Artificial intelligence in assessment of hepatocellular carcinoma treatment response

Bradley Spieler; Carl Sabottke; Ahmed W. Moawad; Ahmed M. Gabr; Mustafa R. Bashir; Richard Kinh Gian Do; Vahid Yaghmai; Radu Rozenberg; Marielia Gerena; Joseph Yacoub; Khaled M. Elsayes

doi:10.1007/s00261-021-03056-1

Artificial intelligence in assessment of hepatocellular carcinoma treatment response

Bradley Spieler, Carl Sabottke, Ahmed W. Moawad, Ahmed M. Gabr, Mustafa R. Bashir, Richard Kinh Gian Do, Vahid Yaghmai, Radu Rozenberg, Marielia Gerena, Joseph Yacoub, Khaled M. Elsayes

Abdominal Imaging

Research output: Contribution to journal › Review article › peer-review

14 Scopus citations

Abstract

Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).

Original language	English (US)
Pages (from-to)	3660-3671
Number of pages	12
Journal	Abdominal Radiology
Volume	46
Issue number	8
DOIs	https://doi.org/10.1007/s00261-021-03056-1
State	Published - Aug 2021

Keywords

Artificial intelligence
Hepatocellular carcinoma
Liver imaging reporting and data systems treatment response algorithm
Locoregional therapy

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Gastroenterology
Urology

Access to Document

10.1007/s00261-021-03056-1

Cite this

@article{cade988d3d4b4c0281f5c19a40127c18,

title = "Artificial intelligence in assessment of hepatocellular carcinoma treatment response",

abstract = "Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).",

keywords = "Artificial intelligence, Hepatocellular carcinoma, Liver imaging reporting and data systems treatment response algorithm, Locoregional therapy",

author = "Bradley Spieler and Carl Sabottke and Moawad, {Ahmed W.} and Gabr, {Ahmed M.} and Bashir, {Mustafa R.} and Do, {Richard Kinh Gian} and Vahid Yaghmai and Radu Rozenberg and Marielia Gerena and Joseph Yacoub and Elsayes, {Khaled M.}",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.",

year = "2021",

month = aug,

doi = "10.1007/s00261-021-03056-1",

language = "English (US)",

volume = "46",

pages = "3660--3671",

journal = "Abdominal Radiology",

issn = "2366-004X",

publisher = "Springer New York",

number = "8",

}

TY - JOUR

T1 - Artificial intelligence in assessment of hepatocellular carcinoma treatment response

AU - Spieler, Bradley

AU - Sabottke, Carl

AU - Moawad, Ahmed W.

AU - Gabr, Ahmed M.

AU - Bashir, Mustafa R.

AU - Do, Richard Kinh Gian

AU - Yaghmai, Vahid

AU - Rozenberg, Radu

AU - Gerena, Marielia

AU - Yacoub, Joseph

AU - Elsayes, Khaled M.

PY - 2021/8

Y1 - 2021/8

N2 - Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).

AB - Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).

KW - Artificial intelligence

KW - Hepatocellular carcinoma

KW - Liver imaging reporting and data systems treatment response algorithm

KW - Locoregional therapy

UR - http://www.scopus.com/inward/record.url?scp=85103984970&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85103984970&partnerID=8YFLogxK

U2 - 10.1007/s00261-021-03056-1

DO - 10.1007/s00261-021-03056-1

M3 - Review article

C2 - 33786653

AN - SCOPUS:85103984970

SN - 2366-004X

VL - 46

SP - 3660

EP - 3671

JO - Abdominal Radiology

JF - Abdominal Radiology

IS - 8

ER -

Artificial intelligence in assessment of hepatocellular carcinoma treatment response

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this