{"title":"人工智能在放射肿瘤学中的反应预测和个性化应用。","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":"{\"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}","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}
Artificial intelligence for response prediction and personalisation in radiation oncology.
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.
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.