Classifying prostate cancer patients based on total prostate-specific antigen and free prostate-specific antigen features by support vector machine

Aims of Study: In this work, we enhanced the role of prostate-specific antigen (PSA) test by examining the relation between free PSA (fPSA) and total PSA (tPSA) value and other biological information such as age and volume of prostate. Our primary goal is to find an approach that improves the sensitivity but still give a reasonable specificity. Subjects and Methods: We proposed a new approach to predict the prostate cancer (PCa) based on tPSA, fPSA, age, and prostate volume by using combination of statistical techniques and support vector machine (SVM). Our approach detected PCa based on following two steps: Classifying patients into normal or abnormal group by means of SVM method and then predicting which patients in abnormal group with PCa. Results: The sensitivity of our system was 95.1%, whereas the specificity was acceptable (84.6%). The positive biopsy rate was 58% while the unnecessary biopsy rate was 15.4%. We further developed a program to assist clinicians in predicting PCa. Conclusions: Applying SVM not only improved the performance of PSA test in screening and detecting PCa but also explored some molecular information. Based on the information, we can discover more knowledge about cancer disease.