An updated catalog of prostate cancer predictive tools

Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer

PURPOSE

To assess the ability of magnetic resonance imaging (MRI) to diagnose extraprostatic extension (EPE) in prostate cancer.

MATERIALS AND METHODS

With Institutional Review Board (IRB) approval, 149 men with 170 ≥0.5 mL tumors underwent preoperative 3T MRI followed by radical prostatectomy (RP) between 2012-2015. Two blinded radiologists (R1/R2) assessed tumors using Prostate Imaging Reporting and Data System (PI-RADS) v2, subjectively evaluated for the presence of EPE, measured tumor size, and length of capsular contact (LCC). A third blinded radiologist, using MRI-RP-maps, measured whole-lesion: apparent diffusion coefficient (ADC) mean/centile and histogram features. Comparisons were performed using chi-square, logistic regression, and receiver operator characteristic (ROC) analysis.

RESULTS

The subjective EPE assessment showed high specificity (SPEC = 75.4/91.3% [R1/R2]), low sensitivity (SENS = 43.3/43.6% [R1/R2]), and area-under (AU) ROC curve = 0.67 (confidence interval [CI] 0.61-0.73) R1 and 0.61 (CI 0.53-0.70) R2; (k = 0.33). PI-RADS v2 scores were strongly associated with EPE (P < 0.001 / P = 0.008; R1/R2) with AU-ROC curve = 0.72 (0.64-0.79) R1 and 0.61 (0.53-0.70) R2; (k = 0.44). Tumors with EPE were larger (18.8 ± 7.8 [median 17, range 6-51] vs. 18.8 ± 4.9 [12, 6-28] mm) and had greater LCC (21.1 ± 14.9 [16, 1-85] vs. 13.6 ± 6.1 [11.5, 4-30] mm); P < 0.001 and 0.002, respectively. AU-ROC for size was 0.73 (0.64-0.80) and LCC was 0.69 (0.60-0.76), respectively. Optimal SENS/SPEC for diagnosis of EPE were: size ≥15 mm = 67.7/66.7% and LCC ≥11 mm = 84.9/44.8%. 10^th -centile ADC and ADC entropy were both associated with EPE (P = 0.02 and < 0.001), with AU-ROC = 0.56 (0.47-0.65) and 0.76 (0.69-0.83), respectively. Optimal SENS/SPEC for diagnosis of EPE with entropy ≥6.99 was 63.3/75.0%. 25^th -centile ADC trended towards being significantly lower with EPE (P = 0.06) with no difference in other ADC metrics (P = 0.25-0.88). Size, LCC, and ADC entropy improved sensitivity but reduced specificity compared with subjective analysis with no difference in overall accuracy (P = 0.38).

CONCLUSION

Measurements of tumor size, capsular contact, and ADC entropy improve sensitivity but reduce specificity for diagnosis of EPE compared to subjective assessment.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:176-185.

Tools for Predicting Clinical and Patient-reported Outcomes in Prostate Cancer Patients Undergoing Androgen Deprivation Therapy: A Systematic Review of Prognostic Accuracy and Validity

Androgen deprivation therapy (ADT) can result in a range of adverse symptoms that reduce patients' quality of life. Careful patient counseling on the likely clinical outcomes and adverse effects is therefore vital. The present systematic review was undertaken to identify and characterize all the tools used for the prediction of clinical and patient-reported outcome measures (PROMs) in patients with prostate cancer undergoing ADT. PubMed and EMBASE were systematically searched from 2007 to 2016. Search terms related to the inclusion criteria were: prostate cancer, clinical outcomes, PROMs, ADT, and prognosis. Titles and abstracts were reviewed to find relevant studies, which were advanced to full-text review. The reference lists were screened for additional studies. The Centre for Evidence Based Medicine critical appraisal of prognostic studies tool was applied. The search strategy identified 8755 studies. Of the 8755 studies, 22 on clinical outcomes were identified. However, no studies of PROMs were found. Nine tools could be used to predict clinical outcomes in treatment-naive patients and 10 in patients with recurrence. The Japan Cancer of the Prostate Risk Assessment (J-CAPRA) nomogram was the best performing and validated tool for the prediction of clinical outcomes in treatment-naive patients, and the Chi and Shamash prognostic indexes have been validated for use in patients with castration-resistant disease in different clinical contexts. Using the J-CAPRA nomogram should help clinicians deliver accurate, evidence-based counseling to patients undergoing primary ADT. A strong need exists for primary studies that derive and validate tools for the prediction of PROMs in patients undergoing ADT under any circumstance because these are currently absent from the literature.

An appraisal of analytical tools used in predicting clinical outcomes following radiation therapy treatment of men with prostate cancer: a systematic review

Background

Prostate cancer can be treated with several different modalities, including radiation treatment. Various prognostic tools have been developed to aid decision making by providing estimates of the probability of different outcomes. Such tools have been demonstrated to have better prognostic accuracy than clinical judgment alone.

Methods

A systematic review was undertaken to identify papers relating to the prediction of clinical outcomes (biochemical failure, metastasis, survival) in patients with prostate cancer who received radiation treatment, with the particular aim of identifying whether published tools are adequately developed, validated, and provide accurate predictions. PubMed and EMBASE were searched from July 2007. Title and abstract screening, full text review, and critical appraisal were conducted by two reviewers. A review protocol was published in advance of commencing literature searches.

Results

The search strategy resulted in 165 potential articles, of which 72 were selected for full text review and 47 ultimately included. These papers described 66 models which were newly developed and 31 which were external validations of already published predictive tools. The included studies represented a total of 60,457 patients, recruited between 1984 and 2009. Sixty five percent of models were not externally validated, 57% did not report accuracy and 31% included variables which are not readily accessible in existing datasets. Most models (72, 74%) related to external beam radiation therapy with the remainder relating to brachytherapy (alone or in combination with external beam radiation therapy).

Conclusions

A large number of prognostic models (97) have been described in the recent literature, representing a rapid increase since previous reviews (17 papers, 1966–2007). Most models described were not validated and a third utilised variables which are not readily accessible in existing data collections. Where validation had occurred, it was often limited to data taken from single institutes in the US. While validated and accurate models are available to predict prostate cancer specific mortality following external beam radiation therapy, there is a scarcity of such tools relating to brachytherapy. This review provides an accessible catalogue of predictive tools for current use and which should be prioritised for future validation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-017-0786-z) contains supplementary material, which is available to authorized users.

Diabetes and beta-adrenergic blockage are risk factors for metastatic prostate cancer

Background

We evaluated the influence of comorbidity inferred risks for lymph node metastasis (pN1) and positive surgical margins (R1) after radical prostatectomy in order to optimize pretherapeutic risk classification.

We analyzed 454 patients after radical prostatectomy (RP) between 2009 and 2014. Comorbidities were defined by patients’ medication from our electronic patient chart and stratified according to the ATC WHO code. Endpoints were lymph node metastasis (pN1) and positive surgical margins (R1).

Results

Rates for pN1 and R1 were 21.4% (97/454) and 29.3% (133/454), respectively. In addition to CAPRA and Gleason score, we identified diabetes as a significant medication inferred risk factor for pN1 (OR 2.9, p = 0.004/OR 3.2, p = 0.001/OR 3.5, p = 0.001) and beta-blockers for R1 (OR 1.9, p = 0.020/OR 2.9, p = 0.004). Patients with diabetes showed no statistically significant difference in Gleason score, CAPRA Score, PSA, and age compared to non-diabetic patients.

Conclusions

We identified diabetes and beta1 adrenergic blockage as significant risk factors for lymph node metastasis and positive surgical margins in prostate cancer (PCa). Patients at risk will need intensive pretherapeutic staging for optimal therapeutic stratification.

Electronic supplementary material

The online version of this article (doi:10.1186/s12957-017-1117-4) contains supplementary material, which is available to authorized users.

Predicting Prostate Cancer Recurrence After Radical Prostatectomy

BACKGROUND

Prostate cancer prognosis is variable, and management decisions involve balancing patients' risks of recurrence and recurrence-free death. Moreover, the roles of body mass index (BMI) and race in risk of recurrence are controversial [1,2]. To address these issues, we developed and cross-validated RAPS (Risks After Prostate Surgery), a personal prediction model for biochemical recurrence (BCR) within 10 years of radical prostatectomy (RP) that includes BMI and race as possible predictors, and recurrence-free death as a competing risk.

METHODS

RAPS uses a patient's risk factors at surgery to assign him a recurrence probability based on statistical learning methods applied to a cohort of 1,276 patients undergoing RP at the University of Pennsylvania. We compared the performance of RAPS to that of an existing model with respect to calibration (by comparing observed and predicted outcomes), and discrimination (using the area under the receiver operating characteristic curve (AUC)).

RESULTS

RAPS' cross-validated BCR predictions provided better calibration than those of an existing model that underestimated patients' risks. Discrimination was similar for the two models, with BCR AUCs of 0.793, 95% confidence interval (0.766-0.820) for RAPS, and 0.780 (0.745-0.815) for the existing model. RAPS' most important BCR predictors were tumor grade, preoperative prostate-specific antigen (PSA) level and BMI; race was less important [3]. RAPS' predictions can be obtained online at https://predict.shinyapps.io/raps.

CONCLUSION

RAPS' cross-validated BCR predictions were better calibrated than those of an existing model, and BMI information contributed substantially to these predictions. RAPS predictions for recurrence-free death were limited by lack of co-morbidity data; however the model provides a simple framework for extension to include such data. Its use and extension should facilitate decision strategies for post-RP prostate cancer management. Prostate 77:291-298, 2017. © 2016 Wiley Periodicals, Inc.

A four gene signature predictive of recurrent prostate cancer

Prostate cancer is the most common form of non-dermatological cancer among US men, with an increasing incidence due to the aging population. Patients diagnosed with clinically localized disease identified as intermediate or high-risk are often treated by radical prostatectomy. Approximately 33% of these patients will suffer recurrence after surgery. Identifying patients likely to experience recurrence after radical prostatectomy would lead to improved clinical outcomes, as these patients could receive adjuvant radiotherapy. Here, we report a new tool for prediction of prostate cancer recurrence based on the expression pattern of a small set of cooperation response genes (CRGs). CRGs are a group of genes downstream of cooperating oncogenic mutations previously identified in a colon cancer model that are critical to the cancer phenotype. We show that systemic dysregulation of CRGs is also found in prostate cancer, including a 4-gene signature (HBEGF, HOXC13, IGFBP2, and SATB1) capable of differentiating recurrent from non-recurrent prostate cancer. To develop a suitable diagnostic tool to predict disease outcomes in individual patients, multiple algorithms and data handling strategies were evaluated on a training set using leave-one-out cross-validation (LOOCV). The best-performing algorithm, when used in combination with a predictive nomogram based on clinical staging, predicted recurrent and non-recurrent disease outcomes in a blinded validation set with 83% accuracy, outperforming previous methods. Disease-free survival times between the cohort of prostate cancers predicted to recur and predicted not to recur differed significantly (p = 1.38x10-6). Therefore, this test allows us to accurately identify prostate cancer patients likely to experience future recurrent disease immediately following removal of the primary tumor.

PROACT: Iterative Design of a Patient-Centered Visualization for Effective Prostate Cancer Health Risk Communication

Prostate cancer is the most common cancer among men in the US, and yet most cases represent localized cancer for which the optimal treatment is unclear. Accumulating evidence suggests that the available treatment options, including surgery and conservative treatment, result in a similar prognosis for most men with localized prostate cancer. However, approximately 90% of patients choose surgery over conservative treatment, despite the risk of severe side effects like erectile dysfunction and incontinence. Recent medical research suggests that a key reason is the lack of patient-centered tools that can effectively communicate personalized risk information and enable them to make better health decisions. In this paper, we report the iterative design process and results of developing the PROgnosis Assessment for Conservative Treatment (PROACT) tool, a personalized health risk communication tool for localized prostate cancer patients. PROACT utilizes two published clinical prediction models to communicate the patients' personalized risk estimates and compare treatment options. In collaboration with the Maine Medical Center, we conducted two rounds of evaluations with prostate cancer survivors and urologists to identify the design elements and narrative structure that effectively facilitate patient comprehension under emotional distress. Our results indicate that visualization can be an effective means to communicate complex risk information to patients with low numeracy and visual literacy. However, the visualizations need to be carefully chosen to balance readability with ease of comprehension. In addition, due to patients' charged emotional state, an intuitive narrative structure that considers the patients' information need is critical to aid the patients' comprehension of their risk information.

Evaluation of extracapsular extension in prostate cancer using qualitative and quantitative multiparametric MRI

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (mpMRI) for extracapsular extension (ECE) in prostate cancer (PCa).

MATERIALS AND METHODS

In all, 292 patients who received radical prostatectomy and underwent preoperative mpMRI at 3T were enrolled retrospectively. For determining the associations with ECE, the likelihood of ECE was assessed qualitatively on T₂ -weighted imaging (T₂ WI) and combined T₂ WI and diffusion-weighted imaging (DWI) or dynamic contrast-enhanced imaging (DCEI). Quantitative MRI parameters were measured in PCa based on histopathological findings. Two models for detecting ECE including imaging and clinical parameters were developed using multivariate analysis: Model 1 excluding combined T₂ WI and DWI and DCEI and Model 2 excluding combined T₂ WI and DWI, and combined T₂ WI and DCEI. Diagnostic performance of imaging parameters and models was evaluated using the area under the receiver operating characteristics curve (Az).

RESULTS

For detecting ECE, the specificity, accuracy, and Az of combined T₂ WI and DWI or DCEI were statistically better than those of T₂ WI (P < 0.05), and all quantitative MRI parameters showed a statistical difference between the patients with and without ECE (P < 0.05). On multivariate analysis, significant independent markers in Model 1 were combined T₂ WI and DWI, combined T₂ WI and DCEI, and K^trans (P < 0.05). In Model 2, significant markers were combined T₂ WI and DWI and DCEI, K^trans , K_ep , and Ve (P < 0.05). The Az values of models 1 and 2 were 0.944 and 0.957, respectively.

CONCLUSION

mpMRI may be useful to improve diagnostic accuracy of the models for determining the associations with ECE in PCa.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1760-1770.

Prediction of prostate cancer recurrence using quantitative phase imaging: Validation on a general population

Prediction of biochemical recurrence risk of prostate cancer following radical prostatectomy is critical for determining whether the patient would benefit from adjuvant treatments. Various nomograms exist today for identifying individuals at higher risk for recurrence; however, an optimistic under-estimation of recurrence risk is a common problem associated with these methods. We previously showed that anisotropy of light scattering measured using quantitative phase imaging, in the stromal layer adjacent to cancerous glands, is predictive of recurrence. That nested-case controlled study consisted of specimens specifically chosen such that the current prognostic methods fail. Here we report on validating the utility of optical anisotropy for prediction of prostate cancer recurrence in a general population of 192 patients, with 17% probability of recurrence. Our results show that our method can identify recurrent cases with 73% sensitivity and 72% specificity, which is comparable to that of CAPRA-S, a current state of the art method, in the same population. However, our results show that optical anisotropy outperforms CAPRA-S for patients with Gleason grades 7-10. In essence, we demonstrate that anisotropy is a better biomarker for identifying high-risk cases, while Gleason grade is better suited for selecting non-recurrence. Therefore, we propose that anisotropy and current techniques be used together to maximize prediction accuracy.

Prostate cancer-specific mortality after radical prostatectomy: value of preoperative MRI

BACKGROUND

Although magnetic resonance imaging (MRI) is currently indispensable in the preoperative setting of biopsy-proven prostate cancer, the value of preoperative MRI for predicting prostate cancer-specific mortality (PCSM) is not well known.

PURPOSE

To evaluate the value of MRI for predicting PCSM in patients who underwent radical prostatectomy (RP) for localized prostate cancer.

MATERIAL AND METHODS

A total of 318 patients underwent MRI followed by RP. MRI was assessed for the presence of clinically significant cancer using a five-point Likert scale, where ≥4 was considered positive. Cox proportional hazards regression analyses was used to determine the relationship of preoperative factors with PCSM. PCSM was calculated using the Kaplan-Meier method and compared between factors using the log-rank test.

RESULTS

After a median follow-up of 104 months, 11 (3.5%) patients died of prostate cancer. One hundred and four (32.7%) patients had clinically significant prostate cancer on MRI. Univariate analysis revealed that Gleason grade, greatest percentage of involved core length (GPCL), and clinically significant cancer on MRI were significantly related to PCSM (P = 0.001-0.003). Multivariate analysis showed that GPCL (hazard ratio [HR], 1.028; 95% confidence interval [CI], 1.000-1.057; P = 0.048) and clinically significant cancer on MRI (HR, 10.903; 95% CI, 1.287-92.374; P = 0.028) were independent predictors of PCSM. The 5 - and 10-year PCSM rates were 0.6% and 1.3% in patients with GPCL <50% and 5.1% and 8.6% in those with GPCL ≥50% (P = 0.012). Patients without clinically significant cancer on MRI showed 5 - and 10-year PCSM rates of 0% and 0.5%, respectively, whereas those with clinically significant cancer on MRI showed rates of 8% and 14.2%, respectively (P < 0.001).

CONCLUSION

Preoperative MRI and GPCL may be used to predict PCSM after RP.

Prostate Cancer Predictive Simulation Modelling, Assessing the Risk Technique (PCP-SMART): Introduction and Initial Clinical Efficacy Evaluation Data Presentation of a Simple Novel Mathematical Simulation Modelling Method, Devised to Predict the Outcome of Prostate Biopsy on an Individual Basis

INTRODUCTION

We developed a mathematical "prostate cancer (PCa) conditions simulating" predictive model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk determinator [PCRD] index) and a PCa risk equation. We used these to estimate the probability of finding PCa on prostate biopsy, on an individual basis.

MATERIALS AND METHODS

A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy were enrolled in the present study. Given that PCa risk relates to the total prostate-specific antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age; 2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient's tPSA and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating model. Linear regression analysis was used to derive the coefficient of determination (R²), termed the PCRD index. To estimate the PCRD index's predictive validity, we used the χ² test, multiple logistic regression analysis with PCa risk equation formation, calculation of test performance characteristics, and area under the receiver operating characteristic curve analysis using SPSS, version 22 (P < .05).

RESULTS

The biopsy findings were positive for PCa in 167 patients (45.1%) and negative in 164 (44.2%). The PCRD index was positively signed in 89.82% positive PCa cases and negative in 91.46% negative PCa cases (χ² test; P < .001; relative risk, 8.98). The sensitivity was 89.8%, specificity was 91.5%, positive predictive value was 91.5%, negative predictive value was 89.8%, positive likelihood ratio was 10.5, negative likelihood ratio was 0.11, and accuracy was 90.6%. Multiple logistic regression revealed the PCRD index as an independent PCa predictor, and the formulated risk equation was 91% accurate in predicting the probability of finding PCa. On the receiver operating characteristic analysis, the PCRD index (area under the curve, 0.926) significantly (P < .001) outperformed other, established PCa predictors.

CONCLUSION

The PCRD index effectively predicted the prostate biopsy outcome, correctly identifying 9 of 10 men who were eventually diagnosed with PCa and correctly ruling out PCa for 9 of 10 men who did not have PCa. Its predictive power significantly outperformed established PCa predictors, and the formulated risk equation accurately calculated the probability of finding cancer on biopsy, on an individual patient basis.

Preoperative Multiparametric Magnetic Resonance Imaging Predicts Biochemical Recurrence in Prostate Cancer after Radical Prostatectomy

Objectives

To evaluate the utility of preoperative multiparametric magnetic resonance imaging (MP-MRI) in predicting biochemical recurrence (BCR) following radical prostatectomy (RP).

Materials/Methods

From March 2007 to January 2015, 421 consecutive patients with prostate cancer (PCa) underwent preoperative MP-MRI and RP. BCR-free survival rates were estimated using the Kaplan-Meier method. Cox proportional hazards models were used to identify clinical and imaging variables predictive of BCR. Logistic regression was performed to generate a nomogram to predict three-year BCR probability.

Results

Of the total cohort, 370 patients met inclusion criteria with 39 (10.5%) patients experiencing BCR. On multivariate analysis, preoperative prostate-specific antigen (PSA) (p = 0.01), biopsy Gleason score (p = 0.0008), MP-MRI suspicion score (p = 0.03), and extracapsular extension on MP-MRI (p = 0.03) were significantly associated with time to BCR. A nomogram integrating these factors to predict BCR at three years after RP demonstrated a c-index of 0.84, outperforming the predictive value of Gleason score and PSA alone (c-index 0.74, p = 0.02).

Conclusion

The addition of MP-MRI to standard clinical factors significantly improves prediction of BCR in a post-prostatectomy PCa cohort. This could serve as a valuable tool to support clinical decision-making in patients with moderate and high-risk cancers.

Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings

BACKGROUND

Gleason scoring represents the standard for diagnosis of prostate cancer (PCa) and assessment of prognosis following radical prostatectomy (RP), but it does not account for patterns in neighboring normal-appearing benign fields that may be predictive of disease recurrence.

OBJECTIVE

To investigate (1) whether computer-extracted image features within tumor-adjacent benign regions on digital pathology images could predict recurrence in PCa patients after surgery and (2) whether a tumor plus adjacent benign signature (TABS) could better predict recurrence compared with Gleason score or features from benign or cancerous regions alone.

DESIGN, SETTING, AND PARTICIPANTS

We studied 140 tissue microarray cores (0.6mm each) from 70 PCa patients following surgery between 2000 and 2004 with up to 14 yr of follow-up. Overall, 22 patients experienced recurrence (biochemical [prostate-specific antigen], local, or distant recurrence and cancer death) and 48 did not.

INTERVENTION

RP was performed in all patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The top 10 features identified as most predictive of recurrence within both the benign and cancerous regions were combined into a 10-feature signature (TABS). Computer-extracted nuclear shape and architectural features from cancerous regions, adjacent benign fields, and TABS were evaluated via random forest classification accuracy and Kaplan-Meier survival analysis.

RESULTS AND LIMITATIONS

Tumor-adjacent benign field features were predictive of recurrence (area under the receiver operating characteristic curve [AUC]: 0.72). Tumor-field nuclear shape descriptors and benign-field local nuclear arrangement were the predominant features found for TABS (AUC: 0.77). Combining TABS with Gleason sum further improved identification of recurrence (AUC: 0.81). All experiments were performed using threefold cross-validation without independent test set validation.

CONCLUSIONS

Computer-extracted nuclear features within cancerous and benign regions predict recurrence following RP. Furthermore, TABS was shown to provide added value to common predictors including Gleason sum and Kattan and Stephenson nomograms.

PATIENT SUMMARY

Future studies may benefit from evaluation of benign regions proximal to the tumor on surgically excised prostate cancer tissue for assessing risk of disease recurrence.

Comparison of the Formula of PSA, Age, Prostate Volume and Race Versus PSA Density and the Detection of Primary Malignant Circulating Prostate Cells in Predicting a Positive Initial Prostate Biopsy in Chilean Men with Suspicion of Prostate Cancer

BACKGROUND

Combining risk factors for prostate cancer into a predictive tool may improve the detection of prostate cancer while decreasing the number of benign biopsies. We compare one such tool, age multiplied by prostate volume divided by total serum PSA (PSA-AV) with PSA density and detection of primary malignant circulating prostate cells (CPCs) in a Chilean prostate cancer screening program. The objectives were not only to determine the predictive values of each, but to determine the number of clinically significant cancers that would have been detected or missed.

MATERIALS AND METHODS

A prospective study was conducted of all men undergoing 12 core ultrasound guided prostate biopsy for suspicion of cancer attending the Hospital DIPRECA and Hospital de Carabineros de Chile. Total serum PSA was registered, prostate volumecalculated at the moment of biopsy, and an 8 ml blood simple taken immediately before the biopsy procedure. Mononuclear cells were obtained from the blood simple using differential gel centrifugation and CPCs identified using immunocytchemistry with anti- PSA and anti-P504S. Biopsy results were classed as positive or negative for cancer and if positive the Gleason score, number of positive cores and percent infiltration recorded.

RESULTS

A total of 664 men participated, of whom 234 (35.2%) had cancer detected. They were older, had higher mean PSA, PSA density and lower PSA-AV. Detection of CPCs had high predictive score, sensitivity, sensibility and positive and negative predictive values, PSA-AV was not significantly different from PSA density in this population. The use of CPC detection avoided more biopsies and missed fewer significant cancers.

CONCLUSIONS

In this screening population the use of CPC detection predicted the presence of clinically significant prostate cancer better than the other parameters. The high negative predictive value would allow men CPC negative to avoid biopsy but remain in follow up. The formula PSA-AV did not add to the predictive performance using PSA density.

Association of ring box-1 protein overexpression with clinicopathologic prognostic parameters in prostate carcinoma

AIM

To determine the expression of Ring Box-1 (RBX-1) protein in prostate carcinoma (PCa) and the association between RBX-1 expression and clinicopathologic prognostic parameters.

MATERIAL AND METHODS

Relevant data such as age, preoperative serum PSA values, and tumor stage were obtained from 51 patients' with PCa record who underwent radical prostatectomy between January 2010 and March 2014. Hematoxylin-eosin stained pathology slides were evaluated by 2 pathologists blinded to patients' data in order to determine Gleason grade groups, tumor stage, tumor volume, capsule invasion, lymphovascular invasion, perineural invasion, and seminal vesicle invasion. Immunoreactivity scoring system (IRS) was used to determine RBX-1 expressions.

RESULTS

A statistically significant difference was determined in terms of RBX-1 expression between non tumoral prostate tissue, high grade prostatic intraepithelial neoplasia (H-PIN) and carcinoma foci (P = 0.001). RBX-1 expression in the Gleason pattern 4 was higher than the Gleason pattern 3 and H-PIN foci as well as non tumoral prostate tissue. Likewise, in cases with PSA levels of>10.1ng/ml, RBX-1 expression was higher than those≤10ng/ml. Moreover, RBX-1 expression of stage II cases was higher than stage I (P = 0.019), RBX-1 expression of stage III higher than stage I cases (P = 0.044). However, RBX-1 expression was not related with clinicopathologic parameters including patient age, tumor volume, lymphovascular invasion, perineural invasion, seminal vesicle invasion, or capsule invasion.

CONCLUSIONS

RBX-1 protein is overexpressed in PCa and associated with clinicopathologic prognostic parameters related with biological potential of the aggressive disease. Further studies of basic and molecular science are needed to reveal clinical and therapeutic implications of RBX-1 in PCa.

The implications of ageing and life expectancy in prostate cancer treatment

In patients diagnosed with prostate cancer, the selection of treatment, including the type of therapy and its aggressiveness, is often based on a patient's age and life expectancy. Life expectancy estimates are too often calculated solely on the patient's chronological age, overlooking comorbid conditions and their severity, which can greatly affect life expectancy. If, in addition to chronological age, comorbid conditions are used to assess a patient's life expectancy, the most appropriate treatment options are more likely to be selected. Older, healthy patients might be able to tolerate more aggressive treatment than would be administered on the basis of their age alone, and younger patients with numerous comorbid conditions could avoid harsh therapy that might not be appropriate given their current state of health. The key idea to consider in treatment selection is what a patient's quality of life would be like with or without a particular treatment option. In an era of precision medicine, decisions regarding the provision of health care should be made rationally and on the basis of objective estimates of the threat of disease and the benefits and costs of intervention and within the context of the patient's characteristics and desires.

Prostate Cancer: Utility of Whole-Lesion Apparent Diffusion Coefficient Metrics for Prediction of Biochemical Recurrence After Radical Prostatectomy

OBJECTIVE

The purpose of this study was to investigate the additional value of whole-lesion histogram apparent diffusion coefficient (ADC) metrics, when combined with standard pathologic features, in prediction of biochemical recurrence (BCR) after radical prostatectomy for prostate cancer.

MATERIALS AND METHODS

The study included 193 patients (mean age, 61 ± 7 years) who underwent 3-T MRI with DWI (b values, 50 and 1000 s/mm(2)) before prostatectomy. Histogram metrics were derived from 3D volumes of interest encompassing the entire lesion on ADC maps. Pathologic features from radical prostatectomy and subsequent BCR were recorded for each patient. The Fisher exact test and Mann-Whitney test were used to compare ADC-based metrics and pathologic features between patients with and patients without BCR. Stepwise logistic regression analysis was used to construct multivariable models for prediction of BCR, which were assessed by ROC analysis.

RESULTS

BCR occurred in 16.6% (32/193) of patients. Variables significantly associated with BCR included primary Gleason grade, Gleason score, extraprostatic extension, seminal vesicle invasion, positive surgical margin, preoperative prostate-specific antigen level, MRI tumor volume, mean whole-lesion ADC, entropy ADC, and mean ADC of the bottom 10th, 10-25th, and 25-50th percentiles (p ≤ 0.019). Significant independent predictors of BCR at multivariable analysis were primary Gleason grade, extraprostatic extension, mean of the bottom 10th percentile ADC, and entropy ADC (p = 0.002-0.037). The AUC of this multivariable model was 0.94 for prediction of BCR; the AUC of pathologic features alone was 0.89 (p = 0.001).

CONCLUSION

A model integrating whole-lesion ADC metrics had significantly higher performance for prediction of BCR than did standard pathologic features alone and may help guide postoperative prognostic assessments and decisions regarding adjuvant therapy.

Sample size considerations for the external validation of a multivariable prognostic model: a resampling study

After developing a prognostic model, it is essential to evaluate the performance of the model in samples independent from those used to develop the model, which is often referred to as external validation. However, despite its importance, very little is known about the sample size requirements for conducting an external validation. Using a large real data set and resampling methods, we investigate the impact of sample size on the performance of six published prognostic models. Focussing on unbiased and precise estimation of performance measures (e.g. the c-index, D statistic and calibration), we provide guidance on sample size for investigators designing an external validation study. Our study suggests that externally validating a prognostic model requires a minimum of 100 events and ideally 200 (or more) events.

The prognostic role of preoperative serum C-reactive protein in predicting the biochemical recurrence in patients treated with radical prostatectomy

BACKGROUND

To assess the prognostic value of preoperative C-reactive protein (CRP) serum levels for prognostication of biochemical recurrence (BCR) after radical prostatectomy (RP) in a large multi-institutional cohort.

METHODS

Data from 7205 patients treated with RP at five institutions for clinically localized prostate cancer (PCa) were retrospectively analyzed. Preoperative serum levels of CRP within 24 h before surgery were evaluated. A CRP level ⩾0.5 mg dl(-1) was considered elevated. Associations of elevated CRP with BCR were evaluated using univariable and multivariable Cox proportional hazards regression models. Harrel's C-index was used to assess prognostic accuracy (PA).

RESULTS

Patients with higher Gleason score on biopsy and RP, extracapsular extension, seminal vesicle invasion, lymph node metastasis, and positive surgical margins status had a significantly elevated preoperative CRP compared to those without these features. Patients with elevated CRP had a lower 5-year BCR survival proportion as compared to those with normal CRP (55% vs 76%, respectively, P<0.0001). In pre- and postoperative multivariable models that adjusted for standard clinical and pathologic features, elevated CRP was independently associated with BCR (P<0.001). However, the addition of preoperative CRP did not improve the accuracy of the standard pre- and postoperative models for prediction of BCR (70.9% vs 71% and 78.9% vs 78.7%, respectively).

CONCLUSIONS

Preoperative CRP is elevated in patients with pathological features of aggressive PCa and BCR after RP. While CRP has independent prognostic value, it does not add prognostically or clinically significant information to standard predictors of outcomes.

Prostate-specific antigen density as a parameter for the prediction of positive lymph nodes at radical prostatectomy

Objective:

The aim was to determine the prognostic ability of Partin's tables for a patient collective undergoing radical prostatectomy (RP) and to evaluate the association of prostate-specific antigen (PSA) density (PSAD) and postoperative lymph node status.

Methods:

From 1999 to 2006, 393 consecutive patients underwent RP at our Urology department. Patients with Gleason scores < 6, clinical stages >T2c or neoadjuvant hormonal therapy were excluded. Preoperative PSA, biopsy results, digital rectal examination, and prostate size at transrectal ultrasound were recorded. Risk stratification according to the Partin scoring system was performed. Postoperative results were compared with preoperative risk estimation. Univariate and multivariate statistical analysis about prediction of postoperative lymph node status was performed.

Results:

Lymph node invasion (LNI) was found in 36 patients (9.16%). Kendall's rank correlation analysis revealed a significant association between the number of removed LN and LNI (P = 0.016). Patients with LNI had a significantly higher preoperative PSA and PSAD. Preoperative Gleason score was a significant predictor of LNI. The Partin tables' prediction of organ confined stages, extraprostatic extension, and seminal vesicle invasion was in line with the pathological findings in our collective. PSAD was a significant predictor of LNI in univariate and multivariate analysis.

Conclusion:

The most widely used nomogram is of high value in therapy decision-making, although it remains an auxiliary means. Considering the performance of lymph node dissection, surgeons should be aware of the specifics of the applied nomogram. PSAD appears as a useful adjunctive parameter for preoperative prostate risk estimation and warrants further evaluation.

Prediction model for early biochemical recurrence after radical prostatectomy based on the Cancer of the Prostate Risk Assessment score and the presence of secondary circulating prostate cells

OBJECTIVE

To establish a prediction model for early biochemical recurrence based on the Cancer of the Prostate Risk Assessment (CAPRA) score and the presence of secondary circulating prostate cells (CPCs).

PATIENTS AND METHODS

We conducted a prospective single-centre study of men who underwent radical prostatectomy as monotherapy for prostate cancer. Clinicopathological findings were used to calculate the CAPRA score. At 90 days after surgery, blood was taken for CPC detection, mononuclear cells were obtained using differential gel centrifugation, and CPCs were identified using immunocytochemistry. A CPC was defined as a cell expressing prostate-specific antigen (PSA) but not CD45. The CPC test results were defined as positive or negative. Patients were followed up for up to 5 years and biochemical recurrence was defined as a PSA level >0.2 ng/mL. The validity of the CAPRA score was calibrated using partial validation, and Cox proportional hazard regression to build three models: a CAPRA score model, a CPC model and a CAPRA/CPC combined model.

RESULTS

A total of 321 men, with a mean age of 65.5 years, participated in the study. After 5 years of follow-up the biochemical recurrence-free survival rate was 98.55%. For the model that included CAPRA score there was a hazard ratio (HR) of 7.66, for the CPC model there was an HR of 34.52 and for the combined model there were HRs of 2.60 for CAPRA score and 22.5 for CPC. Using the combined model, 23% of men changed from the low-risk to the high-risk category, or vice versa.

CONCLUSION

The incorporation of CPC detection significantly improved the model's discriminative ability in establishing the probability of biochemical recurrence; patients in the high-risk group according to CAPRA score who are negative for CPCs have a much better prognosis. The addition of CPC detection gives clinically significant information to aid the decision on who may be eligible for adjuvant therapy.

Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis

BACKGROUND

Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve predictive accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis.

DESIGN

A systematic literature search of Medline was conducted to identify PCa predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model.

RESULTS

The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz, Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported.

CONCLUSIONS

Risk prediction models improve the predictive accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.

Preoperative predictive model of recovery of urinary continence after radical prostatectomy

OBJECTIVE

To build a predictive model of urinary continence recovery after radical prostatectomy (RP) that incorporates magnetic resonance imaging (MRI) parameters and clinical data.

PATIENTS AND METHODS

We conducted a retrospective review of data from 2,849 patients who underwent pelvic staging MRI before RP from November 2001 to June 2010. We used logistic regression to evaluate the association between each MRI variable and continence at 6 or 12 months, adjusting for age, body mass index (BMI) and American Society of Anesthesiologists (ASA) score, and then used multivariable logistic regression to create our model. A nomogram was constructed using the multivariable logistic regression models.

RESULTS

In all, 68% (1,742/2,559) and 82% (2,205/2,689) regained function at 6 and 12 months, respectively. In the base model, age, BMI and ASA score were significant predictors of continence at 6 or 12 months on univariate analysis (P < 0.005). Among the preoperative MRI measurements, membranous urethral length, which showed great significance, was incorporated into the base model to create the full model. For continence recovery at 6 months, the addition of membranous urethral length increased the area under the curve (AUC) to 0.664 for the validation set, an increase of 0.064 over the base model. For continence recovery at 12 months, the AUC was 0.674, an increase of 0.085 over the base model.

CONCLUSION

Using our model, the likelihood of continence recovery increases with membranous urethral length and decreases with age, BMI and ASA score. This model could be used for patient counselling and for the identification of patients at high risk for urinary incontinence in whom to study changes in operative technique that improve urinary function after RP.

Enhanced expression of centromere protein F predicts clinical progression and prognosis in patients with prostate cancer

Centromere protein F (CENPF) is a protein associated with the centromere-kinetochore complex and chromosomal segregation during mitosis. Previous studies have demonstrated that the upregulation of CENPF may be used as a proliferation marker of malignant cell growth in tumors. The overexpression of CENPF has also been reported to be associated with a poor prognosis in human cancers. However, the clinical significance of CENPF in prostate cancer (PCa) has not yet been fully elucidated. Thus, the aim of the present study was to determine the association of CENPF with tumor progression and prognosis in patients with PCa. The expression of CENPF at the protein level in human PCa and non-cancerous prostate tissues was detected by immunohistochemical analysis, which was further validated using a microarray-based dataset (NCBI GEO accession no: GSE21032) at the mRNA level. Subsequently, the association of CENPF expression with the clinicopathological characteristics of the patients with PCa was statistically analyzed. Immunohistochemistry and dataset analysis revealed that CENPF expression was significantly increased in the PCa tissues compared with the non-cancerous prostate tissues [immunoreactivity score (IRS): PCa, 177.98 ± 94.096 vs. benign, 121.30 ± 89.596, P < 0.001; mRNA expression in the dataset: PCa, 5.67 ± 0.47 vs. benign, 5.40 ± 0.11; P < 0.001]. Additionally, as revealed by the dataset, the upregulation of CENPF mRNA expression in the PCa tissues significantly correlated with a higher Gleason score (GS, P = 0.005), an advanced pathological stage (P = 0.008), the presence of metastasis (P < 0.001), a shorter overall survival (P=0.003) and prostate-specific antigen (PSA) failure (P < 0.001). Furthermore, both univariate and multivariate analyses revealed that the upregulation of CENPF was an independent predictor of poor biochemical recurrence (BCR)-free survival (P < 0.001 and P = 0.012, respectively). Our data suggest that the increased expression of CENPF plays an important role in the progression of PCa. More importantly, the increased expression of CENPF may efficiently predict poor BCR-free survival in patients with PCa.

Prostate cancer risk estimation tool use by members of the American Urological Association: a survey based study

PURPOSE

Prostate cancer risk estimation tools have been developed to help guide patients and physicians with clinical decision making across all disease states. We assessed use patterns of these tools using an online survey sent to AUA (American Urological Association) members.

MATERIALS AND METHODS

We distributed a 21-question online survey to 5,674 AUA members to query prostate cancer risk estimation tool use. The survey was divided into 4 categories, including 1) demographics, 2) prebiopsy risk assessment, 3) pretreatment risk assessment and 4) risk estimation tool use.

RESULTS

A total of 565 members (10%) responded to the online survey, of whom 31% reported using a risk estimation tool in the prebiopsy decision setting. Providers who spent more than 20 minutes counseling patients were more likely to use a risk estimation tool (OR 2.2, p <0.01). After the prostate cancer diagnosis 70% of providers used a risk estimation tools to guide treatment recommendations. The total time spent counseling a patient (greater than 30 minutes) and the number of years in practice (fewer than 10) predicted prostate cancer risk tool use (OR 2.4, p <0.01 and 3.4, p <0.01, respectively).

CONCLUSIONS

AUA respondents use risk estimation tools more frequently in the pretreatment setting than in the prebiopsy setting. The time spent counseling patients and the time since graduation from residency predicted the likelihood of using risk estimation tools.

Is pathology necessary to predict mortality among men with prostate-cancer?

Background

Statistical models developed using administrative databases are powerful and inexpensive tools for predicting survival. Conversely, data abstraction from chart review is time-consuming and costly. Our aim was to determine the incremental value of pathological data obtained from chart abstraction in addition to information acquired from administrative databases in predicting all-cause and prostate cancer (PC)-specific mortality.

Methods

We identified a cohort of men with diabetes and PC utilizing population-based data from Ontario. We used the c-statistic and net-reclassification improvement (NRI) to compare two Cox- proportional hazard models to predict all-cause and PC-specific mortality. The first model consisted of covariates from administrative databases: age, co-morbidity, year of cohort entry, socioeconomic status and rural residence. The second model included Gleason grade and cancer volume in addition to all aforementioned variables.

Results

The cohort consisted of 4001 patients. The accuracy of the admin-data only model (c-statistic) to predict 5-year all-cause mortality was 0.7 (95% CI 0.69-0.71). For the extended model (including pathology information) it was 0.74 (95% CI 0.73-0.75). This corresponded to a change in category of predicted probability of survival among 14.8% in the NRI analysis.

The accuracy of the admin-data model to predict 5-year PC specific mortality was 0.76 (95% CI 0.74-0.78). The accuracy of the extended model was 0.85 (95% CI 0.83-0.87). Corresponding to a 28% change in the NRI analysis.

Conclusions

Pathology chart abstraction, improved the accuracy in predicting all-cause and PC-specific mortality. The benefit is smaller for all-cause mortality, and larger for PC-specific mortality.

External validation of the CAPRA-S score to predict biochemical recurrence, metastasis and mortality after radical prostatectomy in a European cohort

PURPOSE

The CAPRA-S score predicts prostate cancer recurrence based on pathological information from radical prostatectomy. To our knowledge CAPRA-S has never been externally validated in a European cohort. We independently validated CAPRA-S in a single institution European database.

MATERIALS AND METHODS

The study cohort comprised 14,532 patients treated with radical prostatectomy between January 1992 and August 2012. Prediction of biochemical recurrence, metastasis and cancer specific mortality by CAPRA-S was assessed by Kaplan-Meier analysis and the c-index. CAPRA-S performance to predict biochemical recurrence was evaluated by calibration plot and decision curve analysis.

RESULTS

Median followup was 50.8 months (IQR 25.0-96.0). Biochemical recurrence developed in 20.3% of men at a median of 21.2 months (IQR 7.7-44.9). When stratifying patients by CAPRA-S risk group, estimated 5-year biochemical recurrence-free survival was 91.4%, 70.4% and 29.3% in the low, intermediate and high risk groups, respectively. The CAPRA-S c-index to predict biochemical recurrence, metastasis and cancer specific mortality was 0.80, 0.85 and 0.88, respectively. Metastasis developed in 417 men and 196 men died of prostate cancer.

CONCLUSIONS

The CAPRA-S score was accurate when applied in a European study cohort. It predicted biochemical recurrence, metastasis and cancer specific mortality after radical prostatectomy with a c-index of greater than 0.80. The score can be valuable in regard to decision making for adjuvant therapy.

A systematic literature review of life expectancy prediction tools for patients with localized prostate cancer

PURPOSE

We aimed to develop a clinical decision support tool for clinicians counseling patients with localized prostate cancer. The tool would provide estimates of patient life expectancy based on age, comorbidities and tumor characteristics. We reviewed the literature to find suitable prediction models.

MATERIALS AND METHODS

We searched the literature for prediction models for life expectancy. Models were evaluated in terms of whether they provided an estimate of risk, incorporated comorbidities, were clinically feasible and gave plausible estimates. Clinical feasibility was defined in terms of whether the model provided coefficients and could be used in the initial consultation for men across a wide age range without an undue burden of data gathering.

RESULTS

Models in the literature were characterized by the use of life years rather than a risk of death, questionable approaches to comorbidities, implausible estimates, questionable recommendations and poor clinical feasibility. We found tools that involved applying an unvalidated approach to assessing comorbidities to a clearly erroneous life expectancy table, or requiring that a treatment decision be made before life expectancy could be calculated, or giving highly implausible estimates such as a substantial risk of prostate cancer specific mortality even for a highly comorbid 80-year-old with Gleason 6 disease.

CONCLUSIONS

We found gross deficiencies in current tools that predict risk of death from other causes. No existing model was suitable for implementation in our clinical decision support system.

Personalizing colorectal cancer screening: a systematic review of models to predict risk of colorectal neoplasia

BACKGROUND & AIMS

A valid risk prediction model for colorectal neoplasia would allow patients to be screened for colorectal cancer (CRC) on the basis of risk. We performed a systematic review of studies reporting risk prediction models for colorectal neoplasia.

METHODS

We conducted a systematic search of MEDLINE, Scopus, and Cochrane Library databases from January 1990 through March 2013 and of references in identified studies. Case-control, cohort, and cross-sectional studies that developed or attempted to validate a model to predict risk of colorectal neoplasia were included. Two reviewers independently extracted data and assessed model quality. Model quality was considered to be good for studies that included external validation, fair for studies that included internal validation, and poor for studies with neither.

RESULTS

Nine studies developed a new prediction model, and 2 tested existing models. The models varied with regard to population, predictors, risk tiers, outcomes (CRC or advanced neoplasia), and range of predicted risk. Several included age, sex, smoking, a measure of obesity, and/or family history of CRC among the predictors. Quality was good for 6 models, fair for 2 models, and poor for 1 model. The tier with the largest population fraction (low, intermediate, or high risk) depended on the model. For most models that defined risk tiers, the risk difference between the highest and lowest tier ranged from 2-fold to 4-fold. Two models reached the 0.70 threshold for the C statistic, typically considered to indicate good discriminatory power.

CONCLUSIONS

Most current colorectal neoplasia risk prediction models have relatively weak discriminatory power and have not demonstrated generalizability. It remains to be determined how risk prediction models could inform CRC screening strategies.

Prostate cancer nomograms: a review of their use in cancer detection and treatment

As prostate cancer treatment discussions have grown more complex, increasing numbers of nomograms to guide decision-making have been found in the literature. Such nomograms can influence every step in the prostate cancer therapeutic process, from determining the need for biopsy to the need for adjuvant therapy. With a properly counseled patient who is aware of the limitations of nomograms, such tools assist in the shared decision-making that characterizes modern informed consent.

Prognostic DNA methylation markers for prostate cancer

Prostate cancer (PC) is the most commonly diagnosed neoplasm and the third most common cause of cancer-related death amongst men in the Western world. PC is a clinically highly heterogeneous disease, and distinction between aggressive and indolent disease is a major challenge for the management of PC. Currently, no biomarkers or prognostic tools are able to accurately predict tumor progression at the time of diagnosis. Thus, improved biomarkers for PC prognosis are urgently needed. This review focuses on the prognostic potential of DNA methylation biomarkers for PC. Epigenetic changes are hallmarks of PC and associated with malignant initiation as well as tumor progression. Moreover, DNA methylation is the most frequently studied epigenetic alteration in PC, and the prognostic potential of DNA methylation markers for PC has been demonstrated in multiple studies. The most promising methylation marker candidates identified so far include PITX2, C1orf114 (CCDC181) and the GABRE~miR-452~miR-224 locus, in addition to the three-gene signature AOX1/C1orf114/HAPLN3. Several other biomarker candidates have also been investigated, but with less stringent clinical validation and/or conflicting evidence regarding their possible prognostic value available at this time. Here, we review the current evidence for the prognostic potential of DNA methylation markers in PC.

Implications of the new AUA guidelines on prostate cancer detection in the U.S

In 2012, the U.S. Preventive Services Task Force (USPSTF) issued a blanket "D" recommendation against all prostate-specific antigen (PSA)-based early detection efforts for prostate cancer, reflecting critical misinterpretations of the major evidence regarding benefits and harms of such testing. Against the backdrop of the ensuing controversy, in 2013 the American Urological Association (AUA) published a new, methodologically rigorous guideline. This guideline recommended that men aged 55-69 be offered biennial screening in the setting of shared decision-making, that men under 40 or over 69 years of age should not be screened routinely, and that evidence was insufficient to recommend screening for men aged 40-54 years. While it has received criticism with regard to the age-based recommendations, the AUA guideline reflects a far better and more balanced presentation of the available evidence than the USPSTF statement. However, because the USPSTF is far more influential than the AUA among primary care providers, the ultimate impact of the new AUA guideline on practice patterns may be limited. Optimizing early detection practices should involve consensus-building incorporating both primary care and specialist input, with the goals of minimizing overtreatment of low-risk disease while continuing to reduce prostate cancer mortality rates through early detection and aggressive management of high-risk disease.

Pre- and post-operative nomograms to predict recurrence-free probability in korean men with clinically localized prostate cancer

Objectives

Although the incidence of prostate cancer (PCa) is rapidly increasing in Korea, there are few suitable prediction models for disease recurrence after radical prostatectomy (RP). We established pre- and post-operative nomograms estimating biochemical recurrence (BCR)-free probability after RP in Korean men with clinically localized PCa.

Patients and Methods

Our sampling frame included 3,034 consecutive men with clinically localized PCa who underwent RP at our tertiary centers from June 2004 through July 2011. After inappropriate data exclusion, we evaluated 2,867 patients for the development of nomograms. The Cox proportional hazards regression model was used to develop pre- and post-operative nomograms that predict BCR-free probability. Finally, we resampled from our study cohort 200 times to determine the accuracy of our nomograms on internal validation, which were designated with concordance index (c-index) and further represented by calibration plots.

Results

Over a median of 47 months of follow-up, the estimated BCR-free rate was 87.8% (1 year), 83.8% (2 year), and 72.5% (5 year). In the pre-operative model, Prostate-Specific Antigen (PSA), the proportion of positive biopsy cores, clinical T3a and biopsy Gleason score (GS) were independent predictive factors for BCR, while all relevant predictive factors (PSA, extra-prostatic extension, seminal vesicle invasion, lymph node metastasis, surgical margin, and pathologic GS) were associated with BCR in the post-operative model. The c-index representing predictive accuracy was 0.792 (pre-) and 0.821 (post-operative), showing good fit in the calibration plots.

Conclusions

In summary, we developed pre- and post-operative nomograms predicting BCR-free probability after RP in a large Korean cohort with clinically localized PCa. These nomograms will be provided as the mobile application-based SNUH Prostate Cancer Calculator. Our nomograms can determine patients at high risk of disease recurrence after RP who will benefit from adjuvant therapy.

Prostate cancer: role of pretreatment multiparametric 3-T MRI in predicting biochemical recurrence after radical prostatectomy

OBJECTIVE

The purpose of this study is to retrospectively investigate whether pretreatment multiparametric MRI findings can predict biochemical recurrence in patients who underwent radical prostatectomy (RP) for localized prostate cancer.

MATERIALS AND METHODS

In this study, 282 patients with biopsy-proven prostate cancer who received RP underwent pretreatment MRI using a phased-array coil at 3 T, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). MRI variables included apparent tumor presence on combined imaging sequences, extracapsular extension, and tumor size on DWI or DCE-MRI. Clinical variables included baseline prostate-specific antigen (PSA) level, clinical stage, and Gleason score at biopsy. The relationship between clinical and imaging variables and biochemical recurrence was evaluated using Cox regression analysis.

RESULTS

After a median follow-up of 26 months, biochemical recurrence developed in 61 patients (22%). Univariate analysis revealed that all the imaging and clinical variables were significantly associated with biochemical recurrence (p < 0.01). On multivariate analysis, however, baseline PSA level (p = 0.002), Gleason score at biopsy (p = 0.024), and apparent tumor presence on combined T2WI, DWI, and DCE-MRI (p = 0.047) were the only significant independent predictors of biochemical recurrence. Of the independent predictors, apparent tumor presence on combined T2WI, DWI, and DCE-MRI showed the highest hazard ratio (2.38) compared with baseline PSA level (hazard ratio, 1.05) and Gleason score at biopsy (hazard ratio, 1.34).

CONCLUSION

The apparent tumor presence on combined T2WI, DWI, and DCE-MRI of pretreatment MRI is an independent predictor of biochemical recurrence after RP. This finding may be used to construct a predictive model for biochemical recurrence after surgery.

Random biopsy: when, how many and where to take the cores?

PURPOSE

The optimal random prostate biopsy scheme (PBx) in the initial and repeated setting is still an issue of controversy. We performed an analysis of the recent literature about the prostate biopsy techniques.

METHODS

We performed a clinical and critical literature review by searching MEDLINE database from January 2005 up to January 2014. Electronic searches were limited to the English language, and the keywords prostate cancer, prostate biopsy, transrectal ultrasound, transperineal prostate biopsy were used.

RESULTS

Prostate biopsy strategy in initial setting. According to the literature and the major international guidelines, the recommended approach in initial setting is still the extended scheme (EPBx) (12 cores). However, there is now a growing evidence in the literature that (a) saturation PBx (>20 cores) (SPBx) might be indicated in patients with PSA <10 ng/ml or low PSA density or large prostate and (b) an individualized approach with more than 12 cores according to the clinical characteristics of the patients may optimize cancer detection in the single patient. Moreover, in the era of multi-parametric MRI (mpMRI), EPBx or SPBX may be substituted by mpMRI-targeted biopsies that have demonstrated superiority over systematic random biopsies for the detection of clinically significant disease and representation of disease burden, while deploying fewer cores. Prostate biopsy strategy in repeat setting. How and how many cores should be taken in the different scenarios in the repeated setting is still unclear. SPBx clearly improves cancer detection if clinical suspicion persists after previous biopsy with negative findings and is able to provide an accurate prediction of prostate tumour volume and grade. Nevertheless, international guidelines do not strongly recommended SPBx in all situations of repeated setting. In the active surveillance and in focal therapy protocols, the optimal schemes have to be defined.

CONCLUSIONS

The course of PBx has changed significantly from sextant biopsies to systematic and from extended to SPBx schemes. The issue about the number and location of the cores is still a matter of debate both in initial and in repeat setting. At present, EPBx is sufficient in most of the cases to provide adequate diagnosis and prostate cancer characterization in the initial setting, while SPBx seems to be necessary in repeat setting. The PBx schemes are evolving also because the scenario in which a PBx is necessary is changing. Random prostate PBx do not represent the future, while imaging target biopsy are becoming more popular.

miR-19, miR-345, miR-519c-5p serum levels predict adverse pathology in prostate cancer patients eligible for active surveillance

Serum microRNAs hold great promise as easily accessible and measurable biomarkers of disease. In prostate cancer, serum miRNA signatures have been associated with the presence of disease as well as correlated with previously validated risk models. However, it is unclear whether miRNAs can provide independent prognostic information beyond current risk models. Here, we focus on a group of low-risk prostate cancer patients who were eligible for active surveillance, but chose surgery. A major criteria for the low risk category is a Gleason score of 6 or lower based on pre-surgical biopsy. However, a third of these patients are upgraded to Gleason 7 on post surgical pathological analysis. Both in a discovery and a validation cohort, we find that pre-surgical serum levels of miR-19, miR-345 and miR-519c-5p can help identify these patients independent of their pre-surgical age, PSA, stage, and percent biopsy involvement. A combination of the three miRNAs increased the area under a receiver operator characteristics curve from 0.77 to 0.94 (p<0.01). Also, when combined with the CAPRA risk model the miRNA signature significantly enhanced prediction of patients with Gleason 7 disease. In-situ hybridizations of matching tumors showed miR-19 upregulation in transformed versus normal-appearing tumor epithelial, but independent of tumor grade suggesting an alternative source for the increase in serum miR-19a/b levels or the release of pre-existing intracellular miR-19a/b upon progression. Together, these data show that serum miRNAs can predict relatively small steps in tumor progression improving the capacity to predict disease risk and, therefore, potentially drive clinical decisions in prostate cancer patients. It will be important to validate these findings in a larger multi-institutional study as well as with independent methodologies.

Genetic variants in microRNAs and microRNA target sites predict biochemical recurrence after radical prostatectomy in localized prostate cancer

Recent evidence indicates that microRNAs might participate in prostate cancer initiation, progression and treatment response. Germline variations in microRNAs might alter target gene expression and modify the efficacy of prostate cancer therapy. To determine whether genetic variants in microRNAs and microRNA target sites are associated with the risk of biochemical recurrence (BCR) after radical prostatectomy (RP). We retrospectively studied two independent cohorts composed of 320 Asian and 526 Caucasian men with pathologically organ-confined prostate cancer who had a median follow-up of 54.7 and 88.8 months after RP, respectively. Patients were systematically genotyped for 64 single-nucleotide polymorphisms (SNPs) in microRNAs and microRNA target sites, and their prognostic significance on BCR was assessed by Kaplan-Meier analysis and Cox regression model. After adjusting for known clinicopathologic risk factors, two SNPs (MIR605 rs2043556 and CDON rs3737336) remained associated with BCR. The numbers of risk alleles showed a cumulative effect on BCR [perallele hazard ratio (HR) 1.60, 95% confidence interval (CI) 1.16-2.21, p for trend = 0.005] in Asian cohort, and the risk was replicated in Caucasian cohort (HR 1.55, 95% CI 1.15-2.08, p for trend = 0.004) and in combined analysis (HR 1.57, 95% CI 1.26-1.96, p for trend <0.001). Results warrant replication in larger cohorts. This is the first study demonstrating that SNPs in microRNAs and microRNA target sites can be predictive biomarkers for BCR after RP.