Multivariate Statistical Process Control and Classification Applied on Prostate Cancer Screening

Introduction: We report in this study the results of analyzing biomarkers in blood samples with two objectives; i) as an approach for screening patients by use of Multivariate Statistical Process Control (MSPC); ii) Compare various classification methods with the purpose of diagnosing prostate cancer. Methods: We applied Principal Component Analysis (PCA) with statistical limits for outlier detection. Various splits of the data into training and test sets were chosen to evaluate the performance of classification methods as a function of the training/test sample ratio. Results: MSPC based on 12 analytes in blood samples was shown to outperform the traditional biomarker criterion: the level of the analyte Prostate-Specific Antigen (PSA), in screening for prostate cancer. The performance of different multivariate classification techniques for classifying which of the patients in a clinical pathway for prostate cancer have malignant tumors showed that the basic method Linear Discriminant Analysis (LDA) and classification trees gave similar results, whereas adaboost gave a higher specificity but lower sensitivity. Conclusion: The accuracy, especially the sensitivity, does not justify any clinical use of the applied classification methods with the available biomarkers. Additional medical information about the patients might enhance the accuracy with the purpose of identifying benign and malignant tumors.