scholarly journals Prospective Validation of Pentraxin-3 as a Novel Serum Biomarker to Predict the Risk of Prostate Cancer in Patients Scheduled for Prostate Biopsy

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1611
Author(s):  
Ugo Giovanni Falagario ◽  
Gian Maria Busetto ◽  
Giuseppe Stefano Netti ◽  
Francesca Sanguedolce ◽  
Oscar Selvaggio ◽  
...  
Keyword(s):  
Prostate Biopsy ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Digital Rectal Examination ◽  
Multivariable Logistic Regression Analysis ◽  
Gleason Grade ◽  
Pentraxin 3 ◽  
Net Benefit ◽  
Multivariable Models

Purpose: To test and internally validate serum Pentraxin-3 (PTX3) levels as a potential PCa biomarker to predict prostate biopsy (PBx) results. Materials and Methods: Serum PSA and serum PTX3 were prospectively assessed in patients scheduled for PBx at our Institution due to increased serum PSA levels or abnormal digital rectal examination. Uni- and multivariable logistic regression analysis, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA), were used to test the accuracy of serum PTX3 in predicting anyPCa and clinically significant PCa (csPCa) defined as Gleason Grade (GG) ≥ 2. Results: Among the 455 eligible patients, PCa was detected in 49% and csPCa in 25%. During univariate analysis, PTX3 outperformed other variables in predicting both anyPCa and csPCa. The addition of PTX3 to multivariable models based on standard clinical variables, significantly increased each model’s predictive accuracy for anyPCa (AUC from 0.73 to 0.82; p < 0.001) and csPCa (AUC from 0.79 to 0.83; p < 0.001). At DCA, PTX3, and PTX3, density showed higher net benefit than PSA and PSA density and increased the net benefit of multivariable models in deciding when to perform PBx. Conclusions: Serum PTX3 levels might be of clinical utility in predicting prostate biopsy results. Should our findings be confirmed, this novel reflex test could be used to reduce the number and burden of unnecessary prostate biopsies.

scholarly journals Association between HIV status and Positive Prostate Biopsy in a Study of U.S. Veterans

2009 ◽  
Vol 9 ◽  
pp. 102-108 ◽  
Author(s):  
Wayland Hsiao ◽  
Katrina Anastasia ◽  
John Hall ◽  
Michael Goodman ◽  
David Rimland ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Hiv Infection ◽  
Prostate Biopsy ◽  
Medical Center ◽  
Digital Rectal Examination ◽  
Hiv Positive ◽  
Gleason Grade ◽  
Hiv Status ◽  
Prostate Biopsies ◽  
Positive Biopsy

HIV infection is associated with increased incidence of malignancies, such as lymphomas and testicular cancers. We reviewed the relationship between HIV infection and prostate cancer in a contemporary series of prostate biopsy patients. The study is a retrospective analysis of consecutive prostate biopsies performed at a VA Medical Center. The indications for performing a prostate biopsy included an abnormal digital rectal examination and/or an elevated PSA. Patients were categorized according to their HIV status, biopsy results, and various demographic and clinical characteristics. Univariate and multivariate analyses compared distributions of HIV status, and various clinical and demographic characteristics. The adjusted measures of association between HIV status and positive biopsy were expressed as odds ratios (ORs) and corresponding 95% confidence intervals (CI). The likelihood of positive biopsy was significantly higher among 18 HIV-positive patients compared to patients with negative HIV tests (adjusted OR = 3.9; 95% CI: 1.3–11.5). In analyses restricted to prostate cancer patients, HIV-positive patients were not different from the remaining group with respect to their prostate cancer stage, PSA level, PSA velocity, PSA density, or Gleason grade. There is an association between HIV infection and prostate biopsy positive for carcinoma in a population referred for urologic workup. Further confirmation of this association by prospective studies may impact the current screening practices in HIV patients.

scholarly journals Development and Validation of a Nomogram for Predicting Postoperative Pulmonary Infection in Esophageal Cancer

2021 ◽  
Author(s):  
Shuang LI ◽  
Jingwen Su ◽  
Qiyu Sui ◽  
Gongchao Wang
Keyword(s):  
Esophageal Cancer ◽  
Pulmonary Infection ◽  
Predictive Accuracy ◽  
Individual Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Net Benefit ◽  
Individual Probability ◽  
Wide Range ◽  
The Individual ◽  
Development And Validation

Abstract Objective: The main aim of this study is to construct and validate a nomogram for estimating the risk of POI by investigating how perioperative features contribute to POI. Material and Methods: This cohort study enrolled 637 patients with esophageal cancer. Perioperative information on participants were collected to develop and validate a nomogram for predicting postoperative pulmonary infection in esophageal cancer. Predictive accuracy, discriminatory capability and clinical usefulness were evaluated by calibration curves, concordance index (C-index) and decision curve analysis (DCA). Results: Multivariable logistic regression analysis indicated that length of stay, albumin, intraoperative bleeding, and perioperative blood transfusion were independent predictors of POI. The nomogram for assessing individual risk of POI indicated good predictive accuracy in the primary cohort (C-index, 0.802) and validation cohort (C-index, 0.763). Good consistency between predicted risk and observed actual risk was presented as the calibration curve. The nomogram for estimating POI of esophageal cancer had superior net benefit with a wide range of threshold probabilities (4–81%). Conclusions: The present study provided a nomogram developed with perioperative features to assess the individual probability of infection may conducive to strengthen awareness of infection control and provide appropriate resource to manage patients at high-risk following esophagectomy.

scholarly journals External validation of prostate health index-based nomogram for predicting prostate cancer at extended biopsy

2020 ◽  
Vol 148 (5-6) ◽  
pp. 292-298
Author(s):  
Milorad Stojadinovic ◽  
Damnjan Pantic ◽  
Miroslav Stojadinovic
Keyword(s):  
Prostate Cancer ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
External Validation ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Validation Dataset ◽  
Health Index ◽  
Prostate Health Index ◽  
Prostate Health

Introduction/Objective. Prostate Health Index (PHI)-based nomograms were created by Lughezzani et al. (2012) and Zhu et al. (2015) for predicting prostate cancer (PCa) at extended biopsy. The aim of the study was to externally validate two nomograms in the Serbian population. Methods. This retrospective study comprised 71 patients irrespective of digital rectal examination (DRE) findings, with prostate-specific antigen level < 10 ng/ml, who had undergone prostate biopsies, and PHI testing. Data were collected in accordance with previous nomograms predictors. Independent predictors were identified by using logistic regression. The predictive accuracy was measured by the area under the receiver operating characteristic curve (AUC). The calibration belt was used to assess model calibration. The clinical utility was measured by using decision curve analysis (DCA). Results. There were numerous differences in underlying risk factors between validation dataset and previously available data. Analysis demonstrated that the DRE and PHI were independent predictors. AUCs for both nomograms, in patients with normal DRE had shown to have a good discriminatory ability (77.2?86.2%). In the entire population AUC of nomogram had exceptional discrimination (92.9%). Zhu et al. nomogram is associated with lower false positive predictions. The calibration belt for Zhu et al. nomogram was acceptable. Our DCA suggested that both nomograms are likely to be clinically useful. Conclusion. We performed external validation of two PHI-based nomograms predicting the presence of PCa in both the initial and the repeat biopsy setting. The PHI-based nomograms displayed adequate accuracy and justifies its use in Serbian patients.

scholarly journals The use of prostate specific antigen density to predict clinically significant prostate cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Igor Yusim ◽  
Muhammad Krenawi ◽  
Elad Mazor ◽  
Victor Novack ◽  
Nicola J. Mabjeesh
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Prostate Biopsy ◽  
Prostate Volume ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Prostate Adenocarcinoma ◽  
Prostate Specific Antigen Density ◽  
Clinically Significant

AbstractThe purpose of this study was to assess the predictive value of prostate specific antigen density (PSAD) for detection of clinically significant prostate cancer in men undergoing systematic transrectal ultrasound (TRUS)-guided prostate biopsy. We retrospectively analyzed data of men who underwent TRUS-guided prostate biopsy because of elevated PSA (≤ 20 ng/ml) or abnormal digital rectal examination. Receiver operating characteristic curve analysis to compare PSA and PSAD performance and chi-square automatic interaction detector methodologies were used to identify predictors of clinically significant cancer (Gleason score ≥ 7 or international society of urological pathology grade group ≥ 2). Nine-hundred and ninety-two consecutive men with a median age of 66 years (IQR 61–71) were included in the study. Median PSAD was 0.10 ng/ml2 (IQR 0.10–0.22). Prostate adenocarcinoma was diagnosed in 338 men (34%). Clinically significant prostate adenocarcinoma was diagnosed in 167 patients (50% of all cancers and 17% of the whole cohort). The AUC to predict clinically significant prostate cancer was 0.64 for PSA and 0.78 for PSAD (P < 0.001). The highest Youden's index for PSAD was at 0.20 ng/ml2 with 70% sensitivity and 79% specificity for the diagnosis of clinically significant cancer. Men with PSAD < 0.09 ng/ml2 had only 4% chance of having clinically significant disease. The detection rate of clinically significant prostate cancer in patients with PSAD between 0.09 and 0.19 ng/ml2 was significantly higher when prostate volume was less than 33 ml. In conclusion, PSAD was a better predictor than PSA alone of clinically significant prostate cancer in patients undergoing TRUS-guided biopsy. Patients with PSAD below 0.09 ng/ml2 were unlikely to harbor clinically significant prostate cancer. Combining PSAD in the gray zone (0.09–0.19) with prostate volume below 33 ml adds diagnostic value of clinically significant prostate cancer.

Nomograms based on the Tyrol screening data of 2,271 patients to predict prostate cancer biopsy positivity.

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 203-203
Author(s):  
P. Sooriakumaran ◽  
M. John ◽  
J. Bektic ◽  
G. Bartsch ◽  
M. Herman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Predictive Accuracy ◽  
Prostate Volume ◽  
External Validation ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Predictive Ability ◽  
Free Psa ◽  
Better Than

203 Background: There are no published nomograms that predict prostate cancer in a screened population. We describe three nomograms that predict for prostate cancer on biopsy derived from a large screening population. Methods: Patients from the Tyrol screening study of known age, total prostate-specific antigen (tPSA), digital rectal examination (DRE), prostate volume, and percent free PSA (%fPSA), and who underwent an initial prostate biopsy from January 1992 to June 2004, were included (n=2271). Multivariable logistic regression models were used to develop the biopsy positivity predictive nomograms: nomogram 1- age, DRE, tPSA; nomogram 2- age, DRE, tPSA, prostate volume; nomogram 3- age, DRE, tPSA, prostate volume, %fPSA. The predictive accuracy of the models was assessed in terms of discrimination and calibration. External validation of the nomograms was performed by comparison with a urologically referred population of patients who underwent prostate biopsy (n=599). Results: All three nomograms discriminated well between biopsy positive and biopsy negative patients for both the screening and urologically referred cohorts (nomogram 3 better than nomogram 2 better than nomogram 1). All three nomograms were well calibrated internally, but the nomograms under-predicted the probability of a positive biopsy in the urologically referred cohort. Conclusions: Our nomogram based on age, total PSA, and DRE has a good predictive ability to differentiate between screened patients that will show cancer on initial prostate biopsy and those that will not. Adding prostate volume and percent free PSA improves this predictive power further. All three nomograms under-predict prostate cancer in a urologically referred cohort. These simple nomograms may be of value in counseling screened men with raised PSA and/or abnormal DRE regarding the need for biopsy. No significant financial relationships to disclose.

Clinical analysis of the extracellular vesicle-fingerprint score blood test to refine the prediction of clinically significant prostate cancer and avoid prostate biopsy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 5530-5530 ◽  
Author(s):  
Adrian S. Fairey ◽  
Robert J Paproski ◽  
Desmond Pink ◽  
Deborah L Sosnowski ◽  
Catalina Vasquez ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Features ◽  
Prostate Biopsy ◽  
Clinical Care ◽  
Digital Rectal Examination ◽  
Extracellular Vesicle ◽  
Risk Scores ◽  
Gleason Grade ◽  
Prostate Biopsies ◽  
Clinically Significant

5530 Background: The accuracy of the extracellular vesicle-fingerprint score (EV-FPS) test to predict clinically significant prostate cancer (PCa; Gleason grade (GG) ≥ 3) from indolent disease (GG ≤ 2) and avoid unnecessary prostate biopsies was determined at the point of prostate biopsy decision. Methods: Clinical data, health information, and blood samples were collected from a prospective validation cohort of 415 men, without prior PCa diagnosis, referred to urology clinics for prostate biopsy or transurethral prostate surgery (June 2014-Dec 2016). The patient’s EV-FPS risk score was calculated by combining machine learning model-analyzed microflow cytometry data from EV biomarkers with logistic regression-analyzed patient-centric clinical features. The plasma-derived EV biomarkers were prostate-specific membrane antigen, polysialic acid and ghrelin-growth hormone receptor. The patient clinical features were; age, ethnicity, PCa family history, PSA levels, abnormal digital rectal examination (DRE) and prior negative prostate biopsy. Together, the biomarkers and clinical features provided specificity for clinically significant PCa. Results: The EV-FPS test identified clinically significant PCa patients with high accuracy (0.81 area under curve) at 95% sensitivity and 97% negative predictive value. Using a 7.85% probability cut-off after test validation; 95% of the patients with GG ≥ 3 would have been found before biopsy, 35% biopsies would have been avoided and diagnosis of GG ≥ 3 PCa would have been missed in only 5% of the cohort. Conclusions: This minimally invasive EV-FPS test accurately predicted clinically significant PCa in men with high EV-FPS risk scores, high PSA level and/or abnormal DRE. Therefore, men with low EV-FPS risk scores could potentially avoid unnecessary prostate biopsies. Clinical care cut-offs to calculate the number of biopsies that could have been avoided, and the percentage of GG ≥ 1 to GG ≥ 3 PCa that could have had a delayed diagnosis. [Table: see text]

scholarly journals A nomogram for predicting postoperative pulmonary infection in esophageal cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shuang Li ◽  
Jingwen Su ◽  
Qiyu Sui ◽  
Gongchao Wang
Keyword(s):  
Esophageal Cancer ◽  
Pulmonary Infection ◽  
Predictive Accuracy ◽  
Patient Specific ◽  
Individual Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Net Benefit ◽  
Curative Surgery ◽  
Individual Probability ◽  
Wide Range

Abstract Background Although postoperative pulmonary infection (POI) commonly occurs in patients with esophageal cancer after curative surgery, a patient-specific predictive model is still lacking. The main aim of this study is to construct and validate a nomogram for estimating the risk of POI by investigating how perioperative features contribute to POI. Methods This cohort study enrolled 637 patients with esophageal cancer. Perioperative information on participants was collected to develop and validate a nomogram for predicting postoperative pulmonary infection in esophageal cancer. Predictive accuracy, discriminatory capability, and clinical usefulness were evaluated by calibration curves, concordance index (C-index), and decision curve analysis (DCA). Results Multivariable logistic regression analysis indicated that length of stay, albumin, intraoperative bleeding, and perioperative blood transfusion were independent predictors of POI. The nomogram for assessing individual risk of POI indicated good predictive accuracy in the primary cohort (C-index, 0.802) and validation cohort (C-index, 0.763). Good consistency between predicted risk and observed actual risk was presented as the calibration curve. The nomogram for estimating POI of esophageal cancer had superior net benefit with a wide range of threshold probabilities (4–81%). Conclusions The present study provided a nomogram developed with perioperative features to assess the individual probability of infection may conducive to strengthen awareness of infection control and provide appropriate resources to manage patients at high risk following esophagectomy.

scholarly journals Observational study comparing the accuracy/variability between the ERSPC and the PCPT risk calculators for the prediction of significant prostate cancer in patients with PSA <10 ng/mL

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e031032 ◽  
Author(s):  
Enrique Gomez Gomez ◽  
Juan José Salamanca Bustos ◽  
Julia Carrasco Valiente ◽  
Jose Luis Fernandez Rueda ◽  
Ana Blanca ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Observational Study ◽  
Prostate Biopsy ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
University Hospital ◽  
Discriminative Ability ◽  
Randomised Study

IntroductionRisk calculators (RCs) are easy-to-use tools considering available clinical variables that could help to select those patients with risk of prostate cancer (PCa) who should undergo a prostate biopsy.ObjectiveTo perform a comparison for the prediction of significant PCa (SigPCa) between the European Randomised Study of Screening for PCa (ERSPC) and the PCa Prevention Trial (PCPT) RCs in patients with prostate-specific antigen (PSA) between 3 and 10 ng/mL through an evaluation of the accuracy/variability between two consecutive PSA values.SettingAn observational study in a major university hospital in the south of Spain.Methods and participantsAn observational study was performed in patients who underwent a prostate biopsy. SigPCa probabilities were calculated with the two PSA measures using ERSPC3/4+digital rectal examination and PCPT v2+free PSA RCs. The prediction of SigPCa was determined by the area under the receiver operating characteristic curve (AUC). Calibration, discrimination and decision curve analysis were studied. The variability between both RCs’ agreement was compared using Cohen’s kappa coefficient.Results510 patients were analysed (87 diagnosed with SigPCa). The median PSA values were 5.3 and 5 ng/mL for PSA1 and PSA2, respectively. Both RCs overestimated the risk in the case of high-risk probabilities. Discriminative ability for SigPCa was similar between models with an AUC=0.73 (0.68–0.79) for ERSPC-RC versus 0.73 (0.67–0.79) for PCPT-RC. ERSPC-RC showed less variability than PCPT-RC, with a constant agreement (k=0.7–0.8) for usual range of clinical decision-making. Remarkably, a higher number of biopsies would be avoided using the ERSPC-RC, but more SigPCa would be missed along all the risk probabilities.ConclusionsBoth RCs performed similar in the prediction of SigPCa. However, ERSPC-RC seems to be more stable for intraindividual PSA variations.

scholarly journals Decision tree analysis for prostate cancer prediction

2019 ◽  
Vol 147 (1-2) ◽  
pp. 52-58
Author(s):  
Miroslav Stojadinovic ◽  
Milorad Stojadinovic ◽  
Damjan Pantic
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Prostate Biopsy ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Cart Model

Introduction/Objective. The use of serum prostate-specific antigen (PSA) test has dramatically increased the number of men undergoing prostate biopsy. However, the best possible strategies for selecting appropriate patients for prostate biopsy have yet to be defined. The aim of the study was to develop a classification and regression tree (CART) model that could be used to identify patients with significant prostate cancer (PCa) on prostate biopsy in patients referred due to abnormal PSA, digital rectal examination (DRE) findings, or both, regardless of the PSA level. Methods. The data on clinicopathological characteristics regarding prebiopsy assessment collected from patients who had undergone ultrasound-guided prostate biopsies included the following: age, PSA, DRE, volume of the prostate, and PSA density (PSAD). The CART analysis was carried out using all predictors identified by univariate logistic regression analysis. Different aspects of predictive performance and clinical utility risk prediction model were assessed. Results. In this retrospective study, significant PCa was detected in 92 (41.6%) out of 221 patients. The CART model had three splits based on PSAD, as the most decisive variable, prostate volume, DRE, and PSA. Our model resulted in an 83.3% area under the receiver operating characteristic curve. Decision curve analysis showed that the regression tree provided net benefit for relevant threshold probabilities compared with the logistic regression model, PSAD, and the strategy of biopsying all patients. Conclusion. The model helps to reduce unnecessary biopsies without missing significant PCa.

scholarly journals Decision Tree Analysis for Prostate Cancer Prediction in Patients with Serum PSA 10 ng/ml or Less

2020 ◽  
Vol 21 (1) ◽  
pp. 43-50
Author(s):  
Damjan N Pantic ◽  
Milorad M Stojadinovic ◽  
Miroslav M Stojadinovic
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Minimal Risk ◽  
Cart Analysis ◽  
Psa Testing

AbstractSerum prostate-specific antigen (PSA) testing increases the number of persons who undergo prostate biopsy. However, the best possible strategy for selecting patients for prostate biopsy has not yet been defined. The aim of this study was to develop a classification and regression tree (CART) decision model that can be used to predict significant prostate cancer (PCa) in the course of prostate biopsy for patients with serum PSA levels of 10 ng/ml or less.The following clinicopathological characteristics of patients who had undergone ultrasound-guided transrectal prostate biopsy were collected: age, PSA, digital rectal examination, volume of the prostate, and PSA density (PSAD). CART analysis was carried out by using all predictors. Different aspects of the predictive performances of the prediction model were assessed.In this retrospective study, significant PCa values were detected in 26 (26.8%) of a total of 97 patients. The CART model had three branching levels based on PSAD as the most decisive variable and age. The model sensitivity was 73.1%, the specificity was 80.3% and the accuracy was 78.3%. Our model showed an area under the receiver operating characteristic curve of 82.6%. The model was well calibrated.In conclusion, CART analysis determined that PSAD was the key parameter for the identification of patients with a minimal risk for positive biopsies. The model showed a good discrimination capacity that surpassed individual predictors. However, before recommending its use in clinical practice, an evaluation of a larger and more complete database is necessary for the prediction of significant PCa.

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