Artificial Intelligence in Assessment of Hepatocellular Carcinoma Treatment Response

Authors

Bradley Spieler, LSU Health Sciences Center- New OrleansFollow
Carl Sabottke, University of Arizona College of Medicine – Tucson
Ahmed W. Moawad, University of Texas Health Science Center at Houston
Ahmed M. Gabr, Oregon Health & Science University
Mustafa R. Bashir, Duke University Medical Center
Richard Kinh Gian Do
Vahid Yaghmai, University of California, Irvine
Radu Rozenberg, Thunder Bay Regional Health Sciences Centre
Marielia Gerena, Loyola University Medical Center
Joseph Yacoub, Georgetown University
Khaled M. Elsayes, University of Texas MD Anderson Cancer Center

Document Type

Article

Publication Date

8-1-2021

Publication Title

Abdominal Radiology

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).

First Page

3660

Last Page

3671

PubMed ID

33786653

Volume

46

Issue

8

Publisher

Springer

Recommended Citation

Spieler, Bradley; Sabottke, Carl; Moawad, Ahmed W.; Gabr, Ahmed M.; Bashir, Mustafa R.; Do, Richard Kinh Gian; Yaghmai, Vahid; Rozenberg, Radu; Gerena, Marielia; Yacoub, Joseph; and Elsayes, Khaled M., "Artificial Intelligence in Assessment of Hepatocellular Carcinoma Treatment Response" (2021). School of Medicine Faculty Publications. 347.
https://digitalscholar.lsuhsc.edu/som_facpubs/347