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Deep learning in hepatocellular carcinoma: Current status and future perspectives.
Ahn, Joseph C; Qureshi, Touseef Ahmad; Singal, Amit G; Li, Debiao; Yang, Ju-Dong.
Afiliação
  • Ahn JC; Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55904, United States.
  • Qureshi TA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
  • Singal AG; Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.
  • Li D; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
  • Yang JD; Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States. judong.yang@cshs.org.
World J Hepatol ; 13(12): 2039-2051, 2021 Dec 27.
Article em En | MEDLINE | ID: mdl-35070007
ABSTRACT
Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Screening_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Screening_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article