Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.

IF 2 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
American journal of nuclear medicine and molecular imaging Pub Date : 2024-08-25 eCollection Date: 2024-01-01 DOI:10.62347/NLLV9295
Yongshun Liu, Wenpeng Huang, Yihan Yang, Weibo Cai, Zhaonan Sun
{"title":"Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.","authors":"Yongshun Liu, Wenpeng Huang, Yihan Yang, Weibo Cai, Zhaonan Sun","doi":"10.62347/NLLV9295","DOIUrl":null,"url":null,"abstract":"<p><p>Multiple myeloma (MM) is a malignant blood disease, but there have been significant improvements in the prognosis due to advancements in quantitative assessment and targeted therapy in recent years. The quantitative assessment of MM bone marrow infiltration and prognosis prediction is influenced by imaging and artificial intelligence (AI) quantitative parameters. At present, the primary imaging methods include computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). These methods are now crucial for diagnosing MM and evaluating myeloma cell infiltration, extramedullary disease, treatment effectiveness, and prognosis. Furthermore, the utilization of AI, specifically incorporating machine learning and radiomics, shows great potential in the field of diagnosing MM and distinguishing between MM and lytic metastases. This review discusses the advancements in imaging methods, including CT, MRI, and PET/CT, as well as AI for quantitatively assessing MM. We have summarized the key concepts, advantages, limitations, and diagnostic performance of each technology. Finally, we discussed the challenges related to clinical implementation and presented our views on advancing this field, with the aim of providing guidance for future research.</p>","PeriodicalId":7572,"journal":{"name":"American journal of nuclear medicine and molecular imaging","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11411189/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of nuclear medicine and molecular imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.62347/NLLV9295","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Multiple myeloma (MM) is a malignant blood disease, but there have been significant improvements in the prognosis due to advancements in quantitative assessment and targeted therapy in recent years. The quantitative assessment of MM bone marrow infiltration and prognosis prediction is influenced by imaging and artificial intelligence (AI) quantitative parameters. At present, the primary imaging methods include computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). These methods are now crucial for diagnosing MM and evaluating myeloma cell infiltration, extramedullary disease, treatment effectiveness, and prognosis. Furthermore, the utilization of AI, specifically incorporating machine learning and radiomics, shows great potential in the field of diagnosing MM and distinguishing between MM and lytic metastases. This review discusses the advancements in imaging methods, including CT, MRI, and PET/CT, as well as AI for quantitatively assessing MM. We have summarized the key concepts, advantages, limitations, and diagnostic performance of each technology. Finally, we discussed the challenges related to clinical implementation and presented our views on advancing this field, with the aim of providing guidance for future research.

用于多发性骨髓瘤定量评估的成像和人工智能(AI)的最新进展。
多发性骨髓瘤(MM)是一种恶性血液疾病,但近年来由于定量评估和靶向治疗的进步,其预后有了显著改善。多发性骨髓瘤骨髓浸润的定量评估和预后预测受到影像学和人工智能(AI)定量参数的影响。目前,主要的成像方法包括计算机断层扫描(CT)、磁共振成像(MRI)和正电子发射断层扫描(PET)。这些方法对于诊断骨髓瘤、评估骨髓瘤细胞浸润、髓外疾病、治疗效果和预后至关重要。此外,人工智能的应用,特别是机器学习和放射组学的结合,在诊断MM和区分MM与溶解性转移瘤方面显示出巨大的潜力。本综述讨论了成像方法(包括 CT、MRI 和 PET/CT)以及人工智能在定量评估 MM 方面的进展。我们总结了每种技术的关键概念、优势、局限性和诊断性能。最后,我们讨论了与临床实施相关的挑战,并提出了我们对推进这一领域发展的看法,旨在为未来研究提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
American journal of nuclear medicine and molecular imaging
American journal of nuclear medicine and molecular imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
自引率
4.00%
发文量
4
期刊介绍: The scope of AJNMMI encompasses all areas of molecular imaging, including but not limited to: positron emission tomography (PET), single-photon emission computed tomography (SPECT), molecular magnetic resonance imaging, magnetic resonance spectroscopy, optical bioluminescence, optical fluorescence, targeted ultrasound, photoacoustic imaging, etc. AJNMMI welcomes original and review articles on both clinical investigation and preclinical research. Occasionally, special topic issues, short communications, editorials, and invited perspectives will also be published. Manuscripts, including figures and tables, must be original and not under consideration by another journal.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信