Artificial intelligence for response prediction and personalisation in radiation oncology.

IF 2.7 3区 医学 Q3 ONCOLOGY
Alex Zwanenburg, Gareth Price, Steffen Löck
{"title":"Artificial intelligence for response prediction and personalisation in radiation oncology.","authors":"Alex Zwanenburg, Gareth Price, Steffen Löck","doi":"10.1007/s00066-024-02281-z","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely personalised radiotherapy based on pretreatment data, response-driven radiotherapy and dynamically optimised radiotherapy. Finally, we discuss the main challenges in clinical translation of AI systems for radiotherapy personalisation.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Strahlentherapie und Onkologie","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00066-024-02281-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely personalised radiotherapy based on pretreatment data, response-driven radiotherapy and dynamically optimised radiotherapy. Finally, we discuss the main challenges in clinical translation of AI systems for radiotherapy personalisation.

Abstract Image

人工智能在放射肿瘤学中的反应预测和个性化应用。
人工智能(AI)系统可通过评估复杂、多方面的患者数据,预测肿瘤和正常组织对放疗的反应,从而实现个性化放疗。在此,我们将介绍三代不同的人工智能系统,即基于预处理数据的个性化放疗、反应驱动放疗和动态优化放疗。最后,我们将讨论放疗个性化人工智能系统在临床应用中面临的主要挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
5.70
自引率
12.90%
发文量
141
审稿时长
3-8 weeks
期刊介绍: Strahlentherapie und Onkologie, published monthly, is a scientific journal that covers all aspects of oncology with focus on radiooncology, radiation biology and radiation physics. The articles are not only of interest to radiooncologists but to all physicians interested in oncology, to radiation biologists and radiation physicists. The journal publishes original articles, review articles and case studies that are peer-reviewed. It includes scientific short communications as well as a literature review with annotated articles that inform the reader on new developments in the various disciplines concerned and hence allow for a sound overview on the latest results in radiooncology research. Founded in 1912, Strahlentherapie und Onkologie is the oldest oncological journal in the world. Today, contributions are published in English and German. All articles have English summaries and legends. The journal is the official publication of several scientific radiooncological societies and publishes the relevant communications of these societies.
×
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