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Diagnostics
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Ultrasound Imaging in Digestive and Kidney Diseases
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Ultrasound Imaging in Digestive and Kidney Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 5106

Special Issue Editors

Dr. Zhuhuang Zhou

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Guest Editor
Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
Interests: biomedical ultrasonics; quantitative ultrasound for biological tissue characterization; ultrasound wave propagation in biological tissues; medical signal/image processing; artificial intelligence in medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Po-Hsiang Tsui

E-Mail Website
Guest Editor
Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan
Interests: ultrasound imaging; ultrasound scattering; ultrasound tissue characterization
Special Issues, Collections and Topics in MDPI journals
Dr. Ke Lv

E-Mail Website
Guest Editor
Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Interests: medical ultrasound; ultrasound diagnosis; contrast-enhanced ultrasound; ultrasound Doppler

Special Issue Information

Dear Colleagues,

Ultrasound imaging has been frequently used in the diagnosis and management of digestive and kidney diseases, such as hepatic steatosis and fibrosis, and in the treatment planning, guidance and monitoring of digestive and kidney diseases, such as hepatocellular carcinoma and pancreatic cancer. In recent years, advanced ultrasound techniques have been extensively investigated to broaden the application of and to promote the performance of ultrasound imaging in digestive and kidney diseases. These include, but are not limited to, novel quantitative ultrasound and “artificial intelligence (AI) + ultrasound” techniques.

This Special Issue aims to report on the cutting-edge ultrasound techniques in digestive and kidney diseases, from theoretical and simulation studies such as ultrasound wave propagation, to phantom and animal studies ex vivo and in vivo, and to clinical studies. Contributions related to ultrasound imaging techniques in digestive and kidney diseases are welcome.

Dr. Zhuhuang Zhou
Dr. Po-Hsiang Tsui
Dr. Ke Lv
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ultrasound imaging
  • quantitative ultrasound
  • ultrasound elastography
  • artificial intelligence
  • deep learning
  • machine learning
  • contrast-enhanced ultrasound
  • ultrasound Doppler
  • ultrasound wave propagation
  • ultrasound backscattering
  • digestive diseases
  • kidney diseases
  • liver diseases
  • pancreas diseases

Published Papers (4 papers)

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Research

15 pages, 1584 KiB  
Article
The Calculation and Evaluation of an Ultrasound-Estimated Fat Fraction in Non-Alcoholic Fatty Liver Disease and Metabolic-Associated Fatty Liver Disease
by Pál Novák Kaposi, Zita Zsombor, Aladár D. Rónaszéki, Bettina K. Budai, Barbara Csongrády, Róbert Stollmayer, Ildikó Kalina, Gabriella Győri, Viktor Bérczi, Klára Werling, Pál Maurovich-Horvat, Anikó Folhoffer and Krisztina Hagymási
Diagnostics 2023, 13(21), 3353; https://doi.org/10.3390/diagnostics13213353 - 31 Oct 2023
Viewed by 964
Abstract
We aimed to develop a non-linear regression model that could predict the fat fraction of the liver (UEFF), similar to magnetic resonance imaging proton density fat fraction (MRI-PDFF), based on quantitative ultrasound (QUS) parameters. We measured and retrospectively collected the ultrasound attenuation coefficient [...] Read more.
We aimed to develop a non-linear regression model that could predict the fat fraction of the liver (UEFF), similar to magnetic resonance imaging proton density fat fraction (MRI-PDFF), based on quantitative ultrasound (QUS) parameters. We measured and retrospectively collected the ultrasound attenuation coefficient (AC), backscatter-distribution coefficient (BSC-D), and liver stiffness (LS) using shear wave elastography (SWE) in 90 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD), and 51 patients with clinically suspected metabolic-associated fatty liver disease (MAFLD). The MRI-PDFF was also measured in all patients within a month of the ultrasound scan. In the linear regression analysis, only AC and BSC-D showed a significant association with MRI-PDFF. Therefore, we developed prediction models using non-linear least squares analysis to estimate MRI-PDFF based on the AC and BSC-D parameters. We fitted the models on the NAFLD dataset and evaluated their performance in three-fold cross-validation repeated five times. We decided to use the model based on both parameters to calculate UEFF. The correlation between UEFF and MRI-PDFF was strong in NAFLD and very strong in MAFLD. According to a receiver operating characteristics (ROC) analysis, UEFF could differentiate between <5% vs. ≥5% and <10% vs. ≥10% MRI-PDFF steatosis with excellent, 0.97 and 0.91 area under the curve (AUC), accuracy in the NAFLD and with AUCs of 0.99 and 0.96 in the MAFLD groups. In conclusion, UEFF calculated from QUS parameters is an accurate method to quantify liver fat fraction and to diagnose ≥5% and ≥10% steatosis in both NAFLD and MAFLD. Therefore, UEFF can be an ideal non-invasive screening tool for patients with NAFLD and MAFLD risk factors. Full article
(This article belongs to the Special Issue Ultrasound Imaging in Digestive and Kidney Diseases)
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16 pages, 5398 KiB  
Article
Pancreatic Ductal Adenocarcinoma: The Characteristics of Contrast-Enhanced Ultrasound Are Correlated with the Hypoxic Microenvironment
by Lan Wang, Ming Li, Tiantian Dong, Yuanyuan Li, Ci Yin and Fang Nie
Diagnostics 2023, 13(20), 3270; https://doi.org/10.3390/diagnostics13203270 - 20 Oct 2023
Viewed by 871
Abstract
A hypoxic microenvironment is associated with an increased risk of metastasis, treatment resistance and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). This study aimed to identify contrast-enhanced ultrasound (CEUS) characteristics that could predict the hypoxic microenvironment of PDAC. A total of 102 patients [...] Read more.
A hypoxic microenvironment is associated with an increased risk of metastasis, treatment resistance and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). This study aimed to identify contrast-enhanced ultrasound (CEUS) characteristics that could predict the hypoxic microenvironment of PDAC. A total of 102 patients with surgically resected PDAC who underwent CEUS were included. CEUS qualitative and quantitative characteristics were analyzed. The expression of hypoxia-inducible factor-1α (HIF-1) and glucose transporter-1 (GLUT1) were demonstrated by immunohistochemistry. The associations between CEUS characteristics and the HIF-1α and GLUT1 expression of PDACs were evaluated. We found that HIF-1α-high PDACs and GLUT1-high PDACs had a larger tumor size and were more prone to lymph node metastasis. There was a significant positive linear correlation between the expression of HIF-1α and GLUT1. CEUS qualitative characteristics including completeness of enhancement and peak enhancement degree (PED) were related to the expression of HIF-1α and GLUT1. A logistic regression analysis showed that tumor size, lymph node metastasis, incomplete enhancement and iso-enhancement of PED were independent predictors for HIF-1α-high PDACs and GLUT1-high PDACs. As for quantitative characteristics, HIF-1α-high PDACs and GLUT1-high PDACs showed higher peak enhancement (PE) and wash-in rate (WIR). CEUS can effectively reflect the hypoxia microenvironment of PDAC, which may become a noninvasive imaging biomarker for prognosis prediction and individualized treatment. Full article
(This article belongs to the Special Issue Ultrasound Imaging in Digestive and Kidney Diseases)
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10 pages, 6070 KiB  
Article
Evaluation of Liver Stiffness Measurement by Means of 2D-SWE for the Diagnosis of Esophageal Varices
by Bozhidar Hristov, Vladimir Andonov, Daniel Doykov, Katya Doykova, Siyana Valova, Emiliya Nacheva-Georgieva, Petar Uchikov, Gancho Kostov, Mladen Doykov and Eduard Tilkian
Diagnostics 2023, 13(3), 356; https://doi.org/10.3390/diagnostics13030356 - 18 Jan 2023
Cited by 3 | Viewed by 1262
Abstract
Portal hypertension (PH) and esophageal varices (EVs) are a matter of extensive research. According to current Baveno VII guidelines, in patients with compensated advanced chronic liver disease (cACLD), liver stiffness measurement (LSM) < 15 kPa and PLT count > 150 × 109 [...] Read more.
Portal hypertension (PH) and esophageal varices (EVs) are a matter of extensive research. According to current Baveno VII guidelines, in patients with compensated advanced chronic liver disease (cACLD), liver stiffness measurement (LSM) < 15 kPa and PLT count > 150 × 109/L, upper endoscopy (UE) is not mandatory, and the emphasis should be set on non-invasive methods for evaluation of clinically significant portal hypertension (CSPH). The aim of this study is to establish whether liver stiffness (LS) measured by 2D-SWE could be used as a predictor for the presence and severity of EVs in cirrhotic patients. In total, 86 patients of whom 32 with compensated liver cirrhosis (cLC) and 54 with decompensated liver cirrhosis (dLC) were examined in the Gastroenterology clinic of University hospital “Kaspela”, Plovdiv, Bulgaria. Each patient underwent LS assessment by 2D-SWE and EVs grading by UE. EVs were detected in 47 (54.7%) patients, 23 (49%) of them were stage 4-high-risk EVs (HREV). The cut-off value for LS that differentiates HREV from the rest was set at 2.49 m/s with 100% sensitivity and 100% specificity (AUC 1.000, CI 0.925). Conclusions: 2D-SWE can be used as a non-invasive method in the assessment of only high-grade esophageal varices. For the other grades, upper endoscopy remains the method of choice. Full article
(This article belongs to the Special Issue Ultrasound Imaging in Digestive and Kidney Diseases)
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11 pages, 2129 KiB  
Article
Evaluation of Hepatic Fibrosis Using Ultrasound Backscattered Radiofrequency Signals and One-Dimensional Convolutional Neural Networks
by Yong Huang, Yan Zeng, Guangyu Bin, Qiying Ding, Shuicai Wu, Dar-In Tai, Po-Hsiang Tsui and Zhuhuang Zhou
Diagnostics 2022, 12(11), 2833; https://doi.org/10.3390/diagnostics12112833 - 17 Nov 2022
Cited by 3 | Viewed by 1488
Abstract
The early detection of hepatic fibrosis is of critical importance. Ultrasound backscattered radiofrequency signals from the liver contain abundant information about its microstructure. We proposed a method for characterizing human hepatic fibrosis using one-dimensional convolutional neural networks (CNNs) based on ultrasound backscattered signals. [...] Read more.
The early detection of hepatic fibrosis is of critical importance. Ultrasound backscattered radiofrequency signals from the liver contain abundant information about its microstructure. We proposed a method for characterizing human hepatic fibrosis using one-dimensional convolutional neural networks (CNNs) based on ultrasound backscattered signals. The proposed CNN model was composed of four one-dimensional convolutional layers, four one-dimensional max-pooling layers, and four fully connected layers. Ultrasound radiofrequency signals collected from 230 participants (F0: 23; F1: 46; F2: 51; F3: 49; F4: 61) with a 3-MHz transducer were analyzed. Liver regions of interest (ROIs) that contained most of the liver ultrasound backscattered signals were manually delineated using B-mode images reconstructed from the backscattered signals. ROI signals were normalized and augmented by using a sliding window technique. After data augmentation, the radiofrequency signal segments were divided into training sets, validation sets and test sets at a ratio of 80%:10%:10%. In the test sets, the proposed algorithm produced an area under the receive operating characteristic curve of 0.933 (accuracy: 91.30%; sensitivity: 92.00%; specificity: 90.48%), 0.997 (accuracy: 94.29%; sensitivity: 94.74%; specificity: 93.75%), 0.818 (accuracy: 75.00%; sensitivity: 69.23%; specificity: 81.82%), and 0.934 (accuracy: 91.67%; sensitivity: 88.89%; specificity: 94.44%) for diagnosis liver fibrosis stage ≥F1, ≥F2, ≥F3, and ≥F4, respectively. Experimental results indicated that the proposed deep learning algorithm based on ultrasound backscattered signals yields a satisfying performance when diagnosing hepatic fibrosis stages. The proposed method may be used as a new quantitative ultrasound approach to characterizing hepatic fibrosis. Full article
(This article belongs to the Special Issue Ultrasound Imaging in Digestive and Kidney Diseases)
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