多发性骨髓瘤[18F]FDG PET/CT 的放射组学和人工智能前景。

IF 4.6 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Christos Sachpekidis, Hartmut Goldschmidt, Lars Edenbrandt, Antonia Dimitrakopoulou-Strauss
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引用次数: 0

摘要

[18F]FDG正电子发射计算机断层显像/计算机断层扫描(PET/CT)是多发性骨髓瘤(MM)中一种功能强大的成像模式,被认为是评估该疾病治疗反应的适当方法。另一方面,由于多发性骨髓瘤骨髓浸润的异质性和有时复杂的模式,PET/CT 的判读尤其具有挑战性,妨碍了观察者之间的可重复性,限制了该模式的诊断和预后能力。尽管已开发出许多方法来解决标准化问题,但还没有一种方法可被视为 PET/CT 解释或客观量化的标准方法。因此,需要先进的诊断量化方法来支持和指导 MM 的治疗。近年来,放射组学已成为一种创新方法,可高通量挖掘图像特征,用于临床决策,这可能对肿瘤学特别有帮助。此外,机器学习和深度学习这两个与放射组学过程密切相关的人工智能(AI)子领域已越来越多地应用于自动图像分析,为肿瘤学中 CT、PET/CT 和 MRI 等成像模式的标准化评估提供了新的可能性。因此,关于放射组学和基于人工智能的方法在 MM 的 [18F]FDG PET/CT 领域的应用的文献虽然刚刚起步,但在稳步增长,已经取得了令人鼓舞的成果,为该疾病的解释优化和标准化提供了潜在的可靠工具。本综述介绍了这些研究的主要成果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radiomics and Artificial Intelligence Landscape for [18F]FDG PET/CT in Multiple Myeloma.

[18F]FDG PET/CT is a powerful imaging modality of high performance in multiple myeloma (MM) and is considered the appropriate method for assessing treatment response in this disease. On the other hand, due to the heterogeneous and sometimes complex patterns of bone marrow infiltration in MM, the interpretation of PET/CT can be particularly challenging, hampering interobserver reproducibility and limiting the diagnostic and prognostic ability of the modality. Although many approaches have been developed to address the issue of standardization, none can yet be considered a standard method for interpretation or objective quantification of PET/CT. Therefore, advanced diagnostic quantification approaches are needed to support and potentially guide the management of MM. In recent years, radiomics has emerged as an innovative method for high-throughput mining of image-derived features for clinical decision making, which may be particularly helpful in oncology. In addition, machine learning and deep learning, both subfields of artificial intelligence (AI) closely related to the radiomics process, have been increasingly applied to automated image analysis, offering new possibilities for a standardized evaluation of imaging modalities such as CT, PET/CT and MRI in oncology. In line with this, the initial but steadily growing literature on the application of radiomics and AI-based methods in the field of [18F]FDG PET/CT in MM has already yielded encouraging results, offering a potentially reliable tool towards optimization and standardization of interpretation in this disease. The main results of these studies are presented in this review.

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来源期刊
Seminars in nuclear medicine
Seminars in nuclear medicine 医学-核医学
CiteScore
9.80
自引率
6.10%
发文量
86
审稿时长
14 days
期刊介绍: Seminars in Nuclear Medicine is the leading review journal in nuclear medicine. Each issue brings you expert reviews and commentary on a single topic as selected by the Editors. The journal contains extensive coverage of the field of nuclear medicine, including PET, SPECT, and other molecular imaging studies, and related imaging studies. Full-color illustrations are used throughout to highlight important findings. Seminars is included in PubMed/Medline, Thomson/ISI, and other major scientific indexes.
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