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

To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects...

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Published in	Future oncology (London, England) Vol. 19; no. 40; pp. 2651 - 2667
Main Authors	Gu, Yuan, Wang, Mingyue, Gong, Yishu, Li, Xin, Wang, Ziyang, Wang, Yuli, Jiang, Song, Zhang, Dan, Li, Chen
Format	Journal Article
Language	English
Published	England Future Medicine Ltd 01.12.2023
Subjects	breast cancer Breast Neoplasms - epidemiology Breast Neoplasms - genetics Breast Neoplasms - therapy cancer risk profiles Cluster Analysis Cox regression Female Humans K-means clustering Kaplan–Meier curve machine learning Risk Factors shiny survival Survival Analysis unsupervised learning web-based application survival breast cancer shiny Cox regression machine learning web-based application Kaplan–Meier curve K-means clustering cancer risk profiles unsupervised learning
Online Access	Get full text
ISSN	1479-6694 1744-8301 1744-8301
DOI	10.2217/fon-2023-0736

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Abstract	To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. 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. 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.
AbstractList	To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. 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. 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. 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.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.
Author	Gong, Yishu Li, Chen Wang, Mingyue Gu, Yuan Wang, Yuli Wang, Ziyang Zhang, Dan Li, Xin Jiang, Song
AuthorAffiliation	4Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK 5Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA 7Department of Information Science and Engineering, Shandong University, Shan Dong, China 3Harvard T.H. Chan School of Public Health, Harvard University, Boston, NY 02115, USA 2Department of Mathematics, Syracuse University, Syracuse, NY 13244, USA 6Department of Biochemistry, Huzhou Institute of Biological Products Co., Ltd., 313017, China 8Department of Biology, Chemistry and Pharmacy, Free University of Berlin, Berlin, 14195, Germany 1Department of Statistics, The George Washington University, Washington, DC 20052, USA
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Snippet	To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival... Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival...
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SubjectTerms	breast cancer Breast Neoplasms - epidemiology Breast Neoplasms - genetics Breast Neoplasms - therapy cancer risk profiles Cluster Analysis Cox regression Female Humans K-means clustering Kaplan–Meier curve machine learning Risk Factors shiny survival Survival Analysis unsupervised learning web-based application
Title	Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application
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