利用MRI评估癌症预后:深度学习方法综述。

BJR open Pub Date : 2022-01-01 DOI:10.1259/bjro.20210072
Yousef Mazaheri, Sunitha B Thakur, Almir Gv Bitencourt, Roberto Lo Gullo, Andreas M Hötker, David D B Bates, Oguz Akin
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引用次数: 1

摘要

准确评估肿瘤对治疗的反应对于根据预测的反应制定个性化治疗方案以及通过提供准确的反应指标来支持研究新治疗药物的临床试验至关重要。医学成像、计算机硬件和机器学习算法的最新进展导致这些工具在整个医学领域的使用增加,特别是在癌症成像中用于检测和表征恶性病变、预后和评估治疗反应。在目前可用的成像技术中,磁共振成像(MRI)以其优越的软组织对比和多平面成像和功能评估能力,在许多癌症的治疗评估中发挥着重要作用。近年来,深度学习(DL)已成为一个活跃的研究领域,为计算机辅助临床和放射决策支持铺平了道路。DL可以揭示肉眼无法识别的影像特征与相关临床结果之间的关联。这篇综述的目的是强调DL在MRI评估肿瘤反应评估中的应用。在这篇综述中,我们将首先概述医学成像研究中常用的深度学习架构。然后,我们将回顾迄今为止将DL应用于磁共振成像以评估治疗反应的研究。最后,我们将讨论在临床工作流程中使用DL的挑战和机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods.

Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods.

Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods.

Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods.

Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow.

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