imaging tests, operations and biopsies were obtained from medical records. We used unenhanced CT and USG to diagnose fatty liver. Donor candidates with fatty liver underwent weight loss intervention until imaging tests no longer demonstrated any features of fatty liver. Among 220 donors, 61 were diagnosed with preoperative fatty liver. The mean BMI of these 61 donors significantly decreased from 24.9 at the first visit to 23.6 kg/m2 immediately before surgery (P = 0.0386). A multivariate analysis revealed the following significant risk factors for postoperative fatty liver: male sex (P = 0.0033), BMI immediately before surgery (P = 0.0028) and a history of treatment for preoperative fatty liver (P = 0.0231). Postoperative fatty liver was often refractory to weight loss intervention. No improvement was observed in 14 of the 32 donors who had been diagnosed with fatty liver postoperatively, and one of the 14 donors even developed NASH. In conclusion, special attention should be paid to prevent fatty liver after surgery in male donors who show a high BMI immediately before surgery and with a history of treatment for preoperative fatty liver, and lifelong follow‐up is recommended.

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Section: Methodsmentioning

confidence: 99%

Fatty liver disease in living liver donors: a single‐institute experience of 220 donors

et al. 2021

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“…• Three views in transverse plane: hepatic veins at the confluence with the inferior vena cava, right portal vein, and right posterior portal vein • One view in sagittal plane: liver and kidney These views were selected based on their favorable performance in previous papers. 9,10 Importantly, each view is routinely obtained during clinical US exams of the liver.…”

Section: Us Protocolmentioning

confidence: 99%

“…4 Various liver US image features, including vessel obscuration, posterior beam attenuation, and elevated liver-kidney contrast ratio, have been associated with liver fat. 9,10 Accumulation of fat modifies backscattering properties of liver tissue, 11 making the liver brighter in comparison to neighboring tissues, such as the kidney. 12,13 In addition, blood vessels within the liver become blurred or obscured as liver fat increases due to signal attenuation.…”

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confidence: 99%

“…The rationale was that different views contain additional tissue features, such as blood vessels or kidney, which could potentially improve model performance. 9 In addition, we explored whether the deep learning model logic can be visually explained by employing the class activation mapping (CAM) technique to highlight regions in liver images weighted by the models for fatty liver diagnosis. 17…”

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confidence: 99%

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Liver Fat Assessment in Multiview Sonography Using Transfer Learning With Convolutional Neural Networks

Byra

Han

Boehringer

et al. 2021

J of Ultrasound Medicine

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Objectives-To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/ kidney).Methods-US images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth. Transfer learning with a deep convolutional neural network (CNN) was applied to develop models for diagnosis of fatty liver (PDFF ≥ 5%), diagnosis of advanced steatosis (PDFF ≥ 10%), and PDFF quantification for each liver view separately. In addition, an ensemble model based on all four liver view models was investigated. Diagnostic performance was assessed using the area under the receiver operating characteristics curve (AUC), and quantification was assessed using the Spearman correlation coefficient (SCC).Results-The most accurate single view was the right posterior portal vein, with an SCC of 0.78 for quantifying PDFF and AUC values of 0.90 (PDFF ≥ 5%) and 0.79 (PDFF ≥ 10%). The ensemble of models achieved an SCC of 0.81 and AUCs of 0.91 (PDFF ≥ 5%) and 0.86 (PDFF ≥ 10%).Conclusion-Deep learning-based analysis of US images from different liver views can help assess liver fat.

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“…It has been suggested that less portal venous blood flow and/or regional dilution of portal venous blood owing to anatomical variations of supplying vessels are present, therefore providing regional hepatocytes with less fatty substrates than the remaining parenchyma. In liver areas with reduced focal fat accumulation, fibrous tissue remodeling may occur, which seems to be caused by a different vascular supply compared with the rest of the liver [34,43,44,45,46,47,48,49,50,51]. However, the exact cause is unknown.…”

Section: Focal Fatty Sparingmentioning

confidence: 99%

Conventional ultrasound for diagnosis of hepatic steatosis is better than believed

et al. 2021

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Background Hepatic steatosis is a condition frequently encountered in clinical practice, with potential progression towards fibrosis, cirrhosis, and hepatocellular carcinoma. Detection and staging of hepatic steatosis are of most importance in nonalcoholic fatty liver disease (NAFLD), a disease with a high prevalence of more than 1 billion individuals affected. Ultrasound (US) is one of the most used noninvasive imaging techniques used in the diagnosis of hepatic steatosis. Detection of hepatic steatosis with US relies on several conventional US parameters, which will be described. US is the first-choice imaging in adults at risk for hepatic steatosis. The use of some scoring systems may add additional accuracy especially in assessing the severity of hepatic steatosis. Summary In the presented paper, we discuss screening and risk stratification, ultrasound features for diagnosing hepatic steatosis, B-mode criteria, focal fatty patterns and Doppler features of the hepatic vessels, and the value of the different US signs for the diagnosis of liver steatosis including classifying the severity of steatosis using different US scores. Limitations of conventional B-mode and Doppler features in the evaluation of hepatic steatosis are also discussed, including those in grading and assessing the complications of steatosis, namely fibrosis and nonalcoholic steatohepatitis. Key Messages Ultrasound is the first-line imaging examination for the screening and follow-up of patients with liver steatosis. The use of some scoring systems may add additional accuracy in assessing the severity of steatosis. Conventional B-mode and Doppler ultrasound have limitations in grading and assessing the complications of steatosis.

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Reader agreement and accuracy of ultrasound features for hepatic steatosis

Abstract: Large hepatic vein blurring, liver-kidney contrast, and overall impression provided the highest reader agreement. Large hepatic vein blurring may provide the highest classification accuracy for dichotomized grading of hepatic steatosis.

Cited by 18 publications

References 37 publications

Fatty liver disease in living liver donors: a single‐institute experience of 220 donors

Fatty liver disease in living liver donors: a single‐institute experience of 220 donors

Liver Fat Assessment in Multiview Sonography Using Transfer Learning With Convolutional Neural Networks

Conventional ultrasound for diagnosis of hepatic steatosis is better than believed

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