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

作　　者：Xuehua ZHU Lizhi SHAO Zhenyu LIU Zenan LIU Jide HE Jiangang LIU Hao PING Jian LU 

机构地区：[1]Department of Urology,Peking University Third Hospital,Beijing 100191,China [2]CAS Key Laboratory of Molecular Imaging,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China [3]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100080,China [4]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine,School of Engineering Medicine,Beihang University,Beijing 100191,China [5]Key Laboratory of Big Data-Based Precision Medicine(Beihang University),Ministry of Industry and Information Technology of the People’s Republic of China,Beijing 100191,China [6]Department of Urology,Beijing Tongren Hospital,Capital Medical University,Beijing 100730,China

出　　处：《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》2023年第8期663-681,共19页浙江大学学报（英文版）B辑（生物医学与生物技术）

基　　金：supported by the Beijing Natural Science Foundation(Nos.Z200027 and L212051);the Cohort Construction Project of Peking University Third Hospital(No.BYSYDL2021012),the Medicine-X Project of Peking University Health Science Center(No.BMU2022MX014);the National Natural Science Foundation of China(No.61871004).

摘　　要：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.

关 键 词：Magnetic resonance imaging(MRI) Radiomics Prostate cancer Predictive model 

分 类 号：R730.51[医药卫生—肿瘤]