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      CT texture analysis of abdominal lesions – Part I.: Liver lesions

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          Abstract

          Artificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.

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          Most cited references43

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          Hepatocellular carcinoma

          Hepatocellular carcinoma appears frequently in patients with cirrhosis. Surveillance by biannual ultrasound is recommended for such patients because it allows diagnosis at an early stage, when effective therapies are feasible. The best candidates for resection are patients with a solitary tumour and preserved liver function. Liver transplantation benefits patients who are not good candidates for surgical resection, and the best candidates are those within Milan criteria (solitary tumour ≤5 cm or up to three nodules ≤3 cm). Image-guided ablation is the most frequently used therapeutic strategy, but its efficacy is limited by the size of the tumour and its localisation. Chemoembolisation has survival benefit in asymptomatic patients with multifocal disease without vascular invasion or extrahepatic spread. Finally, sorafenib, lenvatinib, which is non-inferior to sorafenib, and regorafenib increase survival and are the standard treatments in advanced hepatocellular carcinoma. This Seminar summarises the scientific evidence that supports the current recommendations for clinical practice, and discusses the areas in which more research is needed.
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            Radiomics: extracting more information from medical images using advanced feature analysis.

            Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              AASLD guidelines for the treatment of hepatocellular carcinoma.

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                Author and article information

                Contributors
                Journal
                1647
                Imaging
                Imaging
                Akadémiai Kiadó (Budapest )
                2732-0960
                19 June 2021
                26 May 2021
                : 13
                : 1
                : 13-24
                Affiliations
                [1] Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine , Budapest, Hungary
                Author notes
                [* ]Corresponding author. Korányi Sándor u. 2, H-1083 Budapest, Hungary. budai.bettina@ 123456med.semmelweis-univ.hu
                Author information
                https://orcid.org/0000-0002-3982-7887
                https://orcid.org/0000-0001-7096-8138
                https://orcid.org/0000-0002-1150-957X
                https://orcid.org/0000-0003-4386-2527
                https://orcid.org/0000-0002-7150-3495
                Article
                10.1556/1647.2021.00007
                e2d484c1-c44d-453c-8deb-7f1b0718bd80
                © 2021 The Author(s)

                Open Access. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated. (SID_1)

                History
                : 06 August 2020
                : 20 April 2021
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 44, Pages: 12
                Funding
                Funded by: Hungarian Academy of Sciences
                Award ID: Bolyai 386/2017
                Funded by: GINOP
                Award ID: 2-2-18
                Custom metadata
                1

                Medicine,Immunology,Health & Social care,Microbiology & Virology,Infectious disease & Microbiology
                machine learning,radiomics,liver,texture analysis,abdominal imaging

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