Evaluation of a Multivariate Prostate-Specific Antigen and Percentage of Free Prostate-Specific Antigen Logistic Regression Model in the Diagnosis of Prostate Cancer

Tumor Biology ◽  
1999 ◽  
Vol 20 (6) ◽  
pp. 312-318 ◽  
Author(s):  
Xavier Filella ◽  
Juan Alcover ◽  
Llorenç Quintó ◽  
Rafael Molina ◽  
Xavier Bosch-Capblanch ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Logistic Regression Model ◽  
Specific Antigen

scholarly journals Prospects for the application of a new logistic regression model for the differential diagnosis of prostate cancer

2021 ◽  
Vol 99 (2) ◽  
pp. 98-102
Author(s):  
S. V. Ponkratov ◽  
I. B. Oleksjuk ◽  
K. L. Kozlov ◽  
A. V. Oleksjuk
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Logistic Regression Model ◽  
Specific Antigen ◽  
Transrectal Ultrasound ◽  
Modern Medicine ◽  
Ultrasound Investigation

The differential diagnostic of prostate cancer is the actual task of modern medicine. The existing methods lack accuracy and specificity. It’s the reason of hyper- or hypo-diagnostic of this disease. We developed and tested the new logistic regression model for diagnostic of prostate cancer in men of various age. The model includes age, the volume of prostate, concentration of prostate specific antigen (PCA), 2-pro-PCA in the blood, the presence of the concretion revealed during digital rectal investigation of prostate and hypoechogenic area in transrectal ultrasound investigation. The model was tested in 114 patients. It has shown the higher accuracy and specificity of the new regression model in comparison to other methods of differential diagnostic of prostate cancer.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

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.

scholarly journals The Effect of Different Levels of Prostate-Specific Antigen and Prostate Imaging Report and Data System Scores on Logistic Regression Model to Predict Clinically Significant Prostate Cancer—A Single Center Retrospective Study

2020 ◽  
Author(s):  
Loudong Zhang ◽  
Hua Zhu ◽  
Donghua Gu ◽  
Xiaodong Pan ◽  
bing zheng
Keyword(s):  
Prostate Cancer ◽  
Retrospective Study ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Data System ◽  
Diagnostic Efficiency ◽  
Prostate Imaging ◽  
Clinically Significant ◽  
Different Levels

Abstract Background: At present, there are various clinical regression models for predicting prostate cancer. But what about the diagnostic effectiveness of these models in different parameter ranges, and are the models applicable to everyone? This study aimed to study the influence of different levels of prostate-specific antigen (PSA) and Prostate Imaging Report and Data System version 2 (PI-RADS v2) scores on the regression model to predict clinically significant prostate cancer (csPCa).Methods: This retrospective study screened 251 patients from our hospital, who were divided into different groups. The regression model was established for each group to predict csPCa, and the effects of PSA and PI-RADS scores on each model were analyzed through the diagnostic effects of the model.Results: In patients with lower PSA scores, although the model was less sensitive than PSA, the AUC of the model was much greater. With the rise of PSA, the sensitivity of the model surpassed that of PSA, while the specificity became the opposite, and the AUC gap also gradually decreased. In the group with low PI-RADS score, the sensitivity and specificity of PI-RADS were lower than the model, and the gap was larger. Although the gap between the two gradually decreased with the increase of PI-RADS, the diagnostic efficiency of the model was still slightly larger than that of pure PI-RADS.Conclusion: As the PSA and PI-RADS v2 scores increase, the diagnostic advantages of the regression model will gradually decrease. However, for patients with low levels of PSA and PI-RADS scores,the regression model is less affected by PSA and PI-RADS, and can better utilize its clinical diagnostic advantages.

scholarly journals Estimation of Prostate Cancer Probability by Logistic Regression: Free and Total Prostate-specific Antigen, Digital Rectal Examination, and Heredity Are Significant Variables

1999 ◽  
Vol 45 (7) ◽  
pp. 987-994 ◽  
Author(s):  
Arja Virtanen ◽  
Mehran Gomari ◽  
Ries Kranse ◽  
Ulf-Håkan Stenman
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Clinical Findings ◽  
Explanatory Variables ◽  
Rectal Examination ◽  
Screening Population ◽  
Total Psa

Abstract Background: Despite low specificity, serum prostate-specific antigen (PSA) is widely used in screening for prostate cancer. Specificity can be improved by measuring free and total PSA and by combining these results with clinical findings. Methods such as neural networks and logistic regression are alternatives to multistep algorithms for clinical use of the combined findings. Methods: We compared multilayer perceptron (MLP) and logistic regression (LR) analysis for predicting prostate cancer in a screening population of 974 men, ages 55–66 years. The study sample comprised men with PSA values >3 μg/L. Explanatory variables considered were age, free and total PSA and their ratio, digital rectal examination (DRE), transrectal ultrasonography, and a family history of prostate cancer. Results: When at least 90% sensitivity in the training sets was required, the mean sensitivity and specificity obtained were 87% and 41% with LR and 85% and 26% with MLP, respectively. The cancer specificity of an LR model comprising the proportion of free to total PSA, DRE, and heredity as explanatory variables was significantly better than that of total PSA and the proportion of free to total PSA (P <0.01, McNemar test). The proportion of free to total PSA, DRE, and heredity were used to prepare cancer probability curves. Conclusion: The probability calculated by logistic regression provides better diagnostic accuracy for prostate cancer than the presently used multistep algorithms for estimation of the need to perform biopsy.

scholarly journals Seroprevalence of Herpes Simplex Virus Type 2 and Human Herpesvirus Type 8 among Prostate Cancer Patients Compared to Cancer-Free Men

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
Solmaz Ohadian Moghadam ◽  
Kamyar Mansori ◽  
Mohammad Reza Nowroozi ◽  
Davoud Afshar ◽  
Ali Nowroozi
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Human Herpesvirus ◽  
Multivariate Logistic Regression Model ◽  
Statistical Association ◽  
Multivariate Logistic Regression ◽  
Simplex Virus ◽  
Significant Statistical Association

Background: As one of the most prevalent cancers in men, prostate cancer is a condition with multiple causes. Viral infections have been identified as one of the major sources of elevated incidence of prostate cancer. Objectives: The purpose of this research was to assess the association of the risk of prostate cancer and its aggressiveness with seropositivity of herpes simplex virus 2 (HSV-2) and/or human herpesvirus 8 (HHV-8). Methods: Totally, 103 men with prostate cancer as cases and 81 healthy individuals as controls were included in this case-control analysis and provided a serum sample. The specific IgG antibodies against HSV-2 and HHV-8 were screened by enzyme-linked immunosorbent assay (ELISA). To determine the association between HSV-2, HHV-8, prostate-specific antigen (PSA) level, and demographic variables with incidence of prostate cancer, univariate and multivariate logistic regression models were applied. Results: The results of the univariate logistic regression model showed a statistically significant association between HSV-2 and HHV-8 seropositivity, PSA level, age, and smoking with prostate cancer incidence (P ≤ 0.20). The multivariate logistic regression model results after adjusting for the potential confounding variables showed a significant statistical association between the mean of PSA level [adjusted odds ratio (OR): 3.44; 95% CI: 2.15 - 5.51; P < 0.001) and incidence of prostate cancer. Moreover, the results of univariate and multivariate logistic regression model showed a significant statistical association between age [adjusted OR: 0.88; 95% confidence interval (CI): 0.81 - 0.95; P = 0.001] and HSV-2 and also significant statistical association was found between PSA (adjusted OR: 1.02; 95% CI: 1.005 - 1.03; P = 0.006) and HHV-8. Conclusions: Although the seroprevalence of HSV-2 and HHV-8 was higher in patients with prostate cancer than in the control group, it cannot be concluded that there is a significant association between the seropositivity of these viruses and prostate cancer incidence. However, the findings showed a significant statistical association between age and seropositivity of HSV-2 and also a significant statistical association between PSA levels and seropositivity of HHV-8.

SU-F-BRD-01: A Logistic Regression Model to Predict Objective Function Weights in Prostate Cancer IMRT

2014 ◽  
Vol 41 (6Part23) ◽  
pp. 395-395
Author(s):  
J Boutilier ◽  
T Chan ◽  
T Craig ◽  
T Lee ◽  
M Sharpe
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Objective Function ◽  
Logistic Regression Model
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