Tissue Polypeptide-Specific Antigen (Tps) Immunoassay in the Diagnosis and Clinical Staging of Prostatic Carcinoma. Comparison with Prostate-Specific Antigen (Psa)

1997 ◽  
Vol 12 (1) ◽  
pp. 27-34 ◽  
Author(s):  
L. Ceriani ◽  
L. Giovanella ◽  
M. Salvadore ◽  
A.V. Bono ◽  
G. Roncari

This experimental study investigated the potential role of Tissue Polypeptide-Specific Antigen (TPS) in comparison with Prostate-Specific Antigen (PSA) in the diagnosis and the clinical and pathological staging of prostate cancer. Serum TPS and PSA levels were determined in 128 patients (pts) with benign prostatic hypertrophy (BPH; Group 1) and in 92 pts with prostate cancer (Group 2). TPS was also measured in a control group of 100 healthy subjects. Normal cutoff values of 85 U/l for TPS and 4 ng/ml for PSA were determined on the basis of ROC curve analysis. The sensitivity, specificity and accuracy in the diagnosis of prostate cancer were 49%, 95% and 76% for TPS, and 84%, 90% and 87% for PSA. The combination of the two markers provided a higher accuracy (88%), improving the sensitivity of PSA, since 47% of patients with normal PSA had pathological levels of TPS. TPS showed an increase in sensitivity from low to higher stages of disease and, in patients with skeletal involvement, from small to larger numbers of bone metastases (Kruskal Wallis p < 0.0001). Nevertheless, TPS serum levels are not useful in the clinical staging of prostate cancer as they have a poorer performance than PSA. TPS was ineffective (ROC curve area=0.68) in predicting extraprostatic disease and demonstrated a reduced ability (area = 0.78) to identify skeletal involvement. Moreover, the combination of the two markers did not significantly improve the performance of PSA alone. The serum concentration of TPS in patients with localized tumors was not related to the degree of tumor cell differentiation evaluated by the Gleason score. Conclusion Our preliminary experience suggests that TPS in association with PSA may be useful at the time of diagnosis of prostate cancer. However, these preliminary data have to be confirmed by larger clinical trials and the role of this association in the clinical setting needs to be analyzed with an adequate evaluation of the cost-effectiveness ratio.

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.


2015 ◽  
Vol 33 (1) ◽  
pp. 16.e1-16.e7 ◽  
Author(s):  
Heikki Seikkula ◽  
Kari T. Syvänen ◽  
Samu Kurki ◽  
Tuomas Mirtti ◽  
Pekka Taimen ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document