scholarly journals CUT-OFF POINT OF PSA AND PSA DENSITY IN PROSTATE CANCER SUSPECTED PATIENTS

2016 ◽  
Vol 23 (1) ◽  
Author(s):  
Daniel Simanjuntak ◽  
Ferry Safriadi
Keyword(s):  
Prostate Cancer ◽  
General Hospital ◽  
Racial Differences ◽  
Prostate Biopsy ◽  
Operating Characteristic ◽  
National Level ◽  
Specific Antigen ◽  
High Rate ◽  
Psa Density ◽  
The Difference

Objective: In Hasan Sadikin General Hospital Bandung prostate biopsy is recommended for patients with prostate specific antigen (PSA) levels above 10 ng/mL or PSA density (PSAD) above 0.15 if PSA in between 4–10 ng/mL. However, there were other factors beside of prostate cancer (PCa) that could influence PSA value, such as racial differences and prostatitis. Thus, the optimal cut-off point for PSA is still debatable in national level. This research aimed to determine PSA and PSAD cut-off point for patient suspected of PCa in Hasan Sadikin Hospital Bandung. Material & Methods: A total of 502 patients underwent prostate biopsy for suspicion PCa from 2010–2014. The cut-off point of PSA and PSAD is generated from receiver operating characteristic (ROC) curve. Results: The cut-off point for PSA was 14.6 ng/mL (sensitivity 81.4%, specificity 29.8%). The cut-off point for PSAD was 0.23 (sensitivity 81.4%, specificity 34.8%). Positive predictive value (PPV) for this new cut-off point of PSA and PSAD were 41.1% and 37.6% respectively. Conclusion: PSA and PSAD cut-off point for patients in our center was 14.6 ng/mL and 0.23 respectively. This value is relatively higher compared to the Indonesian consensus. The difference might occur due to high rate of urinary tract infection in patients attending Hasan Sadikin General Hospital Bandung.

Defining the optimal PSA range for the maximal predictive efficacy of PSA density to detect prostate cancer on biopsy: Results from a multi-institutional and prospective contemporary cohort.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 70-70
Author(s):  
Samarpit Rai ◽  
Nicola Pavan ◽  
Nachiketh Soodana-Prakash ◽  
Bruno Nahar ◽  
Yan Dong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Psa Density ◽  
Detect Prostate Cancer ◽  
Operating Characteristic Curve ◽  
The Difference ◽  
Negative Prostate Biopsy

70 Background: PSA density (PSAD) is an important predictor of prostate cancer (PCa). We assessed whether the predictive accuracy of PSAD varied based on the range of PSA or whether the patient had a previous negative prostate biopsy (PB). Methods: We assessed a prospective cohort of men who were referred for a PB due to suspicion of PCa at 26 different sites across USA. The area under the receiver operating characteristic curve (AUC) was used to assess the added predictive accuracy of PSAD versus PSA across 3 different PSA ranges ( < 4, 4 – 10, > 10 ng/mL) and in men with or without a prior negative PB for the detection of any and significant (Gleason ≥ 7) PCa. Results: Of the 1,290 men, 585 (45%) and 284 (22%) had any and significant PCa, respectively. PSAD was significantly more predictive than PSA for detecting any PCa in the PSA ranges of 4 – 10 (AUC 0.70 vs 0.53, P < 0.00001) and > 10 (AUC 0.84 vs 0.65, P < 0.00001) ng/mL. Similarly, for significant PCa, PSAD was more predictive than PSA in the PSA ranges of 4 – 10 (AUC 0.72 vs 0.57, P < 0.00001) and > 10 (AUC 0.82 vs 0.68, P = 0.0001) ng/mL. Furthermore, PSAD was significantly more predictive than PSA in detecting PCa in men that had a prior negative PB (AUC 0.69 vs 0.56, P = 0.0001 for any PCa and AUC 0.81 vs 0.70, P = 0.0042 for significant PCa), and those that didn’t (AUC 0.72 vs 0.67, P = 0.0001 for any PCa and AUC 0.77 vs 0.73, P = 0.0026 for significant PCa). However the difference between the AUC of PSAD and PSA (ΔAUC) was a lot more pronounced in men that had a prior negative PB (ΔAUC = 0.13 for any PCa and ΔAUC = 0.11 for significant PCa) as opposed to those that didn’t (ΔAUC = 0.05 for any PCa and ΔAUC = 0.04 for significant PCa), suggesting that PSAD is a much better predictor than PSA alone in men who have undergone a previous PB. Conclusions: As PSA increases, the predictive accuracy of PSAD over PSA appears to improve for the detection of any PCa and significant PCa. Additionally, PSAD has a more pronounced predictive value over PSA in detecting any and significant PCa in men who have undergone a prior negativePB. We support the use of PSAD testing to avoid unnecessary biopsies in men who have elevated PSA secondary to an enlarged prostate.

scholarly journals Integrated predictive model for prostatic cancer using clinical, laboratory and ultrasound data

2016 ◽  
Vol 43 (6) ◽  
pp. 430-437
Author(s):  
GUSTAVO DAVID LUDWIG ◽  
HENRIQUE PERES ROCHA ◽  
LÚCIO JOSÉ BOTELHO ◽  
MAIARA BRUSCO FREITAS
Keyword(s):  
Prostate Cancer ◽  
Predictive Model ◽  
Prostate Biopsy ◽  
Clinical Laboratory ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Roc Curves ◽  
Rectal Examination ◽  
Psa Density

ABSTRACT Objective: to develop a predictive model to estimate the probability of prostate cancer prior to biopsy. Methods: from September 2009 to January 2014, 445 men underwent prostate biopsy in a radiology service. We excluded from the study patients with diseases that could compromise the data analysis, who had undergone prostatic resection or used 5-alpha-reductase inhibitors. Thus, we selected 412 patients. Variables included in the model were age, prostate specific antigen (PSA), digital rectal examination, prostate volume and abnormal sonographic findings. We constructed Receiver Operating Characteristic (ROC) curves and calculated the areas under the curve, as well as the model's Positive Predictive Value (PPV) . Results: of the 412 men, 155 (37.62%) had prostate cancer (PC). The mean age was 63.8 years and the median PSA was 7.22ng/ml. In addition, 21.6% and 20.6% of patients had abnormalities on digital rectal examination and image suggestive of cancer by ultrasound, respectively. The median prostate volume and PSA density were 45.15cm3 and 0.15ng/ml/cm3, respectively. Univariate and multivariate analyses showed that only five studied risk factors are predictors of PC in the study (p<0.05). The PSA density was excluded from the model (p=0.314). The area under the ROC curve for PC prediction was 0.86. The PPV was 48.08% for 95%sensitivity and 52.37% for 90% sensitivity. Conclusion: the results indicate that clinical, laboratory and ultrasound data, besides easily obtained, can better stratify the risk of patients undergoing prostate biopsy.

scholarly journals epiCaPture: A Urine DNA Methylation Test for Early Detection of Aggressive Prostate Cancer

2019 ◽  
pp. 1-18 ◽  
Author(s):  
Eve O’Reilly ◽  
Alexandra V. Tuzova ◽  
Anna L. Walsh ◽  
Niamh M. Russell ◽  
Odharnaith O’Brien ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
High Risk ◽  
Early Detection ◽  
Receiver Operating Characteristic ◽  
Prostate Biopsy ◽  
Operating Characteristic ◽  
Specific Antigen ◽  
High Grade ◽  
Cancer Of The Prostate

Purpose Liquid biopsies that noninvasively detect molecular correlates of aggressive prostate cancer (PCa) could be used to triage patients, reducing the burdens of unnecessary invasive prostate biopsy and enabling early detection of high-risk disease. DNA hypermethylation is among the earliest and most frequent aberrations in PCa. We investigated the accuracy of a six-gene DNA methylation panel (Epigenetic Cancer of the Prostate Test in Urine [epiCaPture]) at detecting PCa, high-grade (Gleason score greater than or equal to 8) and high-risk (D’Amico and Cancer of the Prostate Risk Assessment] PCa from urine. Patients and Methods Prognostic utility of epiCaPture genes was first validated in two independent prostate tissue cohorts. epiCaPture was assessed in a multicenter prospective study of 463 men undergoing prostate biopsy. epiCaPture was performed by quantitative methylation-specific polymerase chain reaction in DNA isolated from prebiopsy urine sediments and evaluated by receiver operating characteristic and decision curves (clinical benefit). The epiCaPture score was developed and validated on a two thirds training set to one third test set. Results Higher methylation of epiCaPture genes was significantly associated with increasing aggressiveness in PCa tissues. In urine, area under the receiver operating characteristic curve was 0.64, 0.86, and 0.83 for detecting PCa, high-grade PCa, and high-risk PCa, respectively. Decision curves revealed a net benefit across relevant threshold probabilities. Independent analysis of two epiCaPture genes in the same clinical cohort provided analytical validation. Parallel epiCaPture analysis in urine and matched biopsy cores showed added value of a liquid biopsy. Conclusion epiCaPture is a urine DNA methylation test for high-risk PCa. Its tumor specificity out-performs that of prostate-specific antigen (greater than 3 ng/mL). Used as an adjunct to prostate-specific antigen, epiCaPture could aid patient stratification to determine need for biopsy.

scholarly journals Prostate-Specific Antigen (PSA) and PSA Density: Racial Differences in Men Without Prostate Cancer

1997 ◽  
Vol 89 (2) ◽  
pp. 134-138 ◽  
Author(s):  
R. J. Henderson ◽  
J. A. Eastham ◽  
C. Daniel J. ◽  
T. Whatley ◽  
J. Mata ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Racial Differences ◽  
Specific Antigen ◽  
Psa Density

scholarly journals Parameters predicting postoperative unilateral disease in patients with unilateral prostate cancer in diagnostic biopsy: a rationale for selecting hemiablative focal therapy candidates

2013 ◽  
Vol 7 (1-2) ◽  
pp. 82 ◽  
Author(s):  
Stavros Sfoungaristos ◽  
Petros Perimenis
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Prostate Volume ◽  
Univariate Analysis ◽  
Specific Antigen ◽  
Focal Therapy ◽  
Biopsy Material ◽  
Surgical Specimen ◽  
Psa Density ◽  
Unilateral Disease

Background: Focal hemiablative therapy for prostate cancer is a new treatment alternative. Unilateral and unifocal disease are its main limitations. The aim of this study was to identify the epidemiological, clinical and pathological parameters that may predict unilateral disease in patients diagnosed with prostate cancer.Methods: We performed a retrospective analysis of patients at our institution between January 2005 and January 2011. Only patients with unilateral disease in prostate biopsy were part of the study. The analysis included age, preoperative prostate-specific antigen (PSA) and its density, prostate volume, biopsy first and second Gleason pattern and Gleason summary, number of biopsy cores, percentage of cancer in biopsy material and the presence of high grade prostatic intraepithelial neoplasia. Their role as potential predictors was evaluated by univariate and multivariate analysis.Results: A total of 161 patients had unilateral disease after prostate biopsy. A significant correlation was found between prostate volume, PSA density and percentage of cancer in biopsy material and the presence of unilateral disease in the surgical specimen. These are the same factors significant in the univariate analysis. The results of the multivariate analysis demonstrated that PSA density (p = 0.015) and percentage of cancer in biopsy material (p = 0.028) are the most significant predictors.Interpretation: Our results demonstrate that PSA density and the percentage of cancer in biopsy cores are significant predictorsfor prostate cancer unilaterality and should be considered for theselection of hemiablative focal therapy candidates.

scholarly journals Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 335
Author(s):  
Francesco Gentile ◽  
Matteo Ferro ◽  
Bartolomeo Della Ventura ◽  
Evelina La Civita ◽  
Antonietta Liotti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Specific Antigen ◽  
Molecular Forms ◽  
Psa Density ◽  
Prostate Specific Antigen Test ◽  
Free Psa ◽  
Artificial Neuronal Networks ◽  
Total Psa ◽  
Deep Learning Model

After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets.

scholarly journals Global Trends of Latent Prostate Cancer in Autopsy Studies

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 359
Author(s):  
Takahiro Kimura ◽  
Shun Sato ◽  
Hiroyuki Takahashi ◽  
Shin Egawa
Keyword(s):  
Prostate Cancer ◽  
Racial Differences ◽  
Specific Antigen ◽  
Tumor Location ◽  
Cancer Registration ◽  
Asian Countries ◽  
Global Trends ◽  
Psa Screening ◽  
Asian Men ◽  
Definition Of

The incidence of prostate cancer (PC) has been increasing in Asian countries, where it was previously low. Although the adoption of a Westernized lifestyle is a possible explanation, the incidence is statistically biased due to the increase in prostate-specific antigen (PSA) screening and the accuracy of national cancer registration systems. Studies on latent PC provide less biased information. This review included studies evaluating latent PC in several countries after excluding studies using random or single-section evaluations and those that did not mention section thickness. The findings showed that latent PC prevalence has been stable since 1950 in Western countries, but has increased over time in Asian countries. Latent PC in Asian men has increased in both prevalence and number of high-grade cases. Racial differences between Caucasian and Asian men may explain the tumor location of latent PC. In conclusion, the recent increase in latent PC in Asian men is consistent with an increase in clinical PC. Evidence suggests that this increase is caused not only by the increase in PSA screening, but also by the adoption of a more Westernized lifestyle. Autopsy findings suggest the need to reconsider the definition of clinically insignificant PC.

scholarly journals Is PSA density of the peripheral zone as a useful predictor for prostate cancer in patients with gray zone PSA levels?

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jaegeun Lee ◽  
Seung Woo Yang ◽  
Long Jin ◽  
Chung Lyul Lee ◽  
Ji Yong Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Prostate Biopsy ◽  
Peripheral Zone ◽  
Transitional Zone ◽  
Gray Zone ◽  
Prostate Cancer Diagnosis ◽  
Psa Density ◽  
Diagnosis Group ◽  
Predictive Rate

Abstract Background Serum prostate-specific antigen (PSA) is widely used in screening tests for prostate cancer. As the low specificity of PSA results in unnecessary and invasive prostate biopsies, we evaluated the clinical significance of various PSAs and PSA density (PSAD) related to peripheral zones in patients with gray zone PSA level (4–10 ng/mL). Methods A total of 1300 patients underwent transrectal ultrasonography-guided prostate biopsy from 2014 to 2019. Among them, 545 patients in the gray zone were divided into the prostate cancer diagnosis group and the non-prostate cancer diagnosis group, and PSA, relative extra transitional zone PSA (RETzPSA), estimated post holmium laser enucleation of the prostate PSA (EPHPSA), PSAD, peripheral zone PSA density (PZPSAD) and extra-transitional zone density (ETzD) were compared and analyzed using receiver-operating characteristics (ROC) analysis after 1:1 matching using propensity score. Results Area under the ROC curve values of PSA, EPHPSA, RETzPSA, PSA density, ETzD, and PZPSAD were 0.553 (95% CI: 0.495–0.610), 0.611 (95% CI: 0.554–0.666), 0.673 (95% CI: 0.617–0.725), 0.745 (95% CI: 0.693–0.793), 0.731 (95% CI: 0.677–0.780) and 0.677 (95% CI: 0.611–0.719), respectively. PSAD had 67.11% sensitivity, 71.71% specificity, and 70.34% positive predictive rate at 0.18 ng/mL/cc. ETzD had 69.08% sensitivity, 64.47% specificity, and 66.04% positive predictive rate at 0.04 ng/mL/cc. When the cut-off value of PSAD was increased to 0.18 ng/mL/cc, the best results were obtained with an odds ratio of 5.171 (95% CI: 3.171–8.432), followed by ETzD with 4.054 (95% CI: 2.513–6.540). Conclusions These results suggested that volume-adjusted parameters (ETzD and PSAD) might be more sensitive and accurate than various PSA in gray zone patients who required prostate biopsy to reduce unnecessary biopsy.

scholarly journals PSA Density Help to Identify Patients With Elevated PSA Due to Prostate Cancer Rather Than Intraprostatic Inflammation: A Prospective Single Center Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Salvatore M. Bruno ◽  
Ugo G. Falagario ◽  
Nicola d’Altilia ◽  
Marco Recchia ◽  
Vito Mancini ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Prostate Volume ◽  
Multivariable Analysis ◽  
Binary Logistic Regression Analysis ◽  
Single Center ◽  
Negative Biopsy ◽  
Psa Density ◽  
Clinically Significant ◽  
Prostatic Inflammation

The association between PSA density, prostate cancer (PCa) and BPH is well established. The aim of the present study was to establish whether PSA density can be used as a reliable parameter to predict csPCa and to determine its optimal cutoff to exclude increased PSA levels due to intraprostatic inflammation. This is a large prospective single-center, observational study evaluating the role of PSA density in the discrimination between intraprostatic inflammation and clinically significant PCa (csPCa). Patients with PSA ≥ 4 ng/ml and/or positive digito-rectal examination (DRE) and scheduled for prostate biopsy were enrolled. Prostatic inflammation (PI) was assessed and graded using the Irani Scores. Multivariable binary logistic regression analysis was used to assess if PSA density was associated with clinically significant PCa (csPCa) rather than prostatic inflammation. A total of 1988 patients met the inclusion criteria. Any PCa and csPCa rates were 47% and 24% respectively. In the group without csPCa, patients with prostatic inflammation had a higher PSA (6.0 vs 5.0 ng/ml; p=0.0003), higher prostate volume (58 vs 52 cc; p&lt;0.0001), were more likely to have a previous negative biopsy (29% vs 21%; p=0.0005) and a negative DRE (70% vs 65%; p=0.023) but no difference in PSA density (0.1 vs 0.11; p=0.2). Conversely in the group with csPCa, patients with prostatic inflammation had a higher prostate volume (43 vs 40 cc; p=0.007) but no difference in the other clinical parameters. At multivariable analysis adjusting for age, biopsy history, DRE and prostate volume, PSA density emerged as a strong predictor of csPCA but was not associated with prostatic inflammation. The optimal cutoffs of PSA density to diagnose csPCa and rule out the presence of prostatic inflammation in patients with an elevated PSA (&gt;4 ng/ml) were 0.10 ng/ml2 in biopsy naïve patients and 0.15 ng/ml2 in patients with a previous negative biopsy. PSA density rather than PSA, should be used to evaluate patients at risk of prostate cancer who may need additional testing or prostate biopsy. This readily available parameter can potentially identify men who do not have PCa but have an elevated PSA secondary to benign conditions.

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