Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application-Reference-Cited by-同舟云学术

Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application

Published:2023-12 Issue:40 Volume:19 Page:2651-2667
ISSN:1479-6694
Container-title:Future Oncology
language:en
Short-container-title:Future Oncology

Author:

Gu Yuan¹^ORCID,Wang Mingyue²^ORCID,Gong Yishu³^ORCID,Li Xin¹^ORCID,Wang Ziyang⁴^ORCID,Wang Yuli⁵^ORCID,Jiang Song⁶^ORCID,Zhang Dan⁷^ORCID,Li Chen⁸^ORCID

Affiliation:

1. Department of Statistics, The George Washington University, Washington, DC 20052, USA

2. Department of Mathematics, Syracuse University, Syracuse, NY 13244, USA

3. Harvard T.H. Chan School of Public Health, Harvard University, Boston, NY 02115, USA

4. Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK

5. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA

6. Department of Biochemistry, Huzhou Institute of Biological Products Co., Ltd., 313017, China

7. Department of Information Science and Engineering, Shandong University, Shan Dong, China

8. Department of Biology, Chemistry and Pharmacy, Free University of Berlin, Berlin, 14195, Germany

Abstract

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan–Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.

Publisher

Future Medicine Ltd

Subject

Cancer Research,Oncology,General Medicine

Link

https://www.futuremedicine.com/doi/pdf/10.2217/fon-2023-0736

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