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前列腺癌中放射组学与放射科医生的比较。一项系统评价的结果

Radiomics vs radiologist in prostate cancer. Results from a systematic review.

作者信息

Chiacchio Giuseppe, Castellani Daniele, Nedbal Carlotta, De Stefano Virgilio, Brocca Carlo, Tramanzoli Pietro, Galosi Andrea Benedetto, Donalisio da Silva Rodrigo, Teoh Jeremy Yuen-Chun, Tiong Ho Yee, Naik Nithesh, Somani Bhaskar K, Merseburger Axel S, Gauhar Vineet

机构信息

Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy.

Division of Urology, Denver Health Medical Center, University of Colorado, Denver, USA.

出版信息

World J Urol. 2023 Mar;41(3):709-724. doi: 10.1007/s00345-023-04305-2. Epub 2023 Mar 3.

Abstract

PURPOSE

Radiomics in uro-oncology is a rapidly evolving science proving to be a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. This scoping review aimed to identify key aspects wherein radiomics can potentially improve the accuracy of diagnosis, staging, and extraprostatic extension in prostate cancer (PCa).

METHODS

The literature search was performed on June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only.

RESULTS

Seventeen papers were included. The combination of PIRADS and radiomics score models improves the PIRADS score reporting of 2 and 3 lesions even in the peripheral zone. Multiparametric MRI-based radiomics models suggest that by simply omitting diffusion contrast enhancement imaging in radiomics models can simplify the process of analysis of clinically significant PCa by PIRADS. Radiomics features correlated with the Gleason grade with excellent discriminative ability. Radiomics has higher accuracy in predicting not only the presence but also the side of extraprostatic extension.

CONCLUSIONS

Radiomics research on PCa mainly uses MRI as an imaging modality and is focused on diagnosis and risk stratification and has the best future possibility of improving PIRADS reporting. Radiomics has established its superiority over radiologist-reported outcomes but the variability has to be taken into consideration before translating it to clinical practice.

摘要

目的

泌尿肿瘤学中的放射组学是一门快速发展的科学,已被证明是一种优化医学图像海量数据分析的新方法,可为临床问题提供辅助指导。本综述旨在确定放射组学在哪些关键方面可能提高前列腺癌(PCa)诊断、分期和前列腺外扩展的准确性。

方法

2022年6月使用PubMed、Embase和Cochrane对照试验中央注册库进行文献检索。纳入仅将放射组学与放射学报告进行比较的研究。

结果

纳入17篇论文。PIRADS与放射组学评分模型相结合,即使在外周区也能提高2级和3级病变的PIRADS评分报告。基于多参数MRI的放射组学模型表明,在放射组学模型中简单省略弥散对比增强成像可简化PIRADS对临床显著PCa的分析过程。放射组学特征与Gleason分级相关,具有出色的鉴别能力。放射组学在预测前列腺外扩展的存在及部位方面具有更高的准确性。

结论

PCa的放射组学研究主要使用MRI作为成像方式,侧重于诊断和风险分层,在改善PIRADS报告方面具有最佳的未来可能性。放射组学已显示出优于放射科医生报告结果的优势,但在转化为临床实践之前必须考虑其变异性。

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