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

Abstract

Summary 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.