title: Classifying prostate cancer patients based on total prostate-specific antigen and free prostate-specific antigen features by support vector machine.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: Department of Basic Science, Nursing and Medical Technology, Ho Chi Minh City University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam.
authors.forename: Nguyen Thi Hong
authors.initials: NT
authors.lastname: Nhung
authors.affiliation: Faculty of Math and Computer Science, University of Science, Ho Chi Minh City, Vietnam.
authors.forename: Vu Tran Minh
authors.initials: VT
authors.lastname: Khuong
authors.affiliation: Medical Laboratory Falculty, Nursing and Medical Technology, Ho Chi Minh City University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam.
authors.forename: Vu Quang
authors.initials: VQ
authors.lastname: Huy
authors.affiliation: Faculty of Math and Computer Science, University of Science, Ho Chi Minh City, Vietnam.
authors.forename: Pham The
authors.initials: PT
authors.lastname: Bao
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor; D001706:Biopsy; D006801:Humans; D008297:Male; D008875:Middle Aged; D017430:Prostate-Specific Antigen / Q000378:metabolism*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D047368:Tumor Burden
chemical_list: D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen