Clinical Databases for Breast Cancer Research
Abstract
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.
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The large portion of breast and lung cancer studies can likely be attributed to their high prevalence and extensive clinical data availability, providing a rich dataset for deploying and testing LLMs.
“which can likely be attributed to their high prevalence and extensive clinical data availability, providing a rich dataset for deploying and testing the efficacy of LLMs [43]”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [43]
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- Authors
- Hwang, K.-T.
- Affiliations
- Seoul National University (KR)
- Corresponding author
- Hwang, Ki-Tae
- ORCID
- Hwang, Ki-Tae
- Venue
- Advances in Experimental Medicine and Biology
- Publisher
- Springer Singapore
- Published
- 2021
- Volume
- 1187
- Pages
- 493-509
- ISSN
- 0065-2598 (ISSN-L)
- 2214-8019
- OpenAlex
- W3161329058
- Cited by
- 12
- References
- 17
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 4 · 2022: 1 · 2021: 2
- FWCI
- 2.61
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in experimental medicine and biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- breast cancer, medical databases, medical data classification, clinical trials, big data analytics, artificial intelligence, deep learning, breast cancer epidemiology, breast cancer diagnosis, breast cancer treatment, breast cancer prognosis
- MeSH terms
- Big Data, Artificial Intelligence, Breast, Breast Neoplasms, Humans, Databases, Factual
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