Background: The probability of a prostate cancer-positive biopsy result varies with PSA concentration. Thus, we applied information theory on classification and regression tree (CART) analysis for decision making predicting the probability of a biopsy result at various PSA concentrations.
Methods: From 2007 to 2009, prostate biopsies were performed in 664 referred patients in a tertiary hospital. We created 2 CART models based on the information theory: one for moderate uncertainty (PSA concentration: 2.5-10 ng/ml) and the other for high uncertainty (PSA concentration: 10-25 ng/ml).
Results: The CART model for moderate uncertainty (n=321) had 3 splits based on PSA density (PSAD), hypoechoic nodules, and age and the other CART for high uncertainty (n=160) had 2 splits based on prostate volume and percent-free PSA. In this validation set, the patients (14.3% and 14.0% for moderate and high uncertainty groups, respectively) could avoid unnecessary biopsies without false-negative results.
Conclusions: Using these CART models based on uncertainty information of PSA, the overall reduction in unnecessary prostate biopsies was 14.0-14.3% and CART models were simplified. Using uncertainty of laboratory results from information theoretic approach can provide additional information for decision analysis such as CART.
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