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Exploring cancer register data to find risk factors for recurrence of breast cancer: Application of Canonical Correlation Analysis
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Clinical and Experimental Medicine, Oncology . Linköping University, Faculty of Health Sciences.
Department of Surgery, County Hospital, Kalmar, Sweden.
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2005 (English)In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, Vol. 5, no 29, 29-35 p.Article in journal (Refereed) Published
Abstract [en]


A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time.

One essential outcome after breast cancer treatment is recurrence of the disease. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model.


Data for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. By using CCA and looking at the structure coefficients (loadings), relationships between tumor specifications and the two outcomes during different time intervals were analyzed and a correlation model was built.


The analysis successfully detected known predictors for breast cancer recurrence during the first two years and distant metastasis 2–4 years after diagnosis. Nottingham Histologic Grading (NHG) was the most important predictor, while age of the patient at the time of diagnosis was not an important predictor.


In cancer registers with high dimensionality, CCA can be used for identifying the importance of risk factors for breast cancer recurrence. This technique can result in a model ready for further processing by data mining methods through reducing the number of variables to important ones.

Place, publisher, year, edition, pages
2005. Vol. 5, no 29, 29-35 p.
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-12706DOI: 10.1186/1472-6947-5-29OAI: diva2:16890
Available from: 2009-02-22 Created: 2008-10-24 Last updated: 2009-03-10Bibliographically approved
In thesis
1. Applications of Knowledge Discovery in Quality Registries - Predicting Recurrence of Breast Cancer and Analyzing Non-compliance with a Clinical Guideline
Open this publication in new window or tab >>Applications of Knowledge Discovery in Quality Registries - Predicting Recurrence of Breast Cancer and Analyzing Non-compliance with a Clinical Guideline
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In medicine, data are produced from different sources and continuously stored in data depositories. Examples of these growing databases are quality registries. In Sweden, there are many cancer registries where data on cancer patients are gathered and recorded and are used mainly for reporting survival analyses to high level health authorities.

In this thesis, a breast cancer quality registry operating in South-East of Sweden is used as the data source for newer analytical techniques, i.e. data mining as a part of knowledge discovery in databases (KDD) methodology. Analyses are done to sift through these data in order to find interesting information and hidden knowledge. KDD consists of multiple steps, starting with gathering data from different sources and preparing them in data pre-processing stages prior to data mining.

Data were cleaned from outliers and noise and missing values were handled. Then a proper subset of the data was chosen by canonical correlation analysis (CCA) in a dimensionality reduction step. This technique was chosen because there were multiple outcomes, and variables had complex relationship to one another.

After data were prepared, they were analyzed with a data mining method. Decision tree induction as a simple and efficient method was used to mine the data. To show the benefits of proper data pre-processing, results from data mining with pre-processing of the data were compared with results from data mining without data pre-processing. The comparison showed that data pre-processing results in a more compact model with a better performance in predicting the recurrence of cancer.

An important part of knowledge discovery in medicine is to increase the involvement of medical experts in the process. This starts with enquiry about current problems in their field, which leads to finding areas where computer support can be helpful. The experts can suggest potentially important variables and should then approve and validate new patterns or knowledge as predictive or descriptive models. If it can be shown that the performance of a model is comparable to domain experts, it is more probable that the model will be used to support physicians in their daily decision-making. In this thesis, we validated the model by comparing predictions done by data mining and those made by domain experts without finding any significant difference between them.

Breast cancer patients who are treated with mastectomy are recommended to receive radiotherapy. This treatment is called postmastectomy radiotherapy (PMRT) and there is a guideline for prescribing it. A history of this treatment is stored in breast cancer registries. We analyzed these datasets using rules from a clinical guideline and identified cases that had not been treated according to the PMRT guideline. Data mining revealed some patterns of non-compliance with the PMRT guideline. Further analysis with data mining revealed some reasons for guideline non-compliance. These patterns were then compared with reasons acquired from manual inspection of patient records. The comparisons showed that patterns resulting from data mining were limited to the stored variables in the registry. A prerequisite for better results is availability of comprehensive datasets.

Medicine can take advantage of KDD methodology in different ways. The main advantage is being able to reuse information and explore hidden knowledge that can be obtained using advanced analysis techniques. The results depend on good collaboration between medical informaticians and domain experts and the availability of high quality data.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik, 2007. 58 p.
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1018
Breast cancer, Clinical guidelines, Canonical correlation analysis, Data Mining, Data pre-processing, Decision tree induction, Knowledge Discovery in Databases
National Category
Biomedical Laboratory Science/Technology
urn:nbn:se:liu:diva-10142 (URN)978-91-85895-81-6 (ISBN)
Public defence
2007-11-22, Elsa Brändström, Campus US, Linköpings universitet, Linköping, 09:00 (English)
Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2009-05-12

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