Henning Cammann

A [-2]proPSA-based artificial neural network significantly improves differentiation between prostate cancer and benign prostatic diseases

The aim of this study was to combine the new automated Access [‐2]proPSA (p2PSA) assay with a percent free PSA (%fPSA) based artificial neural network (ANN) or logistic regression (LR) model to enhance discrimination between patients with prostate cancer (PCa) and with no evidence of malignancy (NEM) and to detect aggressive PCa.

Comparative Assessment of Urinary Prostate Cancer Antigen 3 and TMPRSS2:ERG Gene Fusion with the Serum [−2]Proprostate-Specific Antigen–Based Prostate Health Index for Detection of Prostate Cancer

BACKGROUND We compared urinary prostate cancer antigen 3 (PCA3), transmembrane protease, serine 2 (TMPRSS2):v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) gene fusion (T2:ERG), and the serum [-2]proprostate-specific antigen ([-2]proPSA)-based prostate health index (Phi) for predicting biopsy outcome. METHODS Serum samples and first-catch urine samples were collected after digital rectal examination (DRE) from consented outpatients with PSA 0.5-20 μg/L who were scheduled for prostate biopsy. The PCA3 score (PROGENSA PCA3, Hologic Gen-Probe) and T2:ERG score (Hologic Gen-Probe) were determined. Measurements of serum PSA, free PSA, and [-2]proPSA (Beckman Coulter) were performed, and the percentages of free PSA (%fPSA) and Phi ([-2]proPSA/fPSA × √PSA) were determined. RESULTS Of 246 enrolled men, prostate cancer (PCa) was diagnosed in 110 (45%) and there was no evidence of malignancy (NEM) in 136 (55%). A first set of biopsies was performed in 136 (55%) of all men, and 110 (45%) had ≥1 repeat biopsies. PCA3, Phi, and T2:ERG differed significantly between men with PCa and NEM, and these markers showed the largest areas under the ROC curve (AUCs) (0.74, 0.68, and 0.63, respectively). PCA3 had the largest AUC of all parameters, albeit not statistically different from Phi. Phi showed somewhat lower specificities than PCA3 at 90% sensitivity. Combination of both markers enhanced diagnostic power with modest AUC gains of 0.01-0.04. Although PCA3 had the highest AUC in the repeat-biopsy cohort, the highest AUC for Phi was observed in DRE-negative patients with PSA in the 2-10 μg/L range. CONCLUSIONS PCA3 and Phi were superior to the other evaluated parameters but their combination gave only moderate enhancements in diagnostic accuracy for PCa at first or repeat prostate biopsy.

Multicenter Evaluation of [−2]Proprostate-Specific Antigen and the Prostate Health Index for Detecting Prostate Cancer

BACKGROUND Total prostate-specific antigen (tPSA) is flawed for prostate cancer (PCa) detection. [-2]proprostate-specific antigen (p2PSA), a molecular isoform of free PSA (fPSA), shows higher specificity compared with tPSA or percentage of free PSA (%fPSA). The prostate health index (Phi), a measure based on p2PSA and calculated as p2PSA/fPSA × √tPSA, was evaluated in a multicenter study for detecting PCa. METHODS A total of 1362 patients from 4 different study sites who had tPSA values of 1.6-8.0 μg/L (668 patients with PCa, 694 without PCa) underwent ≥10 core biopsies. Serum concentrations of tPSA, fPSA (both calibrated against a WHO reference material), and p2PSA were measured on Access2 or DxI800 analyzers (Beckman Coulter). RESULTS The percentage ratio of p2PSA to fPSA (%p2PSA) and Phi were significantly higher in all PCa subcohorts (positive initial or repeat biopsy result or negative digital rectal examination) (P < 0.0001) compared with patients without PCa. Phi had the largest area under the ROC curve (AUC) (AUC = 0.74) and provided significantly better clinical performance for predicting PCa compared with %p2PSA (AUC = 0.72, P = 0.018), p2PSA (AUC = 0.63, P < 0.0001), %fPSA (AUC = 0.61) or tPSA (AUC = 0.56). Significantly higher median values of Phi were observed for patients with a Gleason score ≥7 (Phi = 60) compared with a Gleason score <7 (Phi = 53; P = 0.0018). The proportion of aggressive PCa (Gleason score ≥7) increased with the Phi score. CONCLUSIONS The results of this multicenter study show that Phi, compared with tPSA or %fPSA, demonstrated superior clinical performance in detecting PCa at tPSA 1.6-8.0 μg/L (i.e., approximately 2-10 μg/L in traditional calibration) and is better able to detect aggressive PCa.

Clinical utility of human glandular kallikrein 2 within a neural network for prostate cancer detection

To assess, using artificial neural networks (ANNs), human glandular kallikrein 2 (hK2), prostate‐specific antigen (PSA), and percentage free/total PSA (f/tPSA), for discriminating between prostate cancer and benign prostatic hyperplasia (BPH).

An artificial neural network for five different assay systems of prostate-specific antigen in prostate cancer diagnostics

To compare separate prostate‐specific antigen (PSA) assay‐specific artificial neural networks (ANN) for discrimination between patients with prostate cancer (PCa) and no evidence of malignancy (NEM).

Reduced Serum Selenoprotein P Concentrations in German Prostate Cancer Patients

Selenium (Se) is essentially needed for the biosynthesis of selenoproteins. Low Se intake causes reduced selenoprotein biosynthesis and constitutes a risk factor for tumorigenesis. Accordingly, some Se supplementation trials have proven effective to reduce prostate cancer risk, especially in poorly supplied individuals. Because Se metabolism is controlled by selenoprotein P (SEPP), we have tested whether circulating SEPP concentrations correlate to prostate cancer stage and grade. A total of 190 men with prostate cancer (n = 90) and “no evidence of malignancy” (NEM; n = 100) histologically confirmed by prostate biopsy were retrospectively analyzed for established tumor markers and for their Se and SEPP status. Prostate specific antigen (PSA), free PSA, total Se, and SEPP concentrations were determined from serum samples and compared with clinicopathologic parameters. The diagnostic performance was analyzed with receiver operating characteristic curves. Median Se and SEPP concentrations differed significantly (P < 0.001) between the groups. Median serum Se concentrations in the 25th to 75th percentile were 95.9 μg/L (82-117.9) in NEM patients and 81.4 μg/L (67.9-98.4) in prostate cancer patients. Corresponding serum SEPP concentrations were 3.4 mg/L (1.9-5.6) in NEM and 2.9 mg/L (1.1-5.5) in prostate cancer patients. The area under the curve (AUC) of a marker combination with age, PSA, and percent free PSA (%fPSA) in combination with the SEPP concentration, yielded the highest diagnostic value (AUC 0.80) compared with the marker combination without SEPP (AUC 0.77) or %fPSA (AUC 0.76). We conclude that decreased SEPP concentration in serum might represent an additional valuable marker for prostate cancer diagnostics.(Cancer Epidemiol Biomarkers Prev 2009;18(9):2386–90)

Between-Method Differences in Prostate-Specific Antigen Assays Affect Prostate Cancer Risk Prediction by Nomograms

BACKGROUND To date, no published nomogram for prostate cancer (PCa) risk prediction has considered the between-method differences associated with estimating concentrations of prostate-specific antigen (PSA). METHODS Total PSA (tPSA) and free PSA were measured in 780 biopsy-referred men with 5 different assays. These data, together with other clinical parameters, were applied to 5 published nomograms that are used for PCa detection. Discrimination and calibration criteria were used to characterize the accuracy of the nomogram models under these conditions. RESULTS PCa was found in 455 men (58.3%), and 325 men had no evidence of malignancy. Median tPSA concentrations ranged from 5.5 μg/L to 7.04 μg/L, whereas the median percentage of free PSA ranged from 10.6% to 16.4%. Both the calibration and discrimination of the nomograms varied significantly across different types of PSA assays. Median PCa probabilities, which indicate PCa risk, ranged from 0.59 to 0.76 when different PSA assays were used within the same nomogram. On the other hand, various nomograms produced different PCa probabilities when the same PSA assay was used. Although the ROC curves had comparable areas under the ROC curve, considerable differences were observed among the 5 assays when the sensitivities and specificities at various PCa probability cutoffs were analyzed. CONCLUSIONS The accuracy of the PCa probabilities predicted according to different nomograms is limited by the lack of agreement between the different PSA assays. This difference between methods may lead to unacceptable variation in PCa risk prediction. A more cautious application of nomograms is recommended.

Improved prostate cancer detection with a human kallikrein 11 and percentage free PSA-based artificial neural network

Abstract Human kallikrein 11 (hK11) was evaluated in a percentage free PSA-based artificial neural network (ANN) to reduce unnecessary prostate biopsies. Serum samples from 357 patients with (n=132) and without (n=225) prostate cancer (PCa) were analyzed and ANN models were constructed and compared to all parameters. The discriminatory power of hK11 was lower than that of PSA, but receiver operator characteristic (ROC) analyses demonstrated significantly larger areas under the curves for the ANN compared to all other parameters. ANNs with hK11 may lead to a further reduction in unnecessary prostate biopsies, especially when analyzing patients with less than 15% free PSA.

The percentage of prostate-specific antigen (PSA) isoform [-2]proPSA and the Prostate Health Index improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared with total PSA and percentage free PSA in men aged ≤65 years.

To prospectively test the diagnostic accuracy of the percentage of prostate specific antigen (PSA) isoform [–2]proPSA (%p2PSA) and the Prostate Health Index (PHI), and to determine their role for discrimination between significant and insignificant prostate cancer at initial and repeat prostate biopsy in men aged ≤65 years.

Hormonal ‘minipuberty’ influences the somatic development of boys but not of girls up to the age of 6 years

Hormonal ‘minipuberty’ refers to a transient sex‐specific surge of LH, FSH, testosterone (T) and estradiol (E2) in the first few months of life. We hypothesized a potential long‐term effect of this hormonal surge on somatic parameters in the following years and therefore designed this longitudinal study.