Chinese Medical Sciences Journal ›› 2021, Vol. 36 ›› Issue (3): 196-203.doi: 10.24920/003963

所属专题: 人工智能与精准肿瘤学

• 综述 • 上一篇    下一篇

人工智能在卵巢癌医学影像中的应用进展

陈旭1,霍晓菲1,吴哲2,陆菁菁1,*()   

  1. 1北京和睦家医院放射科,北京 100015,中国
    2抚顺市中心医院放射科,辽宁 113006,中国
  • 收稿日期:2021-06-30 接受日期:2021-08-24 出版日期:2021-09-30 发布日期:2021-08-30
  • 通讯作者: 陆菁菁 E-mail:cjr.lujingjing@vip.163.com

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

Chen Xu1,Huo Xiaofei1,Wu Zhe2,Lu Jingjing1,*()   

  1. 1Department of Radiology, Beijing United Family Hospital, Beijing 100015, China
    2Department of Radiology, Fushun Central Hospital, Fushun, Liaoning 113006, China
  • Received:2021-06-30 Accepted:2021-08-24 Online:2021-09-30 Published:2021-08-30
  • Contact: Lu Jingjing E-mail:cjr.lujingjing@vip.163.com

摘要:

卵巢癌是世界范围内常见的三大妇科恶性肿瘤之一,但病死率居首位。近年来,许多医学及工程技术研究尝试将人工智能(artificial intelligence,AI)技术应用于卵巢癌诊治的多个临床场景中,在医学影像AI技术的开发与应用中取得了较为丰富的进展。AI相关的医学影像研究主要涉及计算机断层扫描、超声成像和磁共振成像。我们对AI在卵巢癌医学影像方面的已发表的研究进行了文献检索和回顾,从卵巢癌的影像诊断、病理分类,靶向活检引导以及预后预测四个方面深入分析了医学影像领域AI相关研究的最新进展,同时对其现状及存在的问题进行了相应阐述。

关键词: 人工智能, 机器学习, 卵巢癌, 放射组学, 算法, 医学影像

Abstract:

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women’s cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.

Key words: artificial intelligence, machine learning, ovarian cancer, radiomics, algorithm, medical imaging

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