Abstract

PURPOSE:

To systematically review the current literature and discuss the applications and limitations of radiomics and machine-learning augmented radiomics in the management of bladder cancer.

METHODS:

Pubmed ®, Scopus ®, and Web of Science ® databases were searched systematically for all full-text English-language articles assessing the impact of Artificial Intelligence OR Radiomics OR Machine Learning AND Bladder Cancer AND (staging OR grading OR prognosis) published up to January 2020.

RESULTS:

Of the 686 articles that were identified, 13 studies met the criteria for quantitative analysis. Staging, Grading and Tumor Classification, Prognosis, and Therapy Response were discussed in 7, 3, 2 and 7 studies, respectively. Data on cost of implementation were not reported. CT and MRI were the most common imaging approaches.

CONCLUSION:

Radiomics shows potentials in bladder cancer detection, staging, grading, and response to therapy, thereby supporting the physician in personalizing patient management. Extension and validation of this promising technology in large multisite prospective trials is warranted to pave the way for its clinical translation.

中文翻译：

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放射性药物和膀胱癌：现状

摘要

目的：

系统地回顾当前的文献，并讨论放射线学和机器学习增强放射线学在膀胱癌管理中的应用和局限性。

方法：

系统搜索了Pubmed®，Scopus®和Web of Science®数据库，以查找所有评估1月份之前发布的人工智能或放射学或机器学习与膀胱癌以及（分期或分级或预后）的影响的全文英语文章2020年。

结果：

在确定的686篇文章中，有13项研究符合定量分析的标准。分期，分级和肿瘤分类，预后和治疗反应分别在7、3、2和7个研究中进行了讨论。没有报告执行费用的数据。CT和MRI是最常见的成像方法。

结论：

Radiomics在膀胱癌的检测，分期，分级和对治疗的反应中显示出潜力，从而支持医生个性化患者管理。这项有前途的技术在大型多站点前瞻性试验中的扩展和验证必将为其临床翻译铺平道路。