标题：Value of Serial PSA Measurements for Prostate Cancer Prediction on Screening Using a Maximum Likelihood Estimation – Prostate Specific Antigen ( MLE - PSA ) Model
摘要：PSA measurements are used to assess the risk for prostate cancer. PSArange and PSA kinetics such as PSA velocity have been correlated with increasedcancer detection and assist the clinician in deciding when prostatebiopsy should be performed. Our aim is to evaluate the use of a novel, maximumlikelihood estimation - prostate speci c antigen (MLE-PSA) model forpredicting the probability of prostate cancer using serial PSA measurementscombined with PSA velocity in order to assess whether this reduces the needfor prostate biopsy.A total of 1976 Caucasian patients were included. All these patientshad at least 6 PSA serial measurements; all underwent trans-rectal biopsywith minimum 12 cores within the past 10 years. A multivariate logistic regressionmodel was developed using maximum likelihood estimation (MLE)based on the following parameters (age, at least 6 PSA serial measurements,baseline median natural logarithm of the PSA (ln(PSA)) and PSA velocity(ln(PSAV)), baseline process capability standard deviation of ln(PSA) andln(PSAV), signi cant special causes of variation in ln(PSA) and ln(PSAV)detected using control chart logic, and the volatility of the ln(PSAV). Wethen compared prostate cancer probability using MLE-PSA to the results ofprostate needle biopsy. The MLE-PSA model with a 50% cut-o probabilityhas a sensitivity of 87%, speci city of 85%, positive predictive value (PPV)of 89%, and negative predictive value (NPV) of 82%. By contrast, a singlePSA value with a 4ng/ml threshold has a sensitivity of 59%, speci city of33%, PPV of 56%, and NPV of 36% using the same population of patientsused to generate the MLE-PSA model. Based on serial PSA measurements,the use of the MLE-PSA model signi cantly (p-value < 0.0001) improvesprostate cancer detection and reduces the need for prostate biopsy.
