Recent Advances in Metabolomics Analysis of Fatty Liver Disease and Liver Fibrosis

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2694

Special Issue Editors

Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, Tamil Nadu, India
Interests: biomedical imaging; data analytics; machine learning; deep learning

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Department of Toxicology, Poznan University of Medical Sciences, Dojazd 30, 60-631 Poznań, Poland
Interests: Parkinson's disease; gut–brain axis; urolithin A; neuroprotection; GQDs
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Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu City, Taiwan
Interests: advanced nanomaterials and nanoparticles, MEMS sensing design; hardware/EE/RF circuit and IC design; antenna/ microwave wireless design; EMC/EMI design; millimeter-wave and terahertz communication; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Chronic and acute liver diseases have increased in terms of mortality rate in many countries. As one of the most deadly malignancies worldwide, fatty liver disease and liver fibrosis pose great challenges to early diagnosis and prognosis due to their heterogeneity. Fat is replaced by more than 5-6 percent of normal liver tissue in cases of fatty liver disease (FLD). A condition called steatohepatitis can result from fat accumulation in FLD, causing inflammation, cell death, and scarring. The untreated condition of steatohepatitis can lead to liver fibrosis, which causes a build-up of scar tissue and diminished blood flow to the liver. Hepatitis fibrosis can result in liver cancer, liver failure, or cirrhosis without treatment. Metabolomics is an indespensable tool when aiming to obtain a comprehensive understanding of the liver's function. Researchers and healthcare providers can rely on Metabolon to provide the automated diagnostic and prognostic indicators needed to better understand the molecular mechanisms underlying pathological processes as the incidence of certain liver diseases continues to rise, including fatty liver disease and liver fibrosis. The purpose of this Special Issue is to define the most current scenarios regarding clinical and technological research on fatty liver disease and liver fibrosis. Topics covered in this Special Issue include (but are not limited to):

  • Evolutionary analysis of liver diseases;
  • Analyzing the metabolic profiles of patients with metabolic dysfunction;
  • An analysis of metabolic changes that occur during liver fibrosis;
  • Detection of fatty liver disease and liver fibrosis through computer-aided diagnosis;
  • Optimization and control of the complex liver disease;
  • Classification methods for liver problems that are robust;
  • Parameter identification of fatty liver disease and liver fibrosis;
  • Data-driven modelling and simulation of liver diseases;
  • Machine learning techniques in the model and simulation of liver diseases;
  • Medical imaging technologies and biological modeling of liver diseases;
  • Bioinformatics methods for detecting liver disease in imaging and medical data;
  • Analysis of medical images to diagnose liver disease;
  • Diagnostics of liver disease by means of medical signal processing

Dr. Kavitha C
Dr. Małgorzata Kujawska
Dr. Wen-Cheng Lai
Guest Editors

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  • metabolomics data analysis
  • medical diagnosis
  • liver diseases
  • fatty liver
  • fibrosis
  • cirrhosis
  • cholesterol

Published Papers (1 paper)

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23 pages, 5386 KiB  
En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis
by Suganeshwari G, Jothi Prabha Appadurai, Balasubramanian Prabhu Kavin, Kavitha C and Wen-Cheng Lai
Biomedicines 2023, 11(5), 1309; - 28 Apr 2023
Cited by 6 | Viewed by 1668
Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, [...] Read more.
Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, the final product of a CT scan is a three-dimensional image constructed from a series of interlaced two-dimensional slices. Remember that not all slices deliver useful information for tumor detection. Recently, CT scan images of the liver and its tumors have been segmented using deep learning techniques. The primary goal of this study is to develop a deep learning-based system for automatically segmenting the liver and its tumors from CT scan pictures, and also reduce the amount of time and labor required by speeding up the process of diagnosing liver cancer. At its core, an Encoder–Decoder Network (En–DeNet) uses a deep neural network built on UNet to serve as an encoder, and a pre-trained EfficientNet to serve as a decoder. In order to improve liver segmentation, we developed specialized preprocessing techniques, such as the production of multichannel pictures, de-noising, contrast enhancement, ensemble, and the union of model predictions. Then, we proposed the Gradational modular network (GraMNet), which is a unique and estimated efficient deep learning technique. In GraMNet, smaller networks called SubNets are used to construct larger and more robust networks using a variety of alternative configurations. Only one new SubNet modules is updated for learning at each level. This helps in the optimization of the network and minimizes the amount of computational resources needed for training. The segmentation and classification performance of this study is compared to the Liver Tumor Segmentation Benchmark (LiTS) and 3D Image Rebuilding for Comparison of Algorithms Database (3DIRCADb01). By breaking down the components of deep learning, a state-of-the-art level of performance can be attained in the scenarios used in the evaluation. In comparison to more conventional deep learning architectures, the GraMNets generated here have a low computational difficulty. When associated with the benchmark study methods, the straight forward GraMNet is trained faster, consumes less memory, and processes images more rapidly. Full article
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