Department of Surgery, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea.
Adv Exp Med Biol. 2021;1187:493-509. doi: 10.1007/978-981-32-9620-6_26.
Clinical database is a collection of clinical data related to patients, which can be used for analysis and research. Clinical data can be classified into several categories: patient-related, tumor-related, diagnostics-related, treatment-related, outcome-related, administration-related, and other clinical data. Clinical databases can be classified according to the data types of clinical databases, ranges of institutes, and accessibility to data. The numbers of papers and clinical trials are rapidly increasing. Recently, more than 9000 papers related to breast cancer have been published annually, and more than 7000 papers related to human breast cancer are published annually. The speed of increase is expected to be faster and faster in future. Now, almost 8000 clinical trials are registered world widely. Main research areas of breast cancer can be classified into followings; epidemiology, screening and prevention, diagnosis, treatment, and prognosis. Clinical databases that are available for breast cancer research are also introduced in this chapter. The analysis of big data is expected to be the mainstream of breast cancer research using clinical databases. As the technology of artificial intelligence (AI) is rapidly evolving, the technology of deep learning starts to be applied for breast cancer research. In near future, AI technology is predicted to penetrate deeply the field of breast cancer research.
临床数据库是与患者相关的临床数据的集合,可用于分析和研究。临床数据可以分为几类:患者相关、肿瘤相关、诊断相关、治疗相关、预后相关、管理相关和其他临床数据。临床数据库可以根据临床数据库的数据类型、机构范围和数据可及性进行分类。论文和临床试验的数量正在迅速增加。最近,每年发表的与乳腺癌相关的论文超过 9000 篇,每年发表的与人类乳腺癌相关的论文超过 7000 篇。预计未来的增长速度会越来越快。现在,全世界有近 8000 项临床试验正在注册。乳腺癌的主要研究领域可以分为以下几个方面:流行病学、筛查和预防、诊断、治疗和预后。本章还介绍了可用于乳腺癌研究的临床数据库。利用临床数据库进行大数据分析有望成为乳腺癌研究的主流。随着人工智能(AI)技术的快速发展,深度学习技术开始应用于乳腺癌研究。在不久的将来,人工智能技术预计将深入渗透到乳腺癌研究领域。