Significant Predictors Of Prostate Cancer Research Articles

Patient-related characteristics predict prostate cancers in men with PI-RADS 4-5 to further optimize the diagnostic performance of MRI.

To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4-5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI. The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated. A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694-0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844-0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%. The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4-5.

Prebiopsy bpMRI and hematological parameter-based risk scoring model for predicting outcomes in biopsy-naive men with PSA 4–20 ng/mL

Excessive prostate biopsy is a common problem for clinicians. Although some hematological and bi-parametric magnetic resonance imaging (bpMRI) parameters might help increase the rate of positive prostate biopsies, there is a lack of studies on whether their combination can further improve clinical detection efficiency. We retrospectively enrolled 394 patients with PSA levels of 4–20 ng/mL who underwent prebiopsy bpMRI during 2010–2021. Based on bpMRI and hematological indicators, six models and a nomogram were constructed to predict the outcomes of biopsy. Furthermore, we constructed and evaluated a risk scoring model based on the nomogram. Age, prostate-specific antigen (PSA) density (PSAD), systemic immune-inflammation index, cystatin C level, and the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 score were significant predictors of prostate cancer (PCa) on multivariable logistic regression analyses (P < 0.05) and the five parameters were used to construct the XYFY nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram was 0.916. Based on the nomogram, a risk scoring model (XYFY risk model) was constructed and then we divided the patients into low-(XYFY score: < 95), medium-(XYFY score: 95–150), and, high-risk (XYFY score: > 150) groups. The predictive values for diagnosis of PCa and clinically-significant PCa among the three risk groups were 3.0%(6/201), 41.8%(51/122), 91.5%(65/71); 0.5%(1/201), 19.7%(24/122), 60.6%(43/71), respectively. In conclusion, in this study, we used hematological and bpMRI parameters to establish and internally validate a XYFY risk scoring model for predicting the biopsy outcomes for patients with PSA levels of 4–20 ng/mL and this risk model would support clinical decision-making and reduce excessive biopsies.

Role of the elastography strain ratio using transrectal ultrasonography in the diagnosis of prostate cancer and clinically significant prostate cancer

This study investigated the efficacy of the elastography strain ratio (ESR) as a predictor of prostate cancer (PCa) in targeted prostate biopsy. In total, 257 patients who underwent magnetic resonance imaging-targeted biopsy were enrolled. Before biopsy, we placed regions of interest (zone A and B) in the lesion and levator ani. The ESR was measured as zone A/zone B. Multivariate analyses were performed to predict PCa and clinically significant PCa. There were 206 (71.5%) positive cancer lesions. No difference in digit rectal examination findings was found between patients with and without PCa. For predicting clinically significant PCa, an ESR ≥ 6.8 was significantly higher in the PCa (+) group than in the PCa (−) group (p < 0.001). The area under the receiver operating characteristic curve (AUC) for the conventional variables (model 1) plus the ESR was 0.845, which was significantly higher than that for model 1 (p = 0.001). In prostate imaging reporting and data system score 3 lesions, an ESR ≥ 4.6 was a significant predictor of PCa (p = 0.002). The AUC in model 1 plus the ESR was 0.856, which was significantly higher than that in model 1 alone (p = 0.017). The ESR is useful for predicting clinically significant PCa.

Transrectal ultrasound examination of prostate cancer guided by fusion imaging of multiparametric MRI and TRUS: avoiding unnecessary mpMRI-guided targeted biopsy.

The purpose of this study was to explore transrectal ultrasound (TRUS) findings of prostate cancer (PCa) guided by multiparametric magnetic resonance imaging (mpMRI) and to improve the Prostate Imaging Reporting and Data System (PI-RADS) system for avoiding unnecessary mpMRI-guided targeted biopsy (TB). From January 2018 to October 2019, fusion mpMRI and TRUS-guided biopsies were performed in 162 consecutive patients. The study included 188 suspicious lesions on mpMRI in 156 patients, all of whom underwent mpMRI-TRUS fusion imaging-guided TB and 12-core transperineal systematic biopsy (SB). Univariate analyses were performed to investigate the relationship between TRUS features and PCa. Then, logistic regression analysis with generalized estimating equations was performed to determine the independent predictors of PCa and obtain the fitted probability of PCa. The detection rates of PCa based on TB alone, SB alone, and combined SB and TB were 55.9% (105 of 188), 52.6% (82 of 156), and 62.8% (98 of 156), respectively. The significant predictors of PCa on TRUS were hypoechogenicity (odds ratio [OR]: 9.595, P = 0.002), taller-than-wide shape (OR: 3.539, P = 0.022), asymmetric vascular structures (OR: 3.728, P = 0.031), close proximity to capsule (OR: 3.473, P = 0.040), and irregular margins (OR: 3.843, P = 0.041). We propose subgrouping PI-RADS score 3 into categories 3a, 3b, 3c, and 3d based on different numbers of TRUS predictors, as the creation of PI-RADS 3a (no suspicious ultrasound features) could avoid 16.7% of mpMRI-guided TBs. Risk stratification of PCa with mpMRI-TRUS fusion imaging-directed ultrasound features could avoid unnecessary mpMRI-TBs.

Usefulness of grayscale values of hypoechoic lesions matched with target lesions observed on magnetic resonance imaging for the prediction of clinically significant prostate cancer

BackgroundTo analyze grayscale values for hypoechoic lesions matched with target lesions evaluated using prebiopsy magnetic resonance imaging (MRI) according to the Prostate Imaging-Reporting and Data System (PI-RADS).MethodsWe collected data on 420 target lesions in patients who underwent MRI/transrectal ultrasound fusion-targeted biopsies between January 2017 and September 2020. Images of hypoechoic lesions that matched the target lesions on MRI were stored in a picture archiving and communication system, and their grayscale values were estimated using the red/green/blue scoring method through an embedded function. We analyzed imaging data using grayscale values.ResultsOf the 420 lesions, 261 (62.1%) were prostate cancer lesions. There was no difference in the median grayscale values between benign and prostate cancer lesions. However, grayscale ranges (41.8–98.5 and 42.6–91.8) were significant predictors of prostate cancer and clinically significant prostate cancer (csPC) in multivariable logistic regression analyses. Area under the curve for detecting csPC using grayscale values along with conventional variables (age, prostate-specific antigen levels, prostate volume, previous prostate biopsy results, and PI-RADS scores) was 0.839, which was significantly higher than that for detecting csPC using only conventional variables (0.828; P = 0.036). Subgroup analysis revealed a significant difference for PI-RADS 3 lesions between grayscale values for benign and cancerous lesions (74.5 vs. 58.8, P = 0.008). Grayscale values were the only significant predictive factor (odds ratio = 4.46, P = 0.005) for csPC.ConclusionsDistribution of grayscale values according to PI-RAD 3 scores was potentially useful, and the grayscale range (42.6–91.8) was a potential predictor for csPC diagnosis.

A tactile resonance sensor for prostate cancer detection – evaluation on human prostate tissue

Prostate cancer surgery risks erectile problems and incontinence for the patient. An instrument for guiding surgeons to avoid nerve bundle damage and ensure complete cancer removal is desirable. We present a tactile resonance sensor made of PZT ceramics, mounted in a 3D motorized translation stage for scanning and measuring tissue stiffness for detecting cancer in human prostate. The sensor may be used during surgery for guidance, scanning the prostate surface for the presence of cancer, indicating migration of cancer cells into surrounding tissue. Ten fresh prostates, obtained from patients undergoing prostate cancer surgery, were cut into 0.5 cm thick slices. Each slice was measured for tissue stiffness at about 25 different sites and compared to histology for validation cancer prediction by stiffness. The statistical analysis was based on a total of 148 sites with non-cancer and 40 sites with cancer. Using a generalized linear mixed model (GLMM), the stiffness data predicted cancer with an area under the curve of 0.74, after correcting for overfitting using bootstrap validation. Mean prostate stiffness on the logarithmic scale (p = 0.015) and standardized Z-scores (p = 0.025) were both significant predictors of cancer. This study concludes that stiffness measured by the tactile resonance sensor is a significant predictor of prostate cancer with potential for future development towards a clinical instrument for surgical guidance.

Prostate cancer risk prediction using a polygenic risk score

Hereditary factors have a strong influence on prostate cancer (PC) risk and poorer outcomes, thus stratification by genetic factors addresses a critical need for targeted PC screening and risk-adapted follow-up. In this Finnish population-based retrospective study 2283 clinically diagnosed and 455 screen-detected patients from the Finnish Randomised Study of Screening for Prostate Cancer (FinRSPC), 2400 healthy individuals have been involved. Individual genetic risk through establishment of a polygenic risk score based on 55 PC risk SNPs identified through the Finnish subset of the Collaborative Oncological Gene-Environment Study was assessed. Men with PC had significantly higher median polygenic risk score compared to the controls (6.59 vs. 3.83, P < 0.0001). The polygenic risk score above the control median was a significant predictor of PC (OR 2.13, 95% CI 1.90–2.39). The polygenic risk score predicted the risk of PC with an AUC of 0.618 (95% CI 0.60–0.63). Men in the highest polygenic risk score quartile were 2.8—fold (95% CI 2.4–3.30) more likely to develop PC compared with men in the lowest quartile. In the FinRSPC cohort, a significantly higher percentage of men had a PSA level of ≥ 4 ng/mL in polygenic risk score quartile four compared to quartile one (18.7% vs 8.3%, P < 0.00001). Adding the PRS to a PSA-only model contributed additional information in predicting PC in the FinRSPC model. Results strongly suggest that use of the polygenic risk score would facilitate the identification of men at increased risk for PC.

Low reduction of prostate volume is a significant predictor of prostate cancer at subsequent biopsy in patients with dutasteride: A retrospective study.

Appropriate decision of prostate biopsy in men with 5α‐reductase inhibitor (5AR inhibitor) is still unclear to avoid unnecessary biopsy. We retrospectively investigated patients with initial PSA 4.0 ng/ml or more and underwent subsequent prostate biopsy following dutasteride treatment. From September 2009 to August 2018, 399 cases of benign prostate hyperplasia (BPH) were treated with dutasteride in our department. Of the total, 36 cases with elevated pre‐treatment PSA (4.0 ng/ml or more) and underwent subsequent prostate biopsy were included into this study. We evaluated PSA kinetics and changing prostate volumes (PV), and detection of prostate cancer. Overall, average PSA reduced by half at 6 months from dosing. Pre‐treatment biopsy was performed in 17 of 36 cases, and all were diagnosed as having no malignancy. After treatment, prostate cancer was detected in 15 cases by subsequent biopsy. Fourteen of 15 cases were clinically significant cancer (Gleason score 7 or more). Logistic regression analysis detected a nominal association between prostate cancer detection and three variants, PSAD, PV reduction (1–Before/After PV) and abnormal MRI findings. In addition to abnormal MRI findings and pre‐treatment of high PSAD, the case with low reduction of PV after treatment should consider performing prostate biopsy.

Using clinical parameters to predict prostate cancer and reduce the unnecessary biopsy among patients with PSA in the gray zone

The gold standard for prostate cancer (PCa) diagnosis is prostate biopsy. However, it remines controversial as an invasive mean for patients with PSA levels in the gray zone (4–10 ng/mL). This study aimed to develop strategy to reduce the unnecessary prostate biopsy. We retrospectively identified 235 patients with serum total PSA testing in the gray zone before prostate biopsy between 2014 and 2018. Age, PSA derivates, prostate volume and multiparametric magnetic imaging (mpMRI) examination were assessed as predictors for PCa and clinically significant PCa with Gleason score ≥ 7 (CSPCa). Univariate analysis showed that prostate volume, PSAD, and mpMRI examination were significant predictors of PCa and CSPCa (P < 0.05). The differences of diagnostic accuracy between mpMRI examination (AUC = 0.69) and other clinical parameters in diagnostic accuracy for PCa were not statistically significant. However, mpMRI examination (AUC = 0.79) outperformed prostate volume and PSAD in diagnosis of CSPCa. The multivariate models (AUC = 0.79 and 0.84 for PCa and CSPCa) performed significantly better than mpMRI examination for detection of PCa (P = 0.003) and CSPCa (P = 0.036) among patients with PSA level in the gray zone. At the same level of sensitivity as the mpMRI examination to diagnose PCa, applying the multivariate models could reduce the number of biopsies by 5% compared with mpMRI examination. Overall, our results supported the view that the multivariate model could reduce unnecessary biopsies without compromising the ability to diagnose PCa and CSPCa. Further prospective validation is required.

Histopathology, pharmacotherapy, and predictors of prostatic malignancy in elderly male patients with raised prostate-specific antigen levels - A prospective study.

Background:Prostate cancer is the second most common cancer among adult men in the world, and the diagnosis requires biopsy. Prostate-specific antigen (PSA) test along with digital rectal examination (DRE) increases the detection rate of prostate cancer than DRE alone. The objective of this study was to correlate serum PSA level with histopathological diagnosis, identify the predictors of malignancy, and describe the pharmacotherapy of patients with serum PSA levels >4 ng/ml.Materials and Methods:This was a hospital-based observational study done among patients who presented with lower urinary tract symptoms and PSA levels >4 ng/ml who were planned to undergo prostatic biopsy. DRE followed by transrectal ultrasound (TRUS) assessment and guided sextant (6-core) prostatic biopsy was performed.Results:One hundred and four patients were screened and 87 were included. Nineteen patients were diagnosed with malignancy, and among them, eight had bone metastasis. Spearman's correlation coefficient between PSA and malignancy was 0.449 (P ≤ 0.001). Multivariate analysis suggested that the factors (adjusted odds ratio; 95% confidence interval; P value) such as increasing age (1.127; 1.013, 1.253; 0.027), nodular prostate (22.668; 4.655, 110.377; P < 0.001), and PSA (1.034; 1.004, 1.064; 0.024) were significant predictors of prostate cancer. All patients with benign prostatic hyperplasia were advised a combination therapy with 5-alpha reductase inhibitor and selective alpha-1 receptor antagonist while those with malignancy were prescribed androgen deprivation therapy with antiosteoporosis therapy.Conclusion:In elderly patients with raised PSA levels or suspicious DRE findings, TRUS-guided prostate is recommended to rule out malignancy and plan appropriate management.

Combining Prostate-Specific Antigen Parameters With Prostate Imaging Reporting and Data System Score Version 2.0 to Improve Its Diagnostic Accuracy.

BackgroundAny non-invasive test that can predict the absence of prostate cancer (PCa) or absence of clinically significant PCa (CSPCa) is necessary, as it can reduce the number of unnecessary biopsies in patients with gray zone prostate-specific antigen (PSA, 4 - 10 ng/mL). This study evaluated the diagnostic performance of free PSA% and PSA density (PSAD), and Prostate Imaging Reporting and Data System (PIRADS) score (version 2.0) alone and combined in predicting CSPCa in patients with PSA between 4 and 10 ng/mL.MethodsThis prospective study included a total of 104 consecutive patients with lower urinary tract symptoms (LUTS) and serum PSA between 4 and 10 ng/mL, with or without abnormal digital rectal examination (DRE) findings or any hypoechoic lesion on ultrasound sonography of prostate and without prior transrectal ultrasound (TRUS) biopsy of prostate. PIRADS score was calculated using multi-parametric magnetic resonance imaging (mp-MRI) before TRUS biopsy of prostate. Relationships among PIRADS score, PSAD, free PSA% and presence of CSPCa in TRUS biopsy were statistically analyzed.ResultsIn patients with CSPCa, significantly higher median age (P = 0.001), PSA level (P < 0.001), PSAD (P < 0.001) and significantly lower prostate volume (P < 0.001) and free PSA% were observed as compared to patients with non-CSPCa. Significantly higher proportion of patients with CSPCa showed PIRADS positive test compared to those with non-CSPCa (86.4% vs. 53.3%, P < 0.001). Cut-off values for PSAD and free PSA% were 0.12 ng/mL2 and 25%, respectively. Age, PSAD and free PSA% were significant predictors of PCa, while age and PSAD were significant predictors of CSPCa. Criteria 2, 3 and 4 demonstrated higher specificity and positive predictive value (PPV) in predicting CSPCa as compared to criterion 1. The overall accuracies of criterion 1, 2, 3 and 4 were 64.42%, 85.58%, 80.77% and 79.81%, respectively. The area under the curve (AUC) values of criterion 2, 3 and 4 were higher (0.827, 0.732 and 0.792) than criterion 1 (0.665).ConclusionUsing PIRADS score for predicting CSPCa as a screening test, criteria 2, 3 and 4 have much higher diagnostic performance and present accuracy of mp-MRI to predict CSPCa can be increased with addition of PSAD and free PSA%.

Outcomes of magnetic resonance imaging fusion-targeted biopsy of prostate imaging reporting and data system 3 lesions.

To evaluate the characteristics and histological outcomes in patients with Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions undergoing magnetic resonance imaging-guided fusion-targeted biopsy (MRIFTB). We retrospectively reviewed 138 patients with PI-RADS category 3 lesions classified using multiparametric MRI who underwent MRIFTB between May 2016 and March 2018. The study population included biopsy-naïve and patients with prior negative biopsy. Univariate and multivariate analyzes were performed to determine significant predictors of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The definition of csPCa was set at Gleason score ≥ 3 + 4. Overall, 114 (82.6%) biopsied lesions were benign and 24 (17.4%) were identified as prostate cancer. Of these 24 lesions, 14 (58.3%) harbored csPCa. Peripheral zone (PZ) lesions were more likely to be associated with malignant disease than transition zone lesions (13.7 vs. 6.2%). Multivariate logistic analysis revealed that age, PZ location, and prostate-specific antigen (PSA) density (P < 0.05) were independent predictors of both PCa and csPCa. A non-negligible number of PI-RADS 3 patients harbor csPCa. Moreover, age, lesion location, and PSA density could be potential clinical predictors of PCa and csPCa. Physicians should be aware of the cancer prevalence of PI-RADS 3 lesions, as the use of the aforementioned factors can help in the decision-making process for these patients.

Prostate-Specific Antigen Trends Predict the Probability of Prostate Cancer in a Very Large U.S. Veterans Affairs Cohort

If prostate-specific antigen (PSA) trends help identify elevated prostate cancer (PCa) risk, they might provide early warning of progressing cancer for further evaluation and justify annual testing. Our objective was to determine whether PSA trends predict PCa likelihood. A biopsy cohort of 361,657 men was obtained from a Veterans Affairs database (1999–2012). PSA trends were estimated for the 310,458 men with at least 2 PSA tests prior to biopsy. Cancer tumors may grow exponentially with cells doubling periodically. We hypothesized that PSA from prostate cancer grows exponentially above a no cancer baseline. We estimated PSA trends on that basis along with five descriptive variables: last PSA before biopsy, growth rate in PSA from cancer above a baseline, PSA variability around the trend, number of PSA tests, and time span of tests. PSA variability is a new variable that measures percentage deviations of PSA tests from estimated trends with 0% variability for a smoothly increasing trend. Logistic regression models were used to estimate relationships between the probability of PCa at biopsy and the trend variables and age. All five PSA trend variables and age were significant predictors of prostate cancer at biopsy (p < 0.0001). An overall logistic regression model achieved an AUC of 0.67 for men with at least 4 tests over at least 3 years, which was a substantial improvement over a single PSA (AUC 0.58). High probability of PCa was associated with low PSA variability (smooth trends), high PSA, high growth rate, many tests over a long time-span and older age. For example, at 4.0 PSA the probability of cancer is 32% for 1 PSA test and increases to 68% for 8 tests over 7 years with smooth, fast growth (0% variability and 50% exponential growth). Our results show that smooth, fast exponential growth in PSA above a baseline predicts an increased probability of PCa. The probability increases as smooth (low variability) trends are observed for more tests over a longer time span, which makes annual testing worth considering. Worrisome PSA trends might be used to trigger further evaluation and continued monitoring of the trends—even at low PSA levels.

Prebiopsy multiparametric MRI-based risk score for predicting prostate cancer in biopsy-naive men with prostate-specific antigen between 4-10 ng/mL.

Risk calculators have traditionally utilized serum prostate-specific antigen (PSA) values in addition to clinical variables to predict the likelihood of prostate cancer (PCa). To develop a prebiopsy multiparametric MRI (mpMRI)-based risk score (RS) and a statistical equation for predicting the risk of PCa in biopsy-naive men with serum PSA between 4-10 ng/mL that may help reduce unnecessary biopsies. Prospective cross-sectional study. In all, 137 consecutive men with PSA between 4-10 ng/mL underwent prebiopsy mpMRI (diffusion-weighted [DW]-MRI and MR spectroscopic imaging [MRSI]) during 2009-2015 were recruited for this study. 1.5T (Avanto, Siemens Health Care, Erlangen, Germany); T1 -weighted, T2 -weighted, DW-MRI, and MRSI sequences were used. All eligible patients underwent mpMRI-directed, cognitive-fusion transrectal ultrasound (TRUS)-guided biopsies. An equation model and an RS were developed using receiver operating characteristic (ROC) curve analysis and a multivariable logistic regression approach. A 10-fold crossvalidation and simulation analyses were performed to assess diagnostic performance of various combinations of mpMRI parameters. Of 137 patients, 32 were diagnosed with PCa on biopsy. Multivariable analysis, adjusted with positive pathology, showed apparent diffusion coefficient (ADC), metabolite ratio, and PSA as significant predictors of PCa (P < 0.05). A statistical equation was derived using these predictors. A simple 6-point mpMRI-based RS was derived for calculating the risk of PCa and it showed that it is highly predictive for PCa (odds ratio = 3.74, 95% confidence interval [CI]: 2.24-6.27, area under the curve [AUC] = 0.87). Both models (equation and RS) yielded high predictive performance (AUC ≥0.85) on validation analysis. A statistical equation and a simple 6-point mpMRI-based RS can be used as a point-of-care tool to potentially help limit the number of negative biopsies in men with PSA between 4 and 10 ng/mL. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1227-1236.

Racial Variation in the Outcome of Subsequent Prostate Biopsies in Men With an Initial Diagnosis of Atypical Small Acinar Proliferation.

African American (AA) men are known to have more aggressive prostate cancer (PCa) compared with Caucasian American men. We sought to determine predictors of subsequent detection and risk stratification of PCa in a racially diverse group of men with atypical small acinar proliferation (ASAP) on initial prostate biopsy. A retrospective analysis was conducted on data from men with ASAP on initial prostate biopsy who subsequently received confirmatory biopsies between September 2000 and July 2015. Biopsies with more than 3 years between initial and confirmatory biopsies were excluded. Race, age, body mass index, transrectal ultrasound volume, serum prostate-specific antigen (PSA), PSA velocity, PSA density, and elapsed time between biopsies were assessed for predictive value in subsequent PCa diagnosis after an initial finding of ASAP. Of 106 men analyzed, 75 (71%) were AA and 31 (29%) were non-AA. Baseline variables revealed AA men had higher PSA levels, PSA velocity, and PSA density (all P< .05). PCa was diagnosed in subsequent biopsy in 42 (40%) patients without significant racial variation; 30 (40%) AA versus 12 (39%) non-AA. Of the 42 PCa patients, 25 (24%) met Epstein criteria for significant disease without racial variation; 18 (24%) AA versus 7 (23%) non-AA. Only 10 (9%) patients had any component of Gleason 4; 7 (9%) AA versus 3 (10%) non-AA. In multivariate analysis, increasing age, PSA level, and PSA density were significant predictors of PCa. AA men diagnosed with ASAP on initial prostate biopsy do not have increased risk of PCa on confirmatory biopsy compared with non-AA men.

PD40-09 EVALUATION OF PREBIOPSY MAGNETIC RESONANCE IMAGING COMBINED WITH PROSTATE-SPECIFIC ANTIGEN DENSITY IN THE DIAGNOSIS OF PROSTATE CANCER IN MEN AGED 75 YEARS AND OLDER WITH ELEVATED PSA

You have accessJournal of UrologyProstate Cancer: Detection & Screening V1 Apr 2017PD40-09 EVALUATION OF PREBIOPSY MAGNETIC RESONANCE IMAGING COMBINED WITH PROSTATE-SPECIFIC ANTIGEN DENSITY IN THE DIAGNOSIS OF PROSTATE CANCER IN MEN AGED 75 YEARS AND OLDER WITH ELEVATED PSA Yoshinori Yanai, Takeo Kosaka, Yu Teranishi, Seiya Hattori, Kazuhiro Matsumoto, Shinya Morita, Kazunobu Shinoda, Toshiaki Shinojima, Ryuichi Mizuno, Eiji Kikuchi, Shuji Mikami, Masahiro Jinzaki, Akira Miyajima, and Mototsugu Oya Yoshinori YanaiYoshinori Yanai More articles by this author , Takeo KosakaTakeo Kosaka More articles by this author , Yu TeranishiYu Teranishi More articles by this author , Seiya HattoriSeiya Hattori More articles by this author , Kazuhiro MatsumotoKazuhiro Matsumoto More articles by this author , Shinya MoritaShinya Morita More articles by this author , Kazunobu ShinodaKazunobu Shinoda More articles by this author , Toshiaki ShinojimaToshiaki Shinojima More articles by this author , Ryuichi MizunoRyuichi Mizuno More articles by this author , Eiji KikuchiEiji Kikuchi More articles by this author , Shuji MikamiShuji Mikami More articles by this author , Masahiro JinzakiMasahiro Jinzaki More articles by this author , Akira MiyajimaAkira Miyajima More articles by this author , and Mototsugu OyaMototsugu Oya More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.1749AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES This study was designed to investigate the effectiveness of magnetic resonance imaging (MRI) in diagnosing prostate cancer (PCa) before transrectal needle biopsy of the prostate (PBx) for elderly men who were aged 75 years old or older. METHODS The medical records of 141 elderly patients over the age of 75 years who underwent MRI before PBx at our institutions were reviewed retrospectively. MRI reports were compared with pathological reports. The factors that were used to detect PBx included age, PSA level, lesion size, prostate volume, and PSA density on MRI. RESULTS PCa was positive in 103 (73.0%) and negative in 38 (27.0%) patients. Between the PCa-positive and PCa-negative groups, almost all factors except serum PSA level were significant predictors of PCa. MRI-based PSA density was more significant than the others. Of the 38 patients without PCa, 30 cases were not PCa patients on PBx even though they had been judged to be cancer positive on MRI before PBx. The specificity of MRI was 21.1% (8/38). Multivariate logistic regression analysis showed that MRI-based TZ-PSA density was the best independent predictor of PCa. When an MRI-based TZ-PSA density cut-off value of 0.42 was used, sensitivity was 83.5% and specificity was 71.1%. Of the 30 false-positive cases on MRI, PBx might not have been needed in 19 cases considering an MRI-based TZ-PSA density cut-off value of 0.42. We stratified the PCa-positive group according to Gleason score (GS<6, 7, and >8). The accuracy of MRI for detecting PCa was 84.6% (GS<6), 91.7% (GS=7), and 96.7% (GS>8). The cancer detection rate on MRI for high GS PCa was higher than low GS PCa. CONCLUSIONS These results indicated that MRI before PBx combined with PSA density, especially with MRI-based TZ-PSA density, is helpful to select candidates for PBx. MRI can also detect high GS PCa, so urologists should consider MRI before PBx for elderly men aged 75 years and older with elevated PSA. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e753 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Yoshinori Yanai More articles by this author Takeo Kosaka More articles by this author Yu Teranishi More articles by this author Seiya Hattori More articles by this author Kazuhiro Matsumoto More articles by this author Shinya Morita More articles by this author Kazunobu Shinoda More articles by this author Toshiaki Shinojima More articles by this author Ryuichi Mizuno More articles by this author Eiji Kikuchi More articles by this author Shuji Mikami More articles by this author Masahiro Jinzaki More articles by this author Akira Miyajima More articles by this author Mototsugu Oya More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Prostatic calculi detected in peripheral zone of the gland during a transrectal ultrasound biopsy can be significant predictors of prostate cancer.

Prostatic calculi (PC) are usually associated with benign prostatic hyperplasia or chronic inflammation. However, in several studies prostatic inflammation and calcification have been implicated in the pathogenesis of prostate cancer (CaP). We evaluated the prevalence of PC during transrectal ultrasound (TRUS) and correlate the ultrasonographic patterns with histological findings. A prospective study of 664 patients undergoing TRUS and prostate biopsy was planned. A standardized reproducible technique was used with using a GE Logiq 7 machine equipped with a 5-9MHz multi-frequency convex probe "end-fire". We defined marked presence of PC as multiple hyperechoic foci with significant area (≥ 3 mm in the largest diameter). PC were classified according to zone distribution into the gland: transitional zone (TZ), central zone (CZ), and peripheral zone (PZ). No significant difference was noted between the patients with PC and without PC, when comparing age, preoperative PSA level, prostate volume, and biopsy number, except for DRE findings. 168 patients (25.3%) had marked presence of PC on TRUS: 50.6% in TZ, 20.2% in CZ, and 29.2% in PZ. 31 patients (63.3%) with presence of PC in PZ had CaP on biopsy. The correlation observed between CaP and the presence of PC in PZ was statistically significant (p &lt; 0.001). However, among patients in the CaP group there was no statistical association between PC and moderate or high Gleason grade. This study suggests that chronic prostatic inflammation and PC have a role in the biogenesis of cancer. CaP was more frequent in patients with PC in PZ of the gland, but was not associated with higher Gleason grade among these patients (p &lt; 0.001).

Combination of prostate imaging reporting and data system (PI-RADS) score and prostate-specific antigen (PSA) density predicts biopsy outcome in prostate biopsy naïve patients.

To assess the value of the Prostate Imaging Reporting and Data System (PI-RADS) scoring system, for prostate multi-parametric magnetic resonance imaging (mpMRI) to detect prostate cancer, and classical parameters, such as prostate-specific antigen (PSA) level, prostate volume and PSA density, for predicting biopsy outcome in biopsy naïve patients who have suspected prostate cancer. Patients who underwent mpMRI at our hospital, and who had their first prostate biopsy between July 2010 and April 2014, were analysed retrospectively. The prostate biopsies were taken transperineally under transrectal ultrasonography guidance. In all, 14 cores were biopsied as a systematic biopsy in all patients. Two cognitive fusion-targeted biopsy cores were added for each lesion in patients who had suspicious or equivocal lesions on mpMRI. The PI-RADS scoring system version 2.0 (PI-RADS v2) was used to describe the MRI findings. Univariate and multivariate analyses were performed to determine significant predictors of prostate cancer and clinically significant prostate cancer. In all, 288 patients were analysed. The median patient age, PSA level, prostate volume and PSA density were 69 years, 7.5 ng/mL, 28.7 mL, and 0.26 ng/mL/mL, respectively. The biopsy results were benign, clinically insignificant, and clinically significant prostate cancer in 129 (45%), 18 (6%) and 141 (49%) patients, respectively. The multivariate analysis revealed that PI-RADS v2 score and PSA density were independent predictors for prostate cancer and clinically significant prostate cancer. When PI-RADS v2 score and PSA density were combined, a PI-RADS v2 score of ≥4 and PSA density ≥0.15 ng/mL/mL, or PI-RADS v2 score of 3 and PSA density of ≥0.30 ng/mL/mL, was associated with the highest clinically significant prostate cancer detection rates (76-97%) on the first biopsy. Of the patients in this group with negative biopsy results, 22% were subsequently diagnosed as prostate cancer. In contrast, a PI-RADS v2 score of ≤3 and PSA density of <0.15 ng/mL/mL yielded no clinically significant prostate cancer and no additional detection of prostate cancer on further biopsies. A combination of PI-RADS v2 score and PSA density can help in the decision-making process before prostate biopsy and in the follow-up strategy in biopsy naïve patients. Patients with a PI-RADS v2 score of ≤3 and PSA density of <0.15 ng/mL/mL may avoid unnecessary biopsies.

MP6-01 URINARY BIOMARKERS FOR THE DETECTION OF PROSTATE CANCER IN PATIENTS WITH HIGH-GRADE PROSTATIC INTRAEPITHELIAL NEOPLASIA (HGPIN).

You have accessJournal of UrologyProstate Cancer: Markers II1 Apr 2015MP6-01 URINARY BIOMARKERS FOR THE DETECTION OF PROSTATE CANCER IN PATIENTS WITH HIGH-GRADE PROSTATIC INTRAEPITHELIAL NEOPLASIA (HGPIN). Juan M. Bastarós, Tamara Sequeiros, José Placer, Jacques Planas, Lucas Regis, Milagros Sánchez, Marina Rigau, Melania Montes, Inés de Torres, Jaume Reventós, Andreas Doll, and Juan Morote Juan M. BastarósJuan M. Bastarós More articles by this author , Tamara SequeirosTamara Sequeiros More articles by this author , José PlacerJosé Placer More articles by this author , Jacques PlanasJacques Planas More articles by this author , Lucas RegisLucas Regis More articles by this author , Milagros SánchezMilagros Sánchez More articles by this author , Marina RigauMarina Rigau More articles by this author , Melania MontesMelania Montes More articles by this author , Inés de TorresInés de Torres More articles by this author , Jaume ReventósJaume Reventós More articles by this author , Andreas DollAndreas Doll More articles by this author , and Juan MoroteJuan Morote More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.248AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Men with suspicion of prostate cancer (PCa) are referred for prostatic biopsy (PB), and some of them will present HGPIN (a commonly accepted precursor of PCa) in this first PB. Those patients frequently face several years of active surveillance including repeat PBs. Previously, our research group showed that PCA3 and PSGR gene expression in urine sediment could be useful biomarkers for the detection of PCa in benign prostatic hyperplasia cases. Although with a lower efficacy, we also observed that PCA3 was able to detect PCa in those patients with a previous diagnosis of HGPIN. The aim of this study is to identify urinary biomarkers that could differentiate between indolent HGPIN cases and those who actually present PCa. METHODS From a cohort of 114 patients with diagnosis of HGPIN (in a first PB recommended due to increased serum PSA levels (>4ng/mL) and/or an abnormal diagnostic DRE), urine sediment samples were obtained, after DRE within days before a repeat biopsy after a posterior follow up of at least two years. Reverse-transcriptase PCR (RTqPCR) of extracted RNA was conducted to determine expression of 6 endogenous genes and 17 target genes, all of them putative PCa biomarkers. Univariate tests and univariate and multivariate logistic regressions were used to examine associations between PCa diagnostic status and testing genes. All possible models were created using combinations of the most significant genes obtained in the univariate analysis and multivariate logistic regression was applied to them. The number of PBs potentially avoided by the use of the proposed biomarkers was calculated. RESULTS By univariate statistical analysis of the obtained data, it was found that PSMA, PCA3, PSGR, GOLM, KLK3 and CDH1 were significant predictors of PCa in repeat PB. Multiplex models that use the PCa biomarkers KLK3, PSMA, PSGR, GOLM1 and CDH1 (AUC=0.81-0.86) outperform all the assayed genes, including PCA3 (AUC=0.70), when used individually. With a fixed sensitivity of 95%, the specificity of the models was of 41–58%, compared to the 30% of PCA3. Applying these models, it would be possible to save from 33% and up to 47% of the repeat biopsies practiced. CONCLUSIONS A multiplexed RTqPCR assay on urine sediments from patients presenting for a repeat PB due to a diagnosis of HGPIN can significantly improve the predictive ability when compared to PCA3 or any other assayed gene when used alone. Further evaluation and validation of these biomarkers in larger and independent cohorts is highly desirable, in order to confirm these results. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e54 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Juan M. Bastarós More articles by this author Tamara Sequeiros More articles by this author José Placer More articles by this author Jacques Planas More articles by this author Lucas Regis More articles by this author Milagros Sánchez More articles by this author Marina Rigau More articles by this author Melania Montes More articles by this author Inés de Torres More articles by this author Jaume Reventós More articles by this author Andreas Doll More articles by this author Juan Morote More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Artificial neural network for predicting pathological stage of clinically localized prostate cancer in a Taiwanese population

BackgroundWe developed an artificial neural network (ANN) model to predict prostate cancer pathological staging in patients prior to when they received radical prostatectomy as this is more effective than logistic regression (LR), or combined use of age, prostate-specific antigen (PSA), body mass index (BMI), digital rectal examination (DRE), trans-rectal ultrasound (TRUS), biopsy Gleason sum, and primary biopsy Gleason grade. MethodsOur study evaluated 299 patients undergoing retro-pubic radical prostatectomy or robotic-assisted laparoscopic radical prostatectomy surgical procedures with pelvic lymph node dissection. The results were intended to predict the pathological stage of prostate cancer (T2 or T3) after radical surgery. The predictive ability of ANN was compared with LR and validation of the 2007 Partin Tables was estimated by the areas under the receiving operating characteristic curve (AUCs). ResultsOf the 299 patients we evaluated, 109 (36.45%) displayed prostate cancer with extra-capsular extension (ECE), and 190 (63.55%) displayed organ-confined disease (OCD). LR analysis showed that only PSA and BMI were statistically significant predictors of prostate cancer with capsule invasion. Overall, ANN outperformed LR significantly (0.795 ± 0.023 versus 0.746 ± 0.025, p = 0.016). Validation using the current Partin Tables for the participants of our study was assessed, and the predictive capacity of AUC for OCD was 0.695. ConclusionANN was superior to LR at predicting OCD in prostate cancer. Compared with the validation of current Partin Tables for the Taiwanese population, the ANN model resulted in larger AUCs and more accurate prediction of the pathologic stage of prostate cancer.