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 ORCID:

Hao PING

https://orcid.org/0000-0003-2912-0965

Jian LU

https://orcid.org/0000-0002-9144-7486

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Journal of Zhejiang University SCIENCE B 2023 Vol.24 No.8 P.663-681

http://doi.org/10.1631/jzus.B2200619


MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer


Author(s):  Xuehua ZHU, Lizhi SHAO, Zhenyu LIU, Zenan LIU, Jide HE, Jiangang LIU, Hao PING, Jian LU

Affiliation(s):  Department of Urology, Peking University Third Hospital,Beijing100191,China; more

Corresponding email(s):   lujian@bjmu.edu.cn, pinghaotrh@ccmu.edu.cn

Key Words:  Magnetic resonance imaging (MRI), Radiomics, Prostate cancer, Predictive model


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Xuehua ZHU, Lizhi SHAO, Zhenyu LIU, Zenan LIU, Jide HE, Jiangang LIU, Hao PING, Jian LU. MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer[J]. Journal of Zhejiang University Science B, 2023, 24(8): 663-681.

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year="2023",
publisher="Zhejiang University Press & Springer",
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A1 - Jian LU
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Abstract: 
prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.

MRI相关影像组学模型用于前列腺癌诊断、侵袭性和预后评估

朱学华1,邵立智2,刘振宇2,3,刘泽南1,何继德1,刘建刚4,5,平浩6,卢剑1
1北京大学第三医院泌尿外科,中国北京市,100191
2中国科学院自动化研究所分子影像重点实验室,中国北京市,100190
3中国科学院大学人工智能学院,中国北京市,100080
4北京航空航天大学医工交叉创新研究院大数据精准医疗高精尖创新中心,中国北京市,100191
5工业和信息化部大数据精准医疗重点实验室(北京航空航天大学),中国北京市,100191
6首都医科大学附属北京同仁医院泌尿外科,中国北京市,100730
摘要:前列腺癌(PCa)是一种具有高度异质性的恶性肿瘤,这给PCa的精准诊断和最佳个性化治疗带来了难题。具备解剖和功能序列的多参数磁共振成像(mp-MRI)已经发展成为PCa检测和分期的重要工具。此外,随着人工智能(AI)和图像数据处理技术的快速发展,利用影像组学的方法定量提取医学影像数据迎来广阔的应用前景。影像组学通过提取影像特征来获得影像标志物,并在此基础上建立预测模型进行精确评估。影像组学模型提供了一个辅助精准医疗的可靠且无创的替代方案,较基于临床病理指标的传统模型具有明显优势。本综述致力于对PCa影像组学相关研究进行归纳总结,重点探讨了基于MRI的影像组学模型的开发和验证。本综述对有关PCa诊断、侵袭性和预后评估方面的影像组学预测模型相关文献进行了回顾和总结,重点关注具有临床应用潜力的预测模型。此外,我们深入探讨了不同模型可以解决的关键问题,以及在具体临床背景下可能遇到的困难。因此,本综述有助于鼓励研究人员根据实际的临床需求构建预测模型,并帮助泌尿外科医生更好地了解影像组学相关的重要研究成果。

关键词:核磁共振;影像组学;前列腺癌;预测模型

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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