open access

Vol 12, No 1 (2007)
Untitled
Published online: 2007-01-01
Submitted: 2006-07-24
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Clinical data analysis using artificial neural networks (ANN) and principal component analysis (PCA) of patients with breast cancer after mastectomy

Adam Buciński, Tomasz Bączek, Jerzy Krysiński, Renata Szoszkiewicz, Jerzy Załuski
DOI: 10.1016/S1507-1367(10)60036-3
·
Rep Pract Oncol Radiother 2007;12(1):9-17.

open access

Vol 12, No 1 (2007)
Untitled
Published online: 2007-01-01
Submitted: 2006-07-24

Abstract

Background

Exploitation of the several types of information on patient, disease and treatment variables ranging from sociological to genetic ones by means of chemometric analysis was considered and evaluated.

Aim

Performance of modern data processing methods, namely principal component analysis (PCA) and artificial neural network (ANN) analysis, is demonstrated for predictions of the recurrence of breast cancer in patients treated previously with mastectomy.

Materials/Methods

The data on 718 patients were retrospectively evaluated. 11 subject and treatment variables were determined for each patient. A matrix of 718×11 data points was subjected to PCA and ANN processing. The properly trained ANN was used to predict the patients with recurrence and without recurrence within a 10-year period after mastectomy.

Results

It was found that the prognostic potency of the trained and validated ANN was reasonably high. Additionally, using the principal component analysis (PCA) method two principal components, PC1 and PC2, were extracted from the input data. They accounted cumulatively for 37.5% of the variance of the data analyzed. An apparent clustering of the variables and patients was observed – these have been interpreted in terms of their similarities and dissimilarities.

Conclusions

It has been concluded that ANN analysis offers a promising implementation to established methods of statistical analysis of multivariable data on cancer patients. On the other hand, PCA has been recommended as an alternative to classical regression analysis of multivariable clinical data. By means of ANN and PCA practically useful systematic information may be extracted from large sets of data, which can be of value for prognosis and appropriate adjustment of the treatment of breast cancer.

Abstract

Background

Exploitation of the several types of information on patient, disease and treatment variables ranging from sociological to genetic ones by means of chemometric analysis was considered and evaluated.

Aim

Performance of modern data processing methods, namely principal component analysis (PCA) and artificial neural network (ANN) analysis, is demonstrated for predictions of the recurrence of breast cancer in patients treated previously with mastectomy.

Materials/Methods

The data on 718 patients were retrospectively evaluated. 11 subject and treatment variables were determined for each patient. A matrix of 718×11 data points was subjected to PCA and ANN processing. The properly trained ANN was used to predict the patients with recurrence and without recurrence within a 10-year period after mastectomy.

Results

It was found that the prognostic potency of the trained and validated ANN was reasonably high. Additionally, using the principal component analysis (PCA) method two principal components, PC1 and PC2, were extracted from the input data. They accounted cumulatively for 37.5% of the variance of the data analyzed. An apparent clustering of the variables and patients was observed – these have been interpreted in terms of their similarities and dissimilarities.

Conclusions

It has been concluded that ANN analysis offers a promising implementation to established methods of statistical analysis of multivariable data on cancer patients. On the other hand, PCA has been recommended as an alternative to classical regression analysis of multivariable clinical data. By means of ANN and PCA practically useful systematic information may be extracted from large sets of data, which can be of value for prognosis and appropriate adjustment of the treatment of breast cancer.

Get Citation

Keywords

breast cancer; mastectomy; recurrence model; artificial neural networks (ANN); principal component analysis (PCA)

About this article
Title

Clinical data analysis using artificial neural networks (ANN) and principal component analysis (PCA) of patients with breast cancer after mastectomy

Journal

Reports of Practical Oncology and Radiotherapy

Issue

Vol 12, No 1 (2007)

Pages

9-17

Published online

2007-01-01

DOI

10.1016/S1507-1367(10)60036-3

Bibliographic record

Rep Pract Oncol Radiother 2007;12(1):9-17.

Keywords

breast cancer
mastectomy
recurrence model
artificial neural networks (ANN)
principal component analysis (PCA)

Authors

Adam Buciński
Tomasz Bączek
Jerzy Krysiński
Renata Szoszkiewicz
Jerzy Załuski

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