Does [-2]Pro-Prostate Specific Antigen Meet the Criteria to Justify Its Inclusion in the Clinical Decision-Making Process?

INTRODUCTION: To assess whether [-2]pro-prostate-specific antigen (p2PSA) meets the criteria to justify its inclusion in a predictive model of prostate cancer (PCa) diagnosis and in the clinical decision-making process. MATERIALS AND METHODS: A total 172 men with total prostate-specific antigen of 2-10 ng/mL underwent measurement of free PSA and p2PSA before prostate biopsy in an observational and prospective study. From these measurements, the Prostate Health Index (PHI) was calculated. Clinical and analytical predictive models were created incorporating PHI. RESULTS: Of 172 men, 72 (42%) were diagnosed with PCa, 33 (46%) of whom were found to be with high-grade disease. PHI score was the most predictive of biopsy outcomes in terms of discriminative ability (area under the curve = 0.79), with an added gain in predictive accuracy of 17%. All the models that incorporated PHI worked better in terms of calibration close to 45° on the slope. In the decision curve analysis, at a threshold probability of 40% we could prevent 82 biopsies, missing only 16 tumors and 5 high-grade tumors. CONCLUSIONS: PHI score is a more discriminant biomarker, has superior calibration and superior net benefit, and provides a higher rate of avoided biopsies; thus, it can be useful for aiding in making a more informed decision for each patient.

Does obesity modify prostate cancer detection in a European cohort?

INTRODUCTION: To investigate prostate-specific antigen (PSA) accuracy and digital rectal examination (DRE) accuracy in detecting prostate cancer according to body mass index (BMI) in Spanish men with an indication of the first prostate biopsy. MATERIAL AND METHODS: We reviewed the clinical and histopathological data of 1,319 patients who underwent transrectal ultrasound-guided prostate needle biopsy. The patients were categorised according to the BMI as follows: <25 kg/m2 (normal weight); 25-29.9 kg/m2 (overweight); and ≥30 kg/m2 (obese). Receiver operator characteristic curves were used to assess PSA accuracy and DRE accuracy by calculating the area under the curve. RESULTS: The obesity rate of the cohort was 14%. PSA accuracy for predicting prostate cancer in each BMI category was 0.52, 0.58 and 0.62, respectively (p = 0.01). After stratification by DRE findings, there was no difference in the performance accuracy of PSA in predicting the presence of cancer across BMI groups in abnormal DRE (p = 0.90). Serum PSA, DRE and BMI were strong predictors of prostate cancer diagnosis (odds ratio 1.07, 2.02 and 1.4, respectively; p <0.001). When the DRE was abnormal, a BMI ≥30 increased the risk of prostate cancer twice. With the addition of BMI to the model, the area under the curve of the combined PSA and DRE for diagnosing prostate cancer improved from 0.60 to 0.63. CONCLUSIONS: The predictive value of PSA in predicting prostate cancer is not poorer in the obese population and the predictive value of an abnormal DRE in cancer detection is significantly modified by the patient's BMI.