No Cover Image

E-Thesis 165 views

Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival / LEEN INTABLI

Swansea University Author: LEEN INTABLI

Full text not available from this repository: check for access using links below.

DOI (Published version): 10.23889/SUthesis.59497

Abstract

Breast cancer is the most common neoplasm in women in the UK with around 55,000 new diagnosed cases every year. Due to major advancements made in research and therapy in srecent years, breast cancer is now one of the best understood human cancers. It is understood that breast cancer is a group of bi...

Full description

Published: Swansea 2021
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Lewis, Paul
URI: https://cronfa.swan.ac.uk/Record/cronfa59497
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-03-04T16:53:41Z
last_indexed 2022-03-08T04:28:39Z
id cronfa59497
recordtype RisThesis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-03-07T12:24:13.1686065</datestamp><bib-version>v2</bib-version><id>59497</id><entry>2022-03-04</entry><title>Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival</title><swanseaauthors><author><sid>13d7d1fc2af45ddc9e285f0c988bdb24</sid><firstname>LEEN</firstname><surname>INTABLI</surname><name>LEEN INTABLI</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-03-04</date><abstract>Breast cancer is the most common neoplasm in women in the UK with around 55,000 new diagnosed cases every year. Due to major advancements made in research and therapy in srecent years, breast cancer is now one of the best understood human cancers. It is understood that breast cancer is a group of biologically different diseases, each influenced by a number of pathological and demographic factors. Age at diagnosis has been heavily studied and linked to different survival patterns, as tumours diagnosed in younger and older groups are often the outcome of different causes and have different responses to treatment regimens. In the age of data-backed decision making process, recent years have seen a growth in interest in analysis-based research and decision support tools, in medicine and other disciplines. Currently in Wales, there is a lack of a large, comprehensive database of accurately recorded demographic, clinical and socioeconomic information on breast cancer patients that can be utilised in answering a variety of clinical and socioeconomic research questions within the population. The aim of our work was to create a local interactive online analysis and predictive tool that allows users to define prognostic criteria and return survival patterns through multivariate analysis of a database of patients diagnosed in South West Wales. The Welsh Breast Cancer Survival Database of patients diagnosed in Wales between 1996 and 2011 was constructed containing multiple variables including: age at diagnosis, tumour pathology and survival outcomes. For multivariate analysis, principal component analysis (PCA) and partition around Medoids (PAM) clustering methods were implemented using R statistical programming. Survival analysis visualisation and statistical methods included Kaplan-Meier curves and Cox proportional hazards model. CaFro, a web-based analytical tool that allows for variable selection, survival and cluster analyses of various diagnostic and prognostic factors was developed using the shiny package on R studio. The tool was also utilised to compare different age groups within South-West Wales, and differences in survival rates of up to 7% were detected. Another aim of this project was to compare survival patterns based on socioeconomic differences of patients in the area, based on the Welsh Index of Multiple Deprivations (WIMD) score of their address postcodes at the time of diagnosis. Health, income, education and employment deprivations were all shown to influence survival time.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords/><publishedDay>5</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-02-05</publishedDate><doi>10.23889/SUthesis.59497</doi><url/><notes>Due to Embargo and/or Third Party Copyright restrictions, this thesis is not available via this service.</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Lewis, Paul</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><apcterm/><lastEdited>2022-03-07T12:24:13.1686065</lastEdited><Created>2022-03-04T16:46:44.5739276</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Business Management</level></path><authors><author><firstname>LEEN</firstname><surname>INTABLI</surname><order>1</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2022-03-07T12:24:13.1686065 v2 59497 2022-03-04 Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival 13d7d1fc2af45ddc9e285f0c988bdb24 LEEN INTABLI LEEN INTABLI true false 2022-03-04 Breast cancer is the most common neoplasm in women in the UK with around 55,000 new diagnosed cases every year. Due to major advancements made in research and therapy in srecent years, breast cancer is now one of the best understood human cancers. It is understood that breast cancer is a group of biologically different diseases, each influenced by a number of pathological and demographic factors. Age at diagnosis has been heavily studied and linked to different survival patterns, as tumours diagnosed in younger and older groups are often the outcome of different causes and have different responses to treatment regimens. In the age of data-backed decision making process, recent years have seen a growth in interest in analysis-based research and decision support tools, in medicine and other disciplines. Currently in Wales, there is a lack of a large, comprehensive database of accurately recorded demographic, clinical and socioeconomic information on breast cancer patients that can be utilised in answering a variety of clinical and socioeconomic research questions within the population. The aim of our work was to create a local interactive online analysis and predictive tool that allows users to define prognostic criteria and return survival patterns through multivariate analysis of a database of patients diagnosed in South West Wales. The Welsh Breast Cancer Survival Database of patients diagnosed in Wales between 1996 and 2011 was constructed containing multiple variables including: age at diagnosis, tumour pathology and survival outcomes. For multivariate analysis, principal component analysis (PCA) and partition around Medoids (PAM) clustering methods were implemented using R statistical programming. Survival analysis visualisation and statistical methods included Kaplan-Meier curves and Cox proportional hazards model. CaFro, a web-based analytical tool that allows for variable selection, survival and cluster analyses of various diagnostic and prognostic factors was developed using the shiny package on R studio. The tool was also utilised to compare different age groups within South-West Wales, and differences in survival rates of up to 7% were detected. Another aim of this project was to compare survival patterns based on socioeconomic differences of patients in the area, based on the Welsh Index of Multiple Deprivations (WIMD) score of their address postcodes at the time of diagnosis. Health, income, education and employment deprivations were all shown to influence survival time. E-Thesis Swansea 5 2 2021 2021-02-05 10.23889/SUthesis.59497 Due to Embargo and/or Third Party Copyright restrictions, this thesis is not available via this service. COLLEGE NANME COLLEGE CODE Swansea University Lewis, Paul Doctoral Ph.D 2022-03-07T12:24:13.1686065 2022-03-04T16:46:44.5739276 Faculty of Humanities and Social Sciences School of Management - Business Management LEEN INTABLI 1
title Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival
spellingShingle Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival
LEEN INTABLI
title_short Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival
title_full Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival
title_fullStr Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival
title_full_unstemmed Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival
title_sort Development of a Large Welsh Breast Cancer Prognostic Research Database and Analysis of Influential Demographic, Pathological and Socioeconomic Factors on Survival
author_id_str_mv 13d7d1fc2af45ddc9e285f0c988bdb24
author_id_fullname_str_mv 13d7d1fc2af45ddc9e285f0c988bdb24_***_LEEN INTABLI
author LEEN INTABLI
author2 LEEN INTABLI
format E-Thesis
publishDate 2021
institution Swansea University
doi_str_mv 10.23889/SUthesis.59497
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 0
active_str 0
description Breast cancer is the most common neoplasm in women in the UK with around 55,000 new diagnosed cases every year. Due to major advancements made in research and therapy in srecent years, breast cancer is now one of the best understood human cancers. It is understood that breast cancer is a group of biologically different diseases, each influenced by a number of pathological and demographic factors. Age at diagnosis has been heavily studied and linked to different survival patterns, as tumours diagnosed in younger and older groups are often the outcome of different causes and have different responses to treatment regimens. In the age of data-backed decision making process, recent years have seen a growth in interest in analysis-based research and decision support tools, in medicine and other disciplines. Currently in Wales, there is a lack of a large, comprehensive database of accurately recorded demographic, clinical and socioeconomic information on breast cancer patients that can be utilised in answering a variety of clinical and socioeconomic research questions within the population. The aim of our work was to create a local interactive online analysis and predictive tool that allows users to define prognostic criteria and return survival patterns through multivariate analysis of a database of patients diagnosed in South West Wales. The Welsh Breast Cancer Survival Database of patients diagnosed in Wales between 1996 and 2011 was constructed containing multiple variables including: age at diagnosis, tumour pathology and survival outcomes. For multivariate analysis, principal component analysis (PCA) and partition around Medoids (PAM) clustering methods were implemented using R statistical programming. Survival analysis visualisation and statistical methods included Kaplan-Meier curves and Cox proportional hazards model. CaFro, a web-based analytical tool that allows for variable selection, survival and cluster analyses of various diagnostic and prognostic factors was developed using the shiny package on R studio. The tool was also utilised to compare different age groups within South-West Wales, and differences in survival rates of up to 7% were detected. Another aim of this project was to compare survival patterns based on socioeconomic differences of patients in the area, based on the Welsh Index of Multiple Deprivations (WIMD) score of their address postcodes at the time of diagnosis. Health, income, education and employment deprivations were all shown to influence survival time.
published_date 2021-02-05T04:16:51Z
_version_ 1763754127340339200
score 11.013082

Similar Items