最新技术:放射组学和放射组学相关人工智能的临床转化之路。

BJR open Pub Date : 2023-12-12 eCollection Date: 2024-01-01 DOI:10.1093/bjro/tzad004
Shweta Majumder, Sharyn Katz, Despina Kontos, Leonid Roshkovan
{"title":"最新技术:放射组学和放射组学相关人工智能的临床转化之路。","authors":"Shweta Majumder, Sharyn Katz, Despina Kontos, Leonid Roshkovan","doi":"10.1093/bjro/tzad004","DOIUrl":null,"url":null,"abstract":"<p><p>Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"6 1","pages":"tzad004"},"PeriodicalIF":0.0000,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10860524/pdf/","citationCount":"0","resultStr":"{\"title\":\"State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.\",\"authors\":\"Shweta Majumder, Sharyn Katz, Despina Kontos, Leonid Roshkovan\",\"doi\":\"10.1093/bjro/tzad004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.</p>\",\"PeriodicalId\":72419,\"journal\":{\"name\":\"BJR open\",\"volume\":\"6 1\",\"pages\":\"tzad004\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10860524/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BJR open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/bjro/tzad004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BJR open","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bjro/tzad004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

放射组学和人工智能能够利用传统医学影像数据中蕴含的数千个隐蔽数字成像特征,有望提高肿瘤成像评估的精确度。这些技术虽然功能强大,但也存在一些变异性,目前阻碍了临床转化。为了克服这一障碍,有必要通过统一各机构的成像数据采集、构建可最大限度采集这些特征的标准化成像方案、统一后处理技术和大数据资源来控制这些变异性来源,从而为假设检验提供适当的研究动力。要做到这一点,多学科和多机构合作至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.

Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
18 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信