Developing a medical scoreboard for prediction of breast cancer recurrence / Nurul Husna Jamian

Jamian, Nurul Husna (2013) Developing a medical scoreboard for prediction of breast cancer recurrence / Nurul Husna Jamian. Masters thesis, Universiti Teknologi MARA.

Abstract

Breast cancer is the commonest diagnosed cancer with substantial high proportion of
female globally. Incidence of breast cancer in Asia is lower than in the west but the
incidence is increasing rapidly. In Malaysia, the most common cancer occurred among
women is breast cancer. Breast cancer recurrence can be defined as the return of
breast cancer after primary treatment and can recur within the first three to five years.
The Preliminary Report by National Cancer Patient Registry (NCPR) in 2008 reported
that 108 out of 154 patients with available follow-up information (1st June - 31st Dec
2008), 94.4 percent patients are disease free while 5.6 percent patients are recurrent
cases. There is still lack of studies on the risk factors of breast cancer recurrence in
Malaysia. Most studies focus on risk factors and survival rate of breast cancer
patients. Breast cancer recurrence studies have been mostly conducted in developing
countries such United Stated, Japan and Canada. Thus, the primary aim of this study is
to identify the risk factors of breast cancer recurrence among Malaysian women. Out
of 1149 patients who were diagnosed and undergo treatment at Department of
Surgery, Hospital Kuala Lumpur and only 454 cleaned data were used in this study.
Data obtain retrospectively cohort from year 2006 until 2011 . The outcome was
dichotomized into recurrence (code 1) and remission (code 0) with thit1een categorical
predictors categorized into patients' background and medical factors. Breast Cancer
Recurrence scorecard (BCR-scorecard) model and Logistic Regression model were
developed and compared using SAS Enterprise Miner 7 .1. BCR-scorecard model is
better predictive model with lower misclassification rate (18%) compared to Logistic
Regression model (23%). Five important risk factors were identified: histological
type, race, stage, tumour size and vascular invasion in predicting recurrence status.

Metadata

Item Type: Thesis (Masters)
Creators:
Creators
Email / ID Num.
Jamian, Nurul Husna
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Mathematical statistics. Probabilities
R Medicine > RC Internal Medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Date: 2013
URI: https://ir.uitm.edu.my/id/eprint/12031
Edit Item
Edit Item

Download

[thumbnail of TM_NURUL HUSNA JAMIAN CS 13_5 1.pdf] Text
TM_NURUL HUSNA JAMIAN CS 13_5 1.pdf

Download (1MB)

Fulltext

Fulltext is available at:

ID Number

12031

Indexing

|

Statistic

Statistic details