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      CT texture analysis of abdominal lesions – Part II: Tumors of the Kidney and Pancreas

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          Abstract

          It has been proven in a few early studies that radiomic analysis offers a promising opportunity to detect or differentiate between organ lesions based on their unique texture parameters. Recently, the utilization of CT texture analysis (CTTA) has been receiving significant attention, especially for response evaluation and prognostication of different oncological diagnoses. In this review article, we discuss the unique ability of radiomics and its subfield CTTA to diagnose lesions in the pancreas and kidney. We review studies in which CTTA was used for the classification of histology grades in pancreas and kidney tumors. We also review the role of radiogenomics in the prediction of the molecular and genetic subtypes of pancreatic tumors. Furthermore, we provide a short report on recent advancements of radiomic analysis in predicting prognosis and survival of patients with pancreatic and renal cancers.

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          Author and article information

          Contributors
          Journal
          1647
          Imaging
          Imaging
          Akadémiai Kiadó (Budapest )
          2732-0960
          19 June 2021
          12 June 2021
          : 13
          : 1
          : 25-36
          Affiliations
          [1] Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine , Budapest, Hungary
          Author notes
          [* ]Corresponding author. Korányi Sándor str. 2, H-1083 Budapest, Hungary. Tel.: +36 (1) 459-1500 x.61628. E-mail: kaposi.pal@ 123456med.semmelweis-univ.hu
          [†]

          Both authors contributed equally to this paper

          Article
          10.1556/1647.2021.00020
          55422230-0672-4770-adf2-bd0b51783ed9
          © 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: 3, Tables: 4, Equations: 0, References: 45, Pages: 12
          Funding
          Funded by: Hungarian Academy of Sciences
          Award ID: Bolyai 386/2017
          Funded by: National Research, Development and Innovation Office
          Award ID: GINOP 2-2-18
          Custom metadata
          1

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

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