Current status and future perspectives of radiomics in hepatocellular carcinoma Review

   


Authors: Miranda, J.; Horvat, N.; Fonseca, G. M.; de Arimateia Batista Araujo-Filho, J.; Fernandes, M. C.; Charbel, C.; Chakraborty, J.; Coelho, F. F.; Nomura, C. H.; Herman, P.
Review Title: Current status and future perspectives of radiomics in hepatocellular carcinoma
Abstract: Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC. © The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Keywords: cancer survival; protein expression; treatment response; retrospective studies; gene mutation; pathogenesis; review; cancer recurrence; hepatocellular carcinoma; sorafenib; chemoembolization; liver cell carcinoma; carcinoma, hepatocellular; liver neoplasms; nuclear magnetic resonance imaging; clinical practice; ki 67 antigen; computer assisted tomography; gene expression; validation study; diagnostic imaging; retrospective study; information processing; phosphatidylinositol 3 kinase; protein p53; prediction; histology; liver failure; radiology; artificial intelligence; liver tumor; hepatectomy; radiofrequency ablation; perception; texture analysis; machine learning; workflow; humans; human; deep learning; radiomics
Journal Title: World Journal of Gastroenterology
Volume: 29
Issue: 1
ISSN: 1007-9327
Publisher: Baishideng Publishing Group Inc  
Date Published: 2023-01-07
Start Page: 43
End Page: 60
Language: English
DOI: 10.3748/wjg.v29.i1.43
PUBMED: 36683711
PROVIDER: scopus
PMCID: PMC9850949
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146631089&doi=10.3748%2fwjg.v29.i1.43&partnerID=40&md5=6e5e9f7026f4bc48375e768c2b3abb75
Notes: Review -- Export Date: 1 March 2023 -- Source: Scopus
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MSK Authors
  1. Jayasree Chakraborty
    69 Chakraborty
  2. Natally Horvat
    101 Horvat
  3. Maria Clara Fernandes De Paula Tito Jorge
    20 Fernandes De Paula Tito Jorge
  4. Charlotte Charbel
    5 Charbel
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