人工智能在肿瘤分子PET成像中的应用:Beyond[18F]F-FDG示踪剂的叙述性综述——第一部分。PSMA、胆碱和DOTA放射性示踪剂。

IF 4.6 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Seminars in nuclear medicine Pub Date : 2023-09-24 DOI:10.1053/j.semnuclmed.2023.08.004
Seyed Ali Mirshahvalad MD, MPH, FEBNM , Roya Eisazadeh MD, FEBNM , Malihe Shahbazi-Akbari MD , Christian Pirich MD , Mohsen Beheshti MD, FEBNM, FASNC
{"title":"人工智能在肿瘤分子PET成像中的应用:Beyond[18F]F-FDG示踪剂的叙述性综述——第一部分。PSMA、胆碱和DOTA放射性示踪剂。","authors":"Seyed Ali Mirshahvalad MD, MPH, FEBNM ,&nbsp;Roya Eisazadeh MD, FEBNM ,&nbsp;Malihe Shahbazi-Akbari MD ,&nbsp;Christian Pirich MD ,&nbsp;Mohsen Beheshti MD, FEBNM, FASNC","doi":"10.1053/j.semnuclmed.2023.08.004","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [<sup>18</sup>F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [<sup>18</sup>F]F-FDG. The novel non-[<sup>18</sup>F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions’ location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[<sup>18</sup>F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [<sup>18</sup>F]F-FDG.</p></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"54 1","pages":"Pages 171-180"},"PeriodicalIF":4.6000,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0001299823000739/pdfft?md5=1f3adebbdc26f1d040886c49b4af09fa&pid=1-s2.0-S0001299823000739-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers\",\"authors\":\"Seyed Ali Mirshahvalad MD, MPH, FEBNM ,&nbsp;Roya Eisazadeh MD, FEBNM ,&nbsp;Malihe Shahbazi-Akbari MD ,&nbsp;Christian Pirich MD ,&nbsp;Mohsen Beheshti MD, FEBNM, FASNC\",\"doi\":\"10.1053/j.semnuclmed.2023.08.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [<sup>18</sup>F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [<sup>18</sup>F]F-FDG. The novel non-[<sup>18</sup>F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions’ location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[<sup>18</sup>F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [<sup>18</sup>F]F-FDG.</p></div>\",\"PeriodicalId\":21643,\"journal\":{\"name\":\"Seminars in nuclear medicine\",\"volume\":\"54 1\",\"pages\":\"Pages 171-180\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0001299823000739/pdfft?md5=1f3adebbdc26f1d040886c49b4af09fa&pid=1-s2.0-S0001299823000739-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seminars in nuclear medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0001299823000739\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seminars in nuclear medicine","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0001299823000739","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

人工智能(AI)在过去几十年中有了显著的发展。近年来,这种蓬勃发展的趋势也出现在医学中,尤其是在成像领域。机器学习(ML)、深度学习(DL)及其方法(如SVM、CNN),以及放射组学,是已引入该领域的术语,在某种程度上,已为专业临床医生所熟悉。PET是通过这些最先进的算法增强的模式之一。这种强大的成像技术进一步与解剖模态相结合,如计算机断层扫描(CT)和磁共振成像(MRI),以提供可靠的混合模态PET/CT和PET/MRI。在不同的组件(PET、CT和MRI)上应用基于人工智能的算法已经产生了有希望的结果,最大限度地提高了PET成像的价值。然而,[18F]F-FDG,分子成像中最常用的示踪剂,主要受到关注。因此,我们旨在研究本综述中较少讨论的示踪剂,超越[18F]F-FDG。新型非[18F]F-FDG药物在各种临床任务中也被证明是有价值的,包括病变检测和肿瘤表征、准确描绘和预后影响。关于前列腺患者,基于PSMA的模型在确定肿瘤病变的位置和描绘它们方面非常准确,尤其是在前列腺内。然而,他们也可以评估全身图像,自动检测患者的前列腺外病变。考虑到使用AI的前列腺特异性膜抗原(PSMA)PET的预后价值,它可以预测治疗反应和患者生存率,这对患者管理至关重要。胆碱成像,另一种非[18F]F-FDG示踪剂,同样显示出可接受的结果,可能对临床有益,尽管目前的证据明显比PSMA更为有限。最后,发现DOTA配体的不同亚型是有价值的。它们可以诊断具有挑战性的部位的肿瘤病变,甚至预测组织病理学分级,是一种非常有利的非侵入性工具。总之,目前有限的研究显示出了有希望的结果,这为人工智能在[18F]F-FDG之外的分子成像带来了光明的未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers

Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [18F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [18F]F-FDG. The novel non-[18F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions’ location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[18F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [18F]F-FDG.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
Artificial Intelligence and Workforce Diversity in Nuclear Medicine. Diagnosing Bone Metastases in Breast Cancer: A Systematic Review and Network Meta-Analysis on Diagnostic Test Accuracy Studies of 2-[18F]FDG-PET/CT, 18F-NaF-PET/CT, MRI, Contrast-Enhanced CT, and Bone Scintigraphy. Long Axial Field-of-View PET/CT: New Opportunities for Pediatric Imaging. Total Body PET/CT: Future Aspects. Long Axial Field of View PET/CT: Technical Aspects in Cardiovascular Diseases.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1