尿石症中的放射组学:当前应用、局限性及未来方向的系统评价

Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions.

作者信息

Lim Ee Jean, Castellani Daniele, So Wei Zheng, Fong Khi Yung, Li Jing Qiu, Tiong Ho Yee, Gadzhiev Nariman, Heng Chin Tiong, Teoh Jeremy Yuen-Chun, Naik Nithesh, Ghani Khurshid, Sarica Kemal, De La Rosette Jean, Somani Bhaskar, Gauhar Vineet

机构信息

Department of Urology, Singapore General Hospital, Singapore 169608, Singapore.

Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica Delle Marche, 60126 Ancona, Italy.

出版信息

J Clin Med. 2022 Aug 31;11(17):5151. doi: 10.3390/jcm11175151.

Abstract

Radiomics is increasingly applied to the diagnosis, management, and outcome prediction of various urological conditions. Urolithiasis is a common benign condition with a high incidence and recurrence rate. The purpose of this scoping review is to evaluate the current evidence of the application of radiomics in urolithiasis, especially its utility in diagnostics and therapeutics. An electronic literature search on radiomics in the setting of urolithiasis was conducted on PubMed, EMBASE, and Scopus from inception to 21 March 2022. A total of 7 studies were included. Radiomics has been successfully applied in the field of urolithiasis to differentiate phleboliths from calculi and classify stone types and composition pre-operatively. More importantly, it has also been utilized to predict outcomes and complications after endourological procedures. Although radiomics in urolithiasis is still in its infancy, it has the potential for large-scale implementation. Its greatest potential lies in the correlation with conventional established diagnostic and therapeutic factors.

摘要

放射组学越来越多地应用于各种泌尿系统疾病的诊断、管理和预后预测。尿路结石是一种常见的良性疾病,发病率和复发率都很高。本综述的目的是评估放射组学在尿路结石中应用的现有证据,尤其是其在诊断和治疗方面的效用。从创刊到2022年3月21日,在PubMed、EMBASE和Scopus上对尿路结石背景下的放射组学进行了电子文献检索。共纳入7项研究。放射组学已成功应用于尿路结石领域,以区分静脉石和结石,并在术前对结石类型和成分进行分类。更重要的是,它还被用于预测腔内泌尿外科手术后的结果和并发症。尽管尿路结石的放射组学仍处于起步阶段,但它具有大规模应用的潜力。其最大的潜力在于与传统的既定诊断和治疗因素的相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0283/9457189/eb775052745a/jcm-11-05151-g0A1.jpg

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