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

Stephan, Carsten; Jung, Klaus; Soosaipillai, Antoninus; Yousef, George M.; Cammann, Henning; Meyer, Hellmuth-Alexander; Xu, Chuanliang; Diamandis, Eleftherios P.

doi:10.1111/j.1464-410x.2005.05677.x

Cited by 28 publications

(18 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Phi, %p2PSA, and p2PSA were added to an artificial neural network (ANN) and to binary logistic regression models with the classic variables age, tPSA, %fPSA, prostate volume, and DRE status (14,23 ) to evaluate their ability to improve specificity. The models were constructed with the MATLAB Neural Network Toolbox (Mathworks).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2013

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this reason, we included only age, prostate volume and result of DRE into base model 2 (BM-2). Additionally, we added PHI and %p2PSA to an artificial neural network (ANN) containing both models [31,32]. We constructed the ANN-models with the MATLAB V R2010b Neural Network Toolbox (http:// www.mathworks.se).…”

Section: Methodsmentioning

confidence: 99%

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

Boegemann¹,

Stephan

Cammann

et al. 2015

BJU International

Self Cite

View full text Add to dashboard Cite

ObjectivesTo 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. Patients and MethodsThe diagnostic performance of %p2PSA and PHI were evaluated in a multicentre study. In all, 769 men aged ≤65 years scheduled for initial or repeat prostate biopsy were recruited in four sites based on a total PSA (t-PSA) level of 1.6-8.0 ng/mL World Health Organization (WHO) calibrated (2-10 ng/mL Hybritechcalibrated). Serum samples were measured for the concentration of t-PSA, free PSA (f-PSA) and p2PSA with Beckman Coulter immunoassays on Access-2 or DxI800 instruments. PHI was calculated as (p2PSA/f-PSA 9 √t-PSA). Uni-and multivariable logistic regression models and an artificial neural network (ANN) were complemented by decision curve analysis (DCA). ResultsIn univariate analysis %p2PSA and PHI were the best predictors of prostate cancer detection in all patients In multivariate analysis PHI we added to a base model of age, prostate volume, digital rectal examination, t-PSA and %f-PSA. PHI was strongest in predicting prostate cancer in all patients, at initial and repeat biopsy and for significant prostate cancer (AUC 0.73, 0.68, 0.78 and 0.72, respectively). In DCA for all patients the ANN showed the broadest threshold probability and best net benefit. PHI as single parameter and the base model + PHI were equivalent with threshold probability and net benefit nearing those of the ANN. For significant cancers the ANN was the strongest parameter in DCA. ConclusionThe present multicentre study showed that %p2PSA and PHI have a superior diagnostic performance for detecting prostate cancer in the PSA range of 1.6-8.0 ng/mL compared with t-PSA and %f-PSA at initial and repeat biopsy and for predicting significant prostate cancer in men aged ≤65 years. They are equally superior for counselling patients before biopsy.

show abstract

“…To test the ability of Phi, PCA3, and T2 to improve specificity in detecting PCa at biopsy, these variables were used together in a multivariate artificial neural network (ANN) and binary logistic regression models with age, PSA, %fPSA, prostate volume, and DRE status as described previously in detail (30 ). These models comprised only 1, 2, or all 3 new biomarkers without partial or full addition of the 5 traditional parameters PSA, %fPSA, prostate volume, age, and DRE status.…”

Section: U-test Kruskal Wallis Test Mcnemar Test Andmentioning

confidence: 99%

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

Stephan

Jung

Semjonow³

et al. 2013

Clinical Chemistry

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 28 publications

References 51 publications

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

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

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

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

Contact Info

Product

Resources

About