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Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad av...

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Autores principales: Hayashi, Hiromitsu, Uemura, Norio, Matsumura, Kazuki, Zhao, Liu, Sato, Hiroki, Shiraishi, Yuta, Yamashita, Yo-ichi, Baba, Hideo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613738/
https://www.ncbi.nlm.nih.gov/pubmed/34887644
http://dx.doi.org/10.3748/wjg.v27.i43.7480
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author Hayashi, Hiromitsu
Uemura, Norio
Matsumura, Kazuki
Zhao, Liu
Sato, Hiroki
Shiraishi, Yuta
Yamashita, Yo-ichi
Baba, Hideo
author_facet Hayashi, Hiromitsu
Uemura, Norio
Matsumura, Kazuki
Zhao, Liu
Sato, Hiroki
Shiraishi, Yuta
Yamashita, Yo-ichi
Baba, Hideo
author_sort Hayashi, Hiromitsu
collection PubMed
description Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the “big data” era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.
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spelling pubmed-86137382021-12-08 Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma Hayashi, Hiromitsu Uemura, Norio Matsumura, Kazuki Zhao, Liu Sato, Hiroki Shiraishi, Yuta Yamashita, Yo-ichi Baba, Hideo World J Gastroenterol Minireviews Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the “big data” era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine. Baishideng Publishing Group Inc 2021-11-21 2021-11-21 /pmc/articles/PMC8613738/ /pubmed/34887644 http://dx.doi.org/10.3748/wjg.v27.i43.7480 Text en ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
spellingShingle Minireviews
Hayashi, Hiromitsu
Uemura, Norio
Matsumura, Kazuki
Zhao, Liu
Sato, Hiroki
Shiraishi, Yuta
Yamashita, Yo-ichi
Baba, Hideo
Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma
title Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma
title_full Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma
title_fullStr Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma
title_full_unstemmed Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma
title_short Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma
title_sort recent advances in artificial intelligence for pancreatic ductal adenocarcinoma
topic Minireviews
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613738/
https://www.ncbi.nlm.nih.gov/pubmed/34887644
http://dx.doi.org/10.3748/wjg.v27.i43.7480
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