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A meta-analysis of Watson for Oncology in clinical application

Using the method of meta-analysis to systematically evaluate the consistency of treatment schemes between Watson for Oncology (WFO) and Multidisciplinary Team (MDT), and to provide references for the practical application of artificial intelligence clinical decision-support system in cancer treatmen...

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Autores principales: Jie, Zhou, Zhiying, Zeng, Li, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952578/
https://www.ncbi.nlm.nih.gov/pubmed/33707577
http://dx.doi.org/10.1038/s41598-021-84973-5
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author Jie, Zhou
Zhiying, Zeng
Li, Li
author_facet Jie, Zhou
Zhiying, Zeng
Li, Li
author_sort Jie, Zhou
collection PubMed
description Using the method of meta-analysis to systematically evaluate the consistency of treatment schemes between Watson for Oncology (WFO) and Multidisciplinary Team (MDT), and to provide references for the practical application of artificial intelligence clinical decision-support system in cancer treatment. We systematically searched articles about the clinical applications of Watson for Oncology in the databases and conducted meta-analysis using RevMan 5.3 software. A total of 9 studies were identified, including 2463 patients. When the MDT is consistent with WFO at the ‘Recommended’ or the ‘For consideration’ level, the overall concordance rate is 81.52%. Among them, breast cancer was the highest and gastric cancer was the lowest. The concordance rate in stage I–III cancer is higher than that in stage IV, but the result of lung cancer is opposite (P < 0.05).Similar results were obtained when MDT was only consistent with WFO at the "recommended" level. Moreover, the consistency of estrogen and progesterone receptor negative breast cancer patients, colorectal cancer patients under 70 years old or ECOG 0, and small cell lung cancer patients is higher than that of estrogen and progesterone positive breast cancer patients, colorectal cancer patients over 70 years old or ECOG 1–2, and non-small cell lung cancer patients, with statistical significance (P < 0.05). Treatment recommendations made by WFO and MDT were highly concordant for cancer cases examined, but this system still needs further improvement. Owing to relatively small sample size of the included studies, more well-designed, and large sample size studies are still needed.
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spelling pubmed-79525782021-03-12 A meta-analysis of Watson for Oncology in clinical application Jie, Zhou Zhiying, Zeng Li, Li Sci Rep Article Using the method of meta-analysis to systematically evaluate the consistency of treatment schemes between Watson for Oncology (WFO) and Multidisciplinary Team (MDT), and to provide references for the practical application of artificial intelligence clinical decision-support system in cancer treatment. We systematically searched articles about the clinical applications of Watson for Oncology in the databases and conducted meta-analysis using RevMan 5.3 software. A total of 9 studies were identified, including 2463 patients. When the MDT is consistent with WFO at the ‘Recommended’ or the ‘For consideration’ level, the overall concordance rate is 81.52%. Among them, breast cancer was the highest and gastric cancer was the lowest. The concordance rate in stage I–III cancer is higher than that in stage IV, but the result of lung cancer is opposite (P < 0.05).Similar results were obtained when MDT was only consistent with WFO at the "recommended" level. Moreover, the consistency of estrogen and progesterone receptor negative breast cancer patients, colorectal cancer patients under 70 years old or ECOG 0, and small cell lung cancer patients is higher than that of estrogen and progesterone positive breast cancer patients, colorectal cancer patients over 70 years old or ECOG 1–2, and non-small cell lung cancer patients, with statistical significance (P < 0.05). Treatment recommendations made by WFO and MDT were highly concordant for cancer cases examined, but this system still needs further improvement. Owing to relatively small sample size of the included studies, more well-designed, and large sample size studies are still needed. Nature Publishing Group UK 2021-03-11 /pmc/articles/PMC7952578/ /pubmed/33707577 http://dx.doi.org/10.1038/s41598-021-84973-5 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Jie, Zhou
Zhiying, Zeng
Li, Li
A meta-analysis of Watson for Oncology in clinical application
title A meta-analysis of Watson for Oncology in clinical application
title_full A meta-analysis of Watson for Oncology in clinical application
title_fullStr A meta-analysis of Watson for Oncology in clinical application
title_full_unstemmed A meta-analysis of Watson for Oncology in clinical application
title_short A meta-analysis of Watson for Oncology in clinical application
title_sort meta-analysis of watson for oncology in clinical application
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952578/
https://www.ncbi.nlm.nih.gov/pubmed/33707577
http://dx.doi.org/10.1038/s41598-021-84973-5
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