Literature DB >> 33786653

Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

Bradley Spieler1, Carl Sabottke2, Ahmed W Moawad3, Ahmed M Gabr4, Mustafa R Bashir5, Richard Kinh Gian Do6, Vahid Yaghmai7, Radu Rozenberg8, Marielia Gerena9, Joseph Yacoub10, Khaled M Elsayes11.   

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).

Entities:  

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

Year:  2021        PMID： 33786653     DOI： 10.1007/s00261-021-03056-1

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  61 in total

1.  A primer for understanding radiology articles about machine learning and deep learning.

Authors:  Takeshi Nakaura; Toru Higaki; Kazuo Awai; Osamu Ikeda; Yasuyuki Yamashita
Journal:  Diagn Interv Imaging       Date:  2020-10-26       Impact factor: 4.026

Review 2.  The future of radiology augmented with Artificial Intelligence: A strategy for success.

Authors:  Charlene Liew
Journal:  Eur J Radiol       Date:  2018-03-14       Impact factor: 3.528

Review 3.  Artificial intelligence in diagnostic imaging: impact on the radiography profession.

Authors:  Maryann Hardy; Hugh Harvey
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

Review 4.  Artificial Intelligence in Medicine: Where Are We Now?

Authors:  Sagar Kulkarni; Nuran Seneviratne; Mirza Shaheer Baig; Ameer Hamid Ahmed Khan
Journal:  Acad Radiol       Date:  2019-10-19       Impact factor: 3.173

5.  Reproducibility of LI-RADS treatment response algorithm for hepatocellular carcinoma after locoregional therapy.

Authors:  A A K Abdel Razek; L G El-Serougy; G A Saleh; W Shabana; R Abd El-Wahab
Journal:  Diagn Interv Imaging       Date:  2020-04-03       Impact factor: 4.026

6.  Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest.

Authors:  H Akai; K Yasaka; A Kunimatsu; M Nojima; T Kokudo; N Kokudo; K Hasegawa; O Abe; K Ohtomo; S Kiryu
Journal:  Diagn Interv Imaging       Date:  2018-06-14       Impact factor: 4.026

7.  Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.

Authors:  Dan Liu; Fei Liu; Xiaoyan Xie; Liya Su; Ming Liu; Xiaohua Xie; Ming Kuang; Guangliang Huang; Yuqi Wang; Hui Zhou; Kun Wang; Manxia Lin; Jie Tian
Journal:  Eur Radiol       Date:  2020-01-03       Impact factor: 5.315

8.  LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy.

Authors:  Erin L Shropshire; Mohammad Chaudhry; Chad M Miller; Brian C Allen; Erol Bozdogan; Diana M Cardona; Lindsay Y King; Gemini L Janas; Richard K Do; Charles Y Kim; James Ronald; Mustafa R Bashir
Journal:  Radiology       Date:  2019-04-30       Impact factor: 29.146

Review 9.  Artificial intelligence in medical imaging of the liver.

Authors:  Li-Qiang Zhou; Jia-Yu Wang; Song-Yuan Yu; Ge-Ge Wu; Qi Wei; You-Bin Deng; Xing-Long Wu; Xin-Wu Cui; Christoph F Dietrich
Journal:  World J Gastroenterol       Date:  2019-02-14       Impact factor: 5.742

Review 10.  Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning.

Authors:  Synho Do; Kyoung Doo Song; Joo Won Chung
Journal:  Korean J Radiol       Date:  2020-01       Impact factor: 3.500
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  2 in total

1.  Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection.

Authors:  I-Cheng Lee; Jo-Yu Huang; Ting-Chun Chen; Chia-Heng Yen; Nai-Chi Chiu; Hsuen-En Hwang; Jia-Guan Huang; Chien-An Liu; Gar-Yang Chau; Rheun-Chuan Lee; Yi-Ping Hung; Yee Chao; Shinn-Ying Ho; Yi-Hsiang Huang
Journal:  Liver Cancer       Date:  2021-09-20       Impact factor: 11.740

2.  Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research.

Authors:  Yuan Wang; Chao Wei; Xiangui Deng; Shudi Gao; Jing Chen
Journal:  Biomed Res Int       Date:  2022-09-19       Impact factor: 3.246
  2 in total

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