Cargando…

Artificial intelligence applications in computed tomography in gastric cancer: a narrative review

BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusi...

Descripción completa

Detalles Bibliográficos
Autores principales: Ma, Tingting, Wang, Hua, Ye, Zhaoxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583011/
https://www.ncbi.nlm.nih.gov/pubmed/37859746
http://dx.doi.org/10.21037/tcr-23-201
_version_ 1785122465475723264
author Ma, Tingting
Wang, Hua
Ye, Zhaoxiang
author_facet Ma, Tingting
Wang, Hua
Ye, Zhaoxiang
author_sort Ma, Tingting
collection PubMed
description BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed. METHODS: We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized. KEY CONTENT AND FINDINGS: AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving. CONCLUSIONS: AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists’ workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice.
format Online
Article
Text
id pubmed-10583011
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher AME Publishing Company
record_format MEDLINE/PubMed
spelling pubmed-105830112023-10-19 Artificial intelligence applications in computed tomography in gastric cancer: a narrative review Ma, Tingting Wang, Hua Ye, Zhaoxiang Transl Cancer Res Review Article BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed. METHODS: We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized. KEY CONTENT AND FINDINGS: AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving. CONCLUSIONS: AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists’ workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice. AME Publishing Company 2023-08-28 2023-09-30 /pmc/articles/PMC10583011/ /pubmed/37859746 http://dx.doi.org/10.21037/tcr-23-201 Text en 2023 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Review Article
Ma, Tingting
Wang, Hua
Ye, Zhaoxiang
Artificial intelligence applications in computed tomography in gastric cancer: a narrative review
title Artificial intelligence applications in computed tomography in gastric cancer: a narrative review
title_full Artificial intelligence applications in computed tomography in gastric cancer: a narrative review
title_fullStr Artificial intelligence applications in computed tomography in gastric cancer: a narrative review
title_full_unstemmed Artificial intelligence applications in computed tomography in gastric cancer: a narrative review
title_short Artificial intelligence applications in computed tomography in gastric cancer: a narrative review
title_sort artificial intelligence applications in computed tomography in gastric cancer: a narrative review
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583011/
https://www.ncbi.nlm.nih.gov/pubmed/37859746
http://dx.doi.org/10.21037/tcr-23-201
work_keys_str_mv AT matingting artificialintelligenceapplicationsincomputedtomographyingastriccanceranarrativereview
AT wanghua artificialintelligenceapplicationsincomputedtomographyingastriccanceranarrativereview
AT yezhaoxiang artificialintelligenceapplicationsincomputedtomographyingastriccanceranarrativereview