scholarly journals The predictive value of PSA in diagnosis of prostate cancer in non screened population

2005 ◽  
Vol 52 (4) ◽  
pp. 81-87 ◽  
Author(s):  
V. Vukotic ◽  
S. Cerovic ◽  
M. Kozomara ◽  
M. Lazic
Keyword(s):  
Prostate Cancer ◽  
Predictive Value ◽  
Prostate Cancer Screening ◽  
Predictive Values ◽  
Psa Density ◽  
Free Psa ◽  
Wide Range ◽  
Valuable Marker ◽  
Total Psa ◽  
Sensitivity Specificity

INTRODUCION : PSA is the most important tumor marker in all solid tumor, indispensable in the management of prostate cancer. Screening for prostate cancer is still not recommended, although performed in many countries, which introduced questions about the usefulness of PSA in detection of prostate cancer. The PSA threshold has also been changed, the value of PSA derivatives revised. Whether such changes are applicable in non screened population is questionable. Aim of this study was to evaluate the predictive value of PSA, free/ total PSA and PSA density in our non screened population. Patients and methods: TRUS guided prostate biopsy was performed in 579 patients. The number of cores was 6-12. Mean age of the patients was 67.5 years (30-90). PSA was ranging from 0.41 to 2250 ( mean 38.6ng/ml, median: 11.95, SD 140,45). Digitorectal examination was considered positive in 351 patients. Free PSA was measured in 352 patients with the index ranging from 0.02 to 0.88 ( mean free/total PSA: 0.14, median:0.13, ). The volume of the prostate was measured in all patients according the prostate ellipsoid model, and PSA density calculated according to the formula PSA/PV. Patients were stratified in 6 groups according to PSA value ( I: PSA ng/ml, II: PSA 2.5-4, III: PSA 4-10, IV: PSA 10-20, V: PSA:20 to 50, Group 6: PSA 50 ). RESULTS: Non homogenicity of the patients can be seen through the wide range of PSA which was from 0.4 to 2025). Prostate cancer was diagnosed in 233 pts (40.2%). As expected, the probability of detecting cancer was raised with PSA (p), and was extremely rare in pts with PSA below 4 ng/ml. PSA, free/total PSA, volume of the prostate and PSA density were significantly different according to the presence of cancer. Most of our patients had PSA between 4 and 20 ng/ml. Predictive value of PSA was 20.6% for pts with PSA from 4 to 10 and 32.7% for those with PSA from 10 to 20 ng/ml. Sensitivity, specificity, positive and negative predictive values for different cut off?s of PSA (4, 10 and 20) was performed. The best results were obtained for PSA cut off of 10 ng/ml. In the group of patient with PSA, PSA density more reliable than free/total PSA index. CONCLUSION: PSA is still valuable marker for detection of prostate cancer in our non screened population. According to our results PSA threshold should not be lowered below 4 ng/ml. PSA density is a reliable PSA derivative, free/total PSA index having less importance in pts with PSA below 20 ng/ml.

Long-Term Prediction of Prostate Cancer Up to 25 Years Before Diagnosis of Prostate Cancer Using Prostate Kallikreins Measured at Age 44 to 50 Years

2007 ◽  
Vol 25 (4) ◽  
pp. 431-436 ◽  
Author(s):  
Hans Lilja ◽  
David Ulmert ◽  
Thomas Björk ◽  
Charlotte Becker ◽  
Angel M. Serio ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Predictive Value ◽  
Prostate Cancer Screening ◽  
Conditional Logistic Regression ◽  
Specific Antigen ◽  
Screening Programs ◽  
Free Psa ◽  
Swedish Cancer Registry ◽  
Total Psa

PurposeWe examined whether prostate-specific antigen (PSA) forms and human kallikrein 2 (hK2) measured at age 44 to 50 years predict long-term risk of incident prostate cancer.MethodsFrom 1974 to 1986, 21,277 men age ≤ 50 years in Malmö, Sweden, enrolled onto a cardiovascular study (74% participation). The rate of PSA screening in this population is low. According to the Swedish Cancer Registry, 498 were later diagnosed with prostate cancer. We measured hK2, free PSA, and total PSA (tPSA) in archived blood plasma from 462 participants later diagnosed with prostate cancer and from 1,222 matched controls. Conditional logistic regression was used to test for association of prostate cancer with hK2 and PSA forms measured at baseline.ResultsMedian delay between venipuncture and prostate cancer diagnosis was 18 years. hK2 and all PSA forms were strongly associated with prostate cancer (all P < .0005). None of the 90 anthropometric, lifestyle, biochemical, and medical history variables measured at baseline was importantly predictive. A tPSA increase of 1 ng/mL was associated with an increase in odds of cancer of 3.69 (95% CI, 2.99 to 4.56); addition of other PSA forms or hK2 did not add to the predictive value of tPSA. tPSA remained predictive for men diagnosed ≥ 20 years after venipuncture, and the predictive value remained unchanged in an analysis restricted to palpable disease.ConclusionA single PSA test at age 44 to 50 years predicts subsequent clinically diagnosed prostate cancer. This raises the possibility of risk stratification for prostate cancer screening programs.

Novel Artificial Neural Network for Early Detection of Prostate Cancer

2002 ◽  
Vol 20 (4) ◽  
pp. 921-929 ◽  
Author(s):  
Bob Djavan ◽  
Mesut Remzi ◽  
Alexandre Zlotta ◽  
Christian Seitz ◽  
Peter Snow ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Roc Curve ◽  
Predictive Accuracy ◽  
Specific Antigen ◽  
Psa Density ◽  
Free Psa ◽  
Ann Models ◽  
Artificial Neural ◽  
Total Psa

PURPOSE: Two artificial neural networks (ANN) for the early detection of prostate cancer in men with total prostate-specific antigen (PSA) levels from 2.5 to 4 ng/mL and from 4 to 10 ng/mL were prospectively developed. The predictive accuracy of the ANN was compared with that obtained by use of conventional statistical analysis of standard PSA parameters. PATIENTS AND METHODS: Consecutive men with a serum total PSA level between 4 and 10 ng/mL (n = 974) and between 2.5 and 4 ng/mL (n = 272) were analyzed. A separate ANN model was developed for each group of patients. Analyses were performed to determine the presence of prostate cancer. RESULTS: The area under the receiver operator characteristic (ROC) curve (AUC) was 87.6% and 91.3% for the 2.5 to 4 ng/mL and 4 to 10 ng/mL ANN models, respectively. For the latter model, the AUC generated by the ANN was significantly higher than that produced by the single variables of total PSA, percentage of free PSA, PSA density of the transition zone (TZ), and TZ volume (P < .01), but not significantly higher compared with multivariate analysis. For the 2.5 to 4 ng/mL model, the AUC of the ANN ROC curve was significantly higher than the AUCs for percentage of free PSA (P = .0239), PSA-TZ (P = .0204), and PSA density and total prostate volume (P < .01 for both). CONCLUSION: The predictive accuracy of the ANN was superior to that of conventional PSA parameters. ANN models might change the way patients referred for early prostate cancer detection are counseled regarding the need for prostate biopsy.

scholarly journals The Study for Accuracy Assessment of Free PSA Density for Prostate Cancer Detection in Male Patients with High PSA

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Noppakulsatit P
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Predictive Value ◽  
Screening Tool ◽  
Prostate Gland ◽  
Digital Rectal Examination ◽  
Serum Free ◽  
Prostate Cancer Detection ◽  
Psa Density ◽  
Free Psa

Background: Prostate cancer detection is currently based on serum PSA with a digital rectal examination which is neither specific nor sensitive, which caused many unnecessary prostate gland biopsies that are highly expensive and can result in unwanted complications. Serum free PSA increases with a larger prostate gland,yet declines with a gland that contains cancer cells, thus prompting the hypothesis that calculating the ratio of serum free PSA against prostate gland volume provides the so-called “free PSA density” which can be utilized to improve prostate cancer detection. Methods: Male participants were deemed eligible if they are at risk of prostate cancer with a PSA level of 4-10 ng/dL and aged between 50-75 years. Serum PSA and serum free PSA were obtained concurrently, followed by transrectal ultrasonography for prostate volume calculation and biopsy of the gland. Also reported are the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic(ROC) with area under the ROC curve(AUC) of serum PSA, %free PSA ratio, free PSA density and PSA density. The AUC of the variables were compared with the free PSA density and reported. Results: The free PSA density cut point values which provided the highest accuracy was 0.025mg/mL/cc, which had 61.5% sensitivity and 67.25% specificity. The ROC results indicate that %free PSA ratio had the best AUC at 0.86. Free PSA density and PSA density have AUC at 0.65 and 0.61, respectively. Meanwhile, serum PSA had the worst AUC of 0.54. The researchers also calculated different AUCs of other variables to free PSA density. Finally, the AUC of free PSA density was significantly better than the reference standard tool serum PSA (p=0.022). Conclusions: Prostate cancer is an emerging cancer among elderly men. Frequent use of serum PSA as a screening tool allows earlier diagnosis of this cancer but with the expensive of unnecessary further investigations. Most novel and promising tools are too expensive to be used as a generalized screening tool. From this study, free PSA density may be a reasonable alternative tool for detection of prostate cancer.

Predictive value of PSA density and free/total PSA ratio in detecting prostate cancer in patients with a total PSA

2016 ◽  
Vol 15 (11) ◽  
pp. e1357
Author(s):  
M. Stancioiu ◽  
J. Aurelian ◽  
A. Grasu ◽  
V. Ionescu ◽  
G. Opris ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Predictive Value ◽  
Psa Density ◽  
Total Psa

Can PSA density and free-to-total PSA ratio improve our ability to predict prostate cancer on biopsy? Results from a prospective, multi-institutional, and contemporary cohort.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 57-57
Author(s):  
Samarpit Rai ◽  
Nachiketh Soodana-Prakash ◽  
Nicola Pavan ◽  
Bruno Nahar ◽  
Amil Patel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Low Grade ◽  
Psa Density ◽  
Discriminative Value ◽  
Total Psa ◽  
Final Cohort

57 Background: Several studies have reported an increased value of PSA density (PSAD) and free−to−total PSA ratio (f/t PSA) over PSA alone in predicting prostate cancer (PCa). Despite this, they remain underutilized. This study analyzed a cohort of men referred for prostate biopsy (PB) to determine if PSAD and f/t PSA enhanced the prediction of any PCa and/or significant PCa (Gleason score ≥ 7) compared to PSA. Methods: 1,370 prospectively enrolled patients were referred for a PB across 26 urological centers. A phlebotomy was performed immediately prior to PB for PSA and f/t PSA measurement. PSAD was calculated using prostate volume obtained during the trans−rectal ultrasound (TRUS) guided PB. The area under the receiver operating characteristic curve (AUC) was used to assess the added discriminative value of PSAD and f/t PSA when added to a base model consisting of PSA, age, prior biopsy status, and DRE for the prediction of any and significant PCa. Results: Of the 1,290 men in the final cohort, 301 (23%) and 284 (22%) men were diagnosed with low−grade (Gleason score = 6) and significant PCa respectively. The median PSAD values in men with no PCa, low−grade PCa, and significant PCa were 0.09, 0.11, and 0.17 ng/mL/cc, respectively (P < 0.0001). The median f/t PSA in men with no PCa, low−grade PCa, and significant PCa was 0.21, 0.17, and 0.12 respectively (P < 0.0001). The AUC for a model incorporating PSAD showed superior predictive value compared to the base model for diagnosing any PCa (AUC 0.76 versus 0.70, P < 0.0001) and significant PCa (AUC 0.82 versus 0.77, P < 0.0001). Similarly, a model with f/t PSA showed superior predictive value compared to the base model for diagnosing any PCa (AUC 0.73 vs 0.70, P < 0.0001) and significant PCa (AUC 0.82 versus 0.77, P < 0.0001). While PSAD showed superior predictive value over f/t PSA for predicting any PCa (AUC 0.76 versus 0.73, P = 0.0062), there was no difference in their discrimination of significant PCa. Conclusions: PSAD and f/t PSA add substantial predictive power to the diagnostic armamentarium for any and significant PCa. Their calculation may reduce the number of unnecessary biopsies being performed for PCa detection.

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 Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 563
Author(s):  
Chen Shenhar ◽  
Hadassa Degani ◽  
Yaara Ber ◽  
Jack Baniel ◽  
Shlomit Tamir ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Tensor Imaging ◽  
Predictive Value ◽  
Breast Imaging ◽  
Normal Tissue ◽  
Diffusion Tensor ◽  
Peripheral Zone ◽  
Predictive Values ◽  
Directional Diffusion ◽  
Central Gland

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ1) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

scholarly journals Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 973
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Giovanni Cappello ◽  
Valeria Maria Doronzio ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computer Aided Diagnosis ◽  
Multiparametric Mri ◽  
Performance Study ◽  
Predictive Values ◽  
Cad System ◽  
Computer Aided ◽  
Clinically Significant ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

scholarly journals Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value

Medicina ◽  
2021 ◽  
Vol 57 (5) ◽  
pp. 503
Author(s):  
Thomas F. Monaghan ◽  
Syed N. Rahman ◽  
Christina W. Agudelo ◽  
Alan J. Wein ◽  
Jason M. Lazar ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Medical Literature ◽  
Disease Prevalence ◽  
Positive Test Result ◽  
Predictive Values ◽  
Sensitivity Specificity ◽  
Test Result ◽  
Test Probability

Sensitivity, which denotes the proportion of subjects correctly given a positive assignment out of all subjects who are actually positive for the outcome, indicates how well a test can classify subjects who truly have the outcome of interest. Specificity, which denotes the proportion of subjects correctly given a negative assignment out of all subjects who are actually negative for the outcome, indicates how well a test can classify subjects who truly do not have the outcome of interest. Positive predictive value reflects the proportion of subjects with a positive test result who truly have the outcome of interest. Negative predictive value reflects the proportion of subjects with a negative test result who truly do not have the outcome of interest. Sensitivity and specificity are inversely related, wherein one increases as the other decreases, but are generally considered stable for a given test, whereas positive and negative predictive values do inherently vary with pre-test probability (e.g., changes in population disease prevalence). This article will further detail the concepts of sensitivity, specificity, and predictive values using a recent real-world example from the medical literature.

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