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基于机器学习的影像组学在肌层浸润性膀胱癌预测中的作用:一篇综述。

The role of radiomics with machine learning in the prediction of muscle-invasive bladder cancer: A mini review.

作者信息

Huang Xiaodan, Wang Xiangyu, Lan Xinxin, Deng Jinhuan, Lei Yi, Lin Fan

机构信息

Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China.

出版信息

Front Oncol. 2022 Aug 17;12:990176. doi: 10.3389/fonc.2022.990176. eCollection 2022.

Abstract

Bladder cancer is a common malignant tumor in the urinary system. Depending on whether bladder cancer invades muscle tissue, it is classified into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). It is crucial to accurately diagnose the muscle invasion of bladder cancer for its clinical management. Although imaging modalities such as CT and multiparametric MRI play an important role in this regard, radiomics has shown great potential with the development and innovation of precision medicine. It features outstanding advantages such as non-invasive and high efficiency, and takes on important significance in tumor assessment and laor liberation. In this article, we provide an overview of radiomics in the prediction of muscle-invasive bladder cancer and reflect on its future trends and challenges.

摘要

膀胱癌是泌尿系统常见的恶性肿瘤。根据膀胱癌是否侵犯肌肉组织,可分为非肌层浸润性膀胱癌(NMIBC)和肌层浸润性膀胱癌(MIBC)。准确诊断膀胱癌的肌肉浸润情况对于其临床治疗至关重要。尽管CT和多参数MRI等影像学检查在这方面发挥着重要作用,但随着精准医学的发展与创新,放射组学已显示出巨大潜力。它具有无创、高效等突出优势,在肿瘤评估和治疗中具有重要意义。在本文中,我们概述了放射组学在预测肌层浸润性膀胱癌方面的应用,并对其未来趋势和挑战进行了思考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd5b/9428259/c7132092d53f/fonc-12-990176-g001.jpg

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