Quantitative ultrasound and machine learning for assessment of steatohepatitis in a rat model

Tang, An; Destrempes, François; Kazemirad, Siavash; García-Duitama, Julián; Nguyen, Bich; Cloutier, Guy

doi:10.1007/s00330-018-5915-z

Cited by 36 publications

(31 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, several elastography studies have reported sensitivity to identifying NASH with fibrosis in patients with biopsy-proven NAFLD [41][42][43] . However, recent reports have shown that the sensitivity is improved considerably when SWE information is combined with quantitative US measures of tissue scattering 44 . Franceschini et al combined spectral-based quantities with SWE to improve classification performance 45 .…”

Section: Discussionmentioning

confidence: 99%

Multiparametric ultrasound imaging for the assessment of normal versus steatotic livers

Basavarajappa

Baek

Reddy

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Liver disease is increasing in prevalence across the globe. We present here a multiparametric ultrasound (mpUS) imaging approach for assessing nonalcoholic fatty liver disease (NALFD). This study was performed using rats (N = 21) that were fed either a control or methionine and choline deficient (MCD) diet. A mpUS imaging approach that includes H-scan ultrasound (US), shear wave elastography, and contrast-enhanced US measurements were then performed at 0 (baseline), 2, and 6 weeks. Thereafter, animals were euthanized and livers excised for histological processing. A support vector machine (SVM) was used to find a decision plane that classifies normal and fatty liver conditions. In vivo mpUS results from control and MCD diet fed animals reveal that all mpUS measures were different at week 6 (P < 0.05). Principal component analysis (PCA) showed that the H-scan US data contributed the highest percentage to the classification among the mpUS measurements. The SVM resulted in 100% accuracy for classification of normal and high fat livers and 92% accuracy for classification of normal, low fat, and high fat livers. Histology findings found considerable steatosis in the MCD diet fed animals. This study suggests that mpUS examinations have the potential to provide a comprehensive estimation of the main components of early stage NAFLD.

show abstract

Section: Discussionmentioning

confidence: 99%

Multiparametric ultrasound imaging for the assessment of normal versus steatotic livers

Basavarajappa

Baek

Reddy

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Additionally, combination of different envelope statistics parametric imaging techniques, and combination of envelope statistics parametric imaging techniques with other QUS imaging techniques may be explored in future developments, in order to improve the performance of hepatic steatosis characterization. For instance, a recent study combined HK imaging with ultrasound elastography for a better characterization of nonalcoholic steatohepatitis of a rat model [54].…”

Section: Discussionmentioning

confidence: 99%

“…The HK model has been applied to characterizing cell pellet biophantoms [43], tissue phantom heated by focused ultrasound [44], reperfused infarcted porcine myocardium in vivo [45], mice breast cancer in vivo [46], human breast lesions in vivo [47,48], response of advanced human breast cancer to neoadjuvant chemotherapy in vivo [49], cancerous human lymph nodes ex vivo [50], porcine red blood cell aggregation ex vivo [51], human carotid artery plaque in vivo [52], human skin ulcer ex vivo [53], nonalcoholic steatohepatitis of rats in vivo [54], and hepatic steatosis of rabbit livers ex vivo [55] and rat livers in vivo [20]. Using a rat model, Ghoshal et al [55] demonstrated that there is a significant increase in the HK µ parameter with increasing fat content in the liver samples.…”

Section: Ultatrasound Homodyned-k Imagingmentioning

confidence: 99%

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Zhou

Zhang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Hepatic steatosis is a key manifestation of non-alcoholic fatty liver disease (NAFLD). Early detection of hepatic steatosis is of critical importance. Currently, liver biopsy is the clinical golden standard for hepatic steatosis assessment. However, liver biopsy is invasive and associated with sampling errors. Ultrasound has been recommended as a first-line diagnostic test for the management of NAFLD. However, B-mode ultrasound is qualitative and can be affected by factors including image post-processing parameters. Quantitative ultrasound (QUS) aims to extract quantified acoustic parameters from the ultrasound backscattered signals for ultrasound tissue characterization and can be a complement to conventional B-mode ultrasound. QUS envelope statistics techniques, both statistical model-based and non-model-based, have shown potential for hepatic steatosis characterization. However, a state-of-the-art review of hepatic steatosis assessment using envelope statistics techniques is still lacking. In this paper, envelope statistics-based QUS parametric imaging techniques for characterizing hepatic steatosis are reviewed and discussed. The reviewed ultrasound envelope statistics parametric imaging techniques include acoustic structure quantification imaging, ultrasound Nakagami imaging, homodyned-K imaging, kurtosis imaging, and entropy imaging. Future developments are suggested.

show abstract

“…They reported that the AUC reached up to 0.94 in patients without suspected fibrosis, but dropped significantly in patients with suspicion of fibrosis (AUC: 0.60). Tang et al further explored the relationship between a quantitative ultrasound‐based machine learning model and histopathology scoring in a rat model 78 . Their results demonstrated that combining quantitative ultrasound parameters with conventional shear wave elastography significantly improved the classification accuracy of steatohepatitis, liver steatosis, inflammation and fibrosis.…”

Section: Radiomics In the Diagnosis And Staging Of Liver Diseasesmentioning

confidence: 99%

Radiomics in liver diseases: Current progress and future opportunities

Wei

Jiang

et al. 2020

Liver International

View full text Add to dashboard Cite

Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision‐making. Radiomics could reflect the heterogeneity of liver lesions via extracting high‐throughput and high‐dimensional features from multi‐modality imaging. Machine learning algorithms are then used to construct clinical target‐oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver‐specific feature extraction, to task‐oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.

show abstract

Quantitative ultrasound and machine learning for assessment of steatohepatitis in a rat model

Cited by 36 publications

References 48 publications

Multiparametric ultrasound imaging for the assessment of normal versus steatotic livers

Multiparametric ultrasound imaging for the assessment of normal versus steatotic livers

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Radiomics in liver diseases: Current progress and future opportunities

Contact Info

Product

Resources

About