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人工智能在前列腺 MRI 自动癌症检测中的应用:机遇与挑战,选自 AI 应用专题系列。

Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the Special Series on AI Applications.

机构信息

Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, Rm B3B85, Bethesda, MD 20892.

Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.

出版信息

AJR Am J Roentgenol. 2022 Aug;219(2):188-194. doi: 10.2214/AJR.21.26917. Epub 2021 Dec 8.

DOI:10.2214/AJR.21.26917
PMID:34877870
Abstract

Use of prostate MRI has increased greatly in the past decade, primarily in directing targeted prostate biopsy. However, prostate MRI interpretation remains prone to interreader variation. Artificial intelligence (AI) has the potential to standardize detection of lesions on MRI that are suspicious for prostate cancer (PCa). The purpose of this review is to explore the current status of AI for the automated detection of PCa on MRI. Recent literature describing promising results regarding AI models for PCa detection on MRI is highlighted. Numerous limitations of the existing literature are also described, including biases in model validation, heterogeneity in reporting of performance metrics, and lack of sufficient evidence of clinical translation. Challenges related to AI ethics and data governance are also discussed. An outlook is provided for AI in lesion detection on prostate MRI in the coming years, emphasizing current research needs. Future investigations, incorporating large-scale diverse multiinstitutional training and testing datasets, are anticipated to enable the development of more robust AI models for PCa detection on MRI, though prospective clinical trials will ultimately be required to establish benefit of AI in patient management.

摘要

在过去的十年中,前列腺 MRI 的使用大大增加,主要用于指导靶向前列腺活检。然而,前列腺 MRI 的解读仍然容易受到读者之间的差异影响。人工智能(AI)有可能标准化检测 MRI 上可疑前列腺癌(PCa)的病变。本综述的目的是探讨 AI 在 MRI 上自动检测 PCa 的现状。重点介绍了最近描述 AI 模型在 MRI 上检测 PCa 有前景结果的文献。还描述了现有文献的许多局限性,包括模型验证中的偏差、性能指标报告的异质性以及缺乏充分的临床转化证据。还讨论了与 AI 伦理和数据治理相关的挑战。提供了对未来几年前列腺 MRI 上病变检测中 AI 的展望,强调了当前的研究需求。未来的研究预计将纳入大规模、多样化的多机构培训和测试数据集,从而开发出更强大的 AI 模型来检测 MRI 上的 PCa,尽管最终需要前瞻性临床试验来确定 AI 在患者管理中的益处。

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