{"title":"Cardiac substructure dosimetry in thoracic radiotherapy: moving beyond mean heart dose and toward precision cardiac risk mitigation.","authors":"Ahmed Sohaib, Mohamed T Amin, Mohamed Alhefny","doi":"10.1007/s00066-026-02561-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Radiation-induced heart disease (RIHD) remains a clinically significant consequence of thoracic radiotherapy (RT). Historically, the mean heart dose (MHD) has served as the primary metric for cardiac risk estimation. However, accumulating evidence demonstrates that MHD inadequately captures the heterogeneous dose distribution across functionally distinct cardiac substructures and poorly predicts specific cardiovascular endpoints.</p><p><strong>Objective: </strong>This narrative review synthesizes recent evidence (2013-2025) on the transition from whole-heart dosimetry to substructure-level dose-volume analysis in thoracic RT, encompassing dose-response relationships for specific cardiac substructures, advances in artificial intelligence (AI)-driven auto-segmentation, modern cardiac-sparing delivery techniques, biomarker-guided surveillance, pharmacologic cardioprotection, and normal tissue complication probability (NTCP) modeling.</p><p><strong>Methods: </strong>A structured literature search was conducted across PubMed, Scopus, and Web of Science databases (January 2013-March 2025) using predefined search terms. Studies reporting cardiac substructure dose-response relationships, auto-segmentation performance, cardiac-sparing techniques, biomarkers, pharmacologic interventions, and NTCP models were included.</p><p><strong>Results: </strong>Substructure-specific dose metrics-particularly dose to the left anterior descending artery (LAD), heart base, left ventricle (LV), and cardiac conduction system-predict coronary events, arrhythmias, and overall survival with substantially greater accuracy than MHD. Deep learning auto-segmentation now achieves ≥ 94% clinical acceptability for cardiac chambers, enabling routine substructure-based planning. Deep inspiration breath hold (DIBH) reduces heart and LAD doses by 36-42%, and proton beam therapy (PBT) offers superior cardiac sparing across all tumor sites, though randomized cardiac outcome data remain pending. Statins demonstrate a compelling retrospective cardioprotective signal (up to 66% reduction in major adverse cardiac events), but prospective validation is lacking. The 2022 European Society of Cardiology (ESC) cardio-oncology guidelines and the 2024 American Radium Society (ARS) appropriate use criteria provide the current clinical framework.</p><p><strong>Conclusion: </strong>The paradigm shift from MHD to substructure-level cardiac dosimetry represents a transformative advance in thoracic RT. Integration of AI-driven segmentation, advanced delivery techniques, biomarker surveillance, and pharmacologic cardioprotection into routine clinical workflows is essential for precision cardiac risk mitigation in irradiated patients.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2026-06-24","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/s00066-026-02561-w","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
Background: Radiation-induced heart disease (RIHD) remains a clinically significant consequence of thoracic radiotherapy (RT). Historically, the mean heart dose (MHD) has served as the primary metric for cardiac risk estimation. However, accumulating evidence demonstrates that MHD inadequately captures the heterogeneous dose distribution across functionally distinct cardiac substructures and poorly predicts specific cardiovascular endpoints.
Objective: This narrative review synthesizes recent evidence (2013-2025) on the transition from whole-heart dosimetry to substructure-level dose-volume analysis in thoracic RT, encompassing dose-response relationships for specific cardiac substructures, advances in artificial intelligence (AI)-driven auto-segmentation, modern cardiac-sparing delivery techniques, biomarker-guided surveillance, pharmacologic cardioprotection, and normal tissue complication probability (NTCP) modeling.
Methods: A structured literature search was conducted across PubMed, Scopus, and Web of Science databases (January 2013-March 2025) using predefined search terms. Studies reporting cardiac substructure dose-response relationships, auto-segmentation performance, cardiac-sparing techniques, biomarkers, pharmacologic interventions, and NTCP models were included.
Results: Substructure-specific dose metrics-particularly dose to the left anterior descending artery (LAD), heart base, left ventricle (LV), and cardiac conduction system-predict coronary events, arrhythmias, and overall survival with substantially greater accuracy than MHD. Deep learning auto-segmentation now achieves ≥ 94% clinical acceptability for cardiac chambers, enabling routine substructure-based planning. Deep inspiration breath hold (DIBH) reduces heart and LAD doses by 36-42%, and proton beam therapy (PBT) offers superior cardiac sparing across all tumor sites, though randomized cardiac outcome data remain pending. Statins demonstrate a compelling retrospective cardioprotective signal (up to 66% reduction in major adverse cardiac events), but prospective validation is lacking. The 2022 European Society of Cardiology (ESC) cardio-oncology guidelines and the 2024 American Radium Society (ARS) appropriate use criteria provide the current clinical framework.
Conclusion: The paradigm shift from MHD to substructure-level cardiac dosimetry represents a transformative advance in thoracic RT. Integration of AI-driven segmentation, advanced delivery techniques, biomarker surveillance, and pharmacologic cardioprotection into routine clinical workflows is essential for precision cardiac risk mitigation in irradiated patients.
背景:放射诱发的心脏病(RIHD)仍然是胸部放射治疗(RT)的临床重要后果。历史上,平均心脏剂量(MHD)一直是心脏风险评估的主要指标。然而,越来越多的证据表明,MHD不能充分捕捉功能不同的心脏亚结构之间的不均匀剂量分布,也不能很好地预测特定的心血管终点。目的:这篇叙述性综述综合了最近的证据(2013-2025),从全心脏剂量学到亚结构水平的剂量-体积分析,包括特定心脏亚结构的剂量-反应关系,人工智能(AI)驱动的自动分割,现代保心给药技术,生物标志物引导的监测,药理学心脏保护和正常组织并发症概率(NTCP)建模。方法:使用预定义的检索词对PubMed、Scopus和Web of Science数据库(2013年1月- 2025年3月)进行结构化文献检索。研究报告了心脏亚结构剂量反应关系、自动分割性能、心脏保留技术、生物标志物、药物干预和NTCP模型。结果:亚结构特异性剂量指标——特别是左前降支(LAD)、心基、左心室(LV)和心脏传导系统的剂量——预测冠状动脉事件、心律失常和总生存期的准确性比MHD高得多。深度学习自动分割现在达到≥ 94%的心脏腔临床可接受度,实现常规的基于子结构的规划。深度吸气屏气(DIBH)可将心脏和LAD剂量降低36-42%,质子束治疗(PBT)在所有肿瘤部位都提供了优越的心脏保护,尽管随机心脏结局数据尚未确定。他汀类药物显示出令人信服的回顾性心脏保护信号(主要不良心脏事件减少高达66%),但缺乏前瞻性验证。2022年欧洲心脏病学会(ESC)心脏肿瘤学指南和2024年美国镭学会(ARS)适当使用标准提供了当前的临床框架。结论:从MHD到亚结构水平心脏剂量学的范式转变代表了胸部rt的革命性进步。将人工智能驱动的分割、先进的递送技术、生物标志物监测和药物心脏保护整合到常规临床工作流程中,对于精确降低放疗患者的心脏风险至关重要。
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.