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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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          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
          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)

          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
          radiomics,abdominal imaging,machine learning,liver,texture analysis

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