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催化精准医学:人工智能在前列腺癌诊断和管理方面的进步

Ali Talyshinskii¹, B M Zeeshan Hameed², Prajwal P Ravinder³

¹Department of Urology and Andrology, Astana Medical University, Astana 010000, Kazakhstan.

|May 25, 2024

概括

深度学习 (DL) 显示出前列腺癌 (PCa) 诊断的前景,特别是在MRI成像和PET/CT分析中. 然而,目前的DL模型需要进一步的研究,因为在广泛的临床采用之前的变化.

科学领域:

放射学放射学是一门学科.
在瘤学瘤学.
人工智能的人工智能

背景情况:

深度学习 (DL) 越来越多地应用于前列腺癌 (PCa) 诊断.
重点领域包括磁共振 (MR) 影像,正子发射断层扫描/计算机断层扫描 (PET/CT) 和前列腺活检.
在PCa中DL的挑战和临床影响正在调查中.

研究的目的:

在PCa诊断中分析DL应用的当前状态.
评估DL在MR前列腺重建,PCa检测/分层,PET/CT分析和活检中的作用.
确定DL在PCa管理中的挑战和临床影响.

主要方法:

在PubMed数据库中进行了系统的搜索.
应用了包含和排除标准,以确定PCa中DL方法的相关研究.
从最初搜索784篇文章中,共包含64项研究.

主要成果:

在前列腺重建 (21项研究),PCa检测/分层 (22项研究),PET/CT诊断 (7项研究) 和活检 (6项研究) 中分析了DL应用.
基于MR的DL研究主要使用3T数据集,而PET/CT研究通常依赖于单一机构的数据.
常见的辐射追踪剂包括Ga-PSMA-11,DCFPyl和PSMA-1007.

结论:

DL模型显示了PCa诊断的潜力,但尚未在临床上准备好.
方法,标签和评估标准的变化需要进一步的研究.
为了提高DL在PCa环境中的临床实用性和有效性,额外的研究至关重要.

关键词:

ADT ADT 在线播放这就是为什么MRI是MRI.检测PCa的检测在PCa重建的重建.聚乙烯/聚乙烯/聚乙烯人工智能的人工智能是人工智能.活组织活检深度学习是一种深度学习.前列腺癌是前列腺癌.前列腺重建前列腺的重建.

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