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Jian LU


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


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.

@article{title="MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer",
author="Xuehua ZHU, Lizhi SHAO, Zhenyu LIU, Zenan LIU, Jide HE, Jiangang LIU, Hao PING, Jian LU",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
%A Xuehua ZHU
%A Lizhi SHAO
%A Zhenyu LIU
%A Zenan LIU
%A Jide HE
%A Jiangang LIU
%A Jian LU
%J Journal of Zhejiang University SCIENCE B
%V 24
%N 8
%P 663-681
%@ 1673-1581
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2200619

T1 - MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
A1 - Xuehua ZHU
A1 - Lizhi SHAO
A1 - Zhenyu LIU
A1 - Zenan LIU
A1 - Jide HE
A1 - Jiangang LIU
A1 - Hao PING
A1 - Jian LU
J0 - Journal of Zhejiang University Science B
VL - 24
IS - 8
SP - 663
EP - 681
%@ 1673-1581
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2200619

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.




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


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