基于图像的放疗后疗效预测中的机器学习:综述。

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Xiaohan Yuan, Chaoqiong Ma, Mingzhe Hu, Richard L J Qiu, Elahheh Salari, Reema Martini, Xiaofeng Yang
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引用次数: 0

摘要

机器学习(ML)与放疗的结合已成为结果预测领域的一项关键创新,在独特的挑战中带来了新的见解。本综述全面探讨了当前机器学习在各种治疗环境中的应用范围,重点关注患者生存、疾病复发和治疗诱发毒性等治疗结果。它强调了研究工作的上升轨迹以及生存分析作为临床优先事项的突出地位。我们分析了几种常见医学成像模式与临床数据的结合使用,强调了这种方法固有的优势和复杂性。这项研究反映出我们致力于推进以患者为中心的护理,倡导扩大对腹部和胰腺癌症的研究。虽然数据收集、患者隐私、标准化和可解释性方面存在重大挑战,但在放射治疗中利用 ML 有助于提高精准医疗水平和改善患者护理效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning in image-based outcome prediction after radiotherapy: A review.

The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope of ML applications in various treatment contexts, focusing on treatment outcomes such as patient survival, disease recurrence, and treatment-induced toxicity. It emphasizes the ascending trajectory of research efforts and the prominence of survival analysis as a clinical priority. We analyze the use of several common medical imaging modalities in conjunction with clinical data, highlighting the advantages and complexities inherent in this approach. The research reflects a commitment to advancing patient-centered care, advocating for expanded research on abdominal and pancreatic cancers. While data collection, patient privacy, standardization, and interpretability present significant challenges, leveraging ML in radiotherapy holds remarkable promise for elevating precision medicine and improving patient care outcomes.

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来源期刊
CiteScore
3.60
自引率
19.00%
发文量
331
审稿时长
3 months
期刊介绍: Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic
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