Development and validation of a nomogram predicting the outcome of prostate biopsy based on patient age, digital rectal examination and serum prostate specific antigen

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study

PURPOSE

To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed.

MATERIALS AND METHODS

A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected prostate cancer with aged 40 to 85 years, and with PSA levels ranging from 2.5 to 10 ng/mL were analyzed.

RESULTS

Among 626 patients, 38.82% (243/626) and 22.52% (141/626) were diagnosed with prostate cancer and clinically significant prostate cancer, respectively. In the PSA 2.5 to 4 ng/mL group, the areas under the curve (AUCs) of the nomograms for overall prostate cancer and clinically significant prostate cancer were 0.796 (0.727-0.866; p<0.001), and 0.697 (0.598-0.795; p=0.001), respectively. In the PSA 4 to 10 ng/mL group, the AUCs of nomograms for overall prostate cancer and clinically significant prostate cancer were 0.812 (0.783-0.842; p<0.001), and 0.839 (0.810-0.869; p<0.001), respectively.

CONCLUSIONS

Even though external validations are necessary, a nomogram using PHI might improve the prediction of prostate cancer, reducing the need for prostate biopsies.

Clinical characteristics and overall survival prognostic nomogram for metaplastic breast cancer

Background

Metaplastic breast cancer (MBC) is a rare breast tumor and the prognostic factors for survival in patients still remain controversial. This study aims to develop and validate a nomogram to predict the overall survival (OS) of patients with MBC.

Methods

We searched the Surveillance, Epidemiology, and End Results (SEER) database for data about patients including metaplastic breast cancer and infiltrating ductal carcinoma (IDC) from 2010 to 2018. The survival outcomes of patients between MBC and IDC were analyzed and compared with the Kaplan-Meier (KM) method. MBC patients were randomly allocated to the training set and validation I set by a ratio of eight to two. Meanwhile, the performance of this model was validated again by the validation II set, which consisted of MBC patients from the Union Hospital of Fujian Medical University between 2010 and 2018. The independent prognostic factors were selected by univariate and multivariate Cox regression analyses. The nomogram was constructed to predict individual survival outcomes for MBC patients. The discriminative power, calibration, and clinical effectiveness of the nomogram were evaluated by the concordance index (C-index), the receiver operating characteristic (ROC) curve, and the decision curve analysis (DCA).

Results

MBC had a significantly higher T stage (T2 and above accounting for 75.1% vs 39.9%), fewer infiltrated lymph nodes (N0 accounted for 76.2% vs 67.7%), a lower proportion of ER (22.2% vs 81.2%), PR (13.6% vs 71.4%), and HER-2(6.7% vs 17.7%) positive, radiotherapy(51.6% vs 58.0%) but more chemotherapy(67.5% vs 44.7%), and a higher rate of mastectomy(53.2% vs 36.8%), which was discovered when comparing the clinical baseline data between MBC and IDC. Age at diagnosis, T, N, and M stage, as well as surgery and radiation treatment, were all significant independent prognostic factors for overall survival (OS). In the validation I cohort, the nomogram's C-index (0.769 95% CI 0.710 -0.828) was indicated to be considerably higher than the standard AJCC model's (0.700 95% CI 0.644 -0.756). Nomogram's great predictive capability capacity further was supported by the comparatively high C-index of the validation II sets (0.728 95%CI 0.588-0.869).

Conclusions

Metaplastic breast cancer is more aggressive, with a worse clinical prognosis than IDC. This nomogram is recommended for patients with MBC, both American and Chinese, which can help clinicians make more accurate individualized survival analyses.

Diagnostic evaluation and incorporation of PSA density and the prostate imaging and data reporting system (PIRADS) version 2 classification in risk-nomograms for prostate cancer

PURPOSE

The diagnostic approach for prostate cancer still depends on PSA and DRE.

OBJECTIVES

to evaluate the diagnostic validity of PSA-Density and PIRADSv2 as diagnostic tests regarding biopsy results, and to design nomograms that include all diagnostic variables for malignancy, significant tumor (ST) and high-grade tumor.

METHODS

Cross-sectional study which included men with PSA ≥ 4 ng/ml and/or suspicious DRE, PIRADSv2 ≥ 3 lesions on multiparametric MRI and prostate biopsy. The gold standard test was the maximum ISUP of the targeted biopsy per patient (malignancy: ISUP ≥ 1, ST: ISUP ≥ 2, high-grade tumor: ISUP ≥ 4). Association and logistic regression tests were used and diagnostic validity parameters using PSA-Density and PIRADSv2 classification was analyzed. Nomograms were designed for malignancy, ST, and high-grade tumor using the best model selection procedure from all possible equations.

RESULTS

336 men with median age, PSA and PSA-Density of 67.7 years (IQR:12.6), 6.3 ng/ml (IQR:3.3) and 0.12 ng/ml/cc (IQR:0.10), respectively; 63 index lesions were PIRADS3, 204 PIRADS4, and 69 PIRADS5. 65.8% and 37.8% were malignant and ST, respectively. The significant positive association highlighted between malignancy and ST with age, DRE, PSA-Density and PIRADSv2. PSA-Density and PIRADSv2 ≥ 3 presented the highest sensitivity to detect malignancy, and their combination showed sensitivity nearly 95% (AUC:0.803). Nomograms for malignancy and ST included the variables age, DRE, PSA-Density, and PIRADSv2 with a sensitivity closely 91% (AUC:0.833), and a specificity of almost 85% for ST, exposing risk < 5% for ST when PSA-Density is < 0.15, not suspicious DRE and PIRADS3.

CONCLUSION

PSA-Density and PIRADSv2 classification in risk nomograms can provide highly relevant information to increase the accuracy in the diagnosis of PC and ST.

Development and validation of a multi-parameter nomogram for predicting prostate cancer: a retrospective analysis from Handan Central Hospital in China

Background

To explore the possible predicting factors related to prostate cancer and develop a validated nomogram for predicting the probability of patients with prostate cancer.

Method

Clinical data of 697 patients who underwent prostate biopsy in Handan Central Hospital from January 2014 to January 2020 were retrospectively collected. Cases were randomized into two groups: 80% (548 cases) as the development group, and 20% (149 cases) as the validation group. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for prostate cancer. The nomogram prediction model was generated using the finalized independent risk factors. Decision curve analysis (DCA) and the area under receiver operating characteristics curve (ROC) of both development group and validation group were calculated and compared to validate the accuracy and efficiency of the nomogram prediction model. Clinical utility curve (CUC) helped to decide the desired cut-off value for the prediction model. The established nomogram with Prostate Cancer Prevention Trial Derived Cancer Risk Calculator (PCPT-CRC) and other domestic prediction models using the entire study population were compared.

Results

The independent risk factors determined through univariate and multivariate logistic regression analyses were: age, tPSA, fPSA, PV, DRE, TRUS and BMI. Nomogram prediction model was developed with the cut-off value of 0.31. The AUC of development group and validation group were 0.856 and 0.797 respectively. DCA exhibits consistent observations with the findings. Through validating our prediction model as well as other three domestic prediction models based on the entire study population of 697 cases, our prediction model demonstrated significantly higher predictive value than all the other models.

Conclusion

The nomogram for predicting prostate cancer can facilitate more accurate evaluation of the probability of having prostate cancer, and provide better ground for prostate biopsy.

Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing

Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical’s mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Prostate cancer is the fourth most common cause of cancer in men in Korea, and there has been a rapid increase in cases. In the present study, we constructed a risk prediction model for prostate cancer using representative data from Korea. Participants who completed health examinations in 2009, based on the Korean National Health Insurance database, were eligible for the present study. The crude and adjusted risks were explored with backward selection using the Cox proportional hazards model to identify possible risk variables. Risk scores were assigned based on the adjusted hazard ratios, and the standardized points for each risk factor were proportional to the β-coefficient. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability was assessed by plotting the mean predicted probability against the mean observed probability of prostate cancer. Among the candidate predictors, age, smoking intensity, body mass index, regular exercise, presence of type 2 diabetes mellitus, and hypertension were included. Our risk prediction model showed good discrimination (c-statistic: 0.826, 95% confidence interval: 0.821-0.832). The relationship between model predictions and actual prostate cancer development showed good correlation in the calibration plot. Our prediction model for individualized prostate cancer risk in Korean men showed good performance. Using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding prostate cancer screening.

Development and validation of a nomogram for predicting prostate cancer in patients with PSA ≤ 20 ng/mL at initial biopsy

The aim of this study was to construct a nomogram for predicting prostate cancer (PCa) in patients with PSA ≤ 20 ng/mL at initial biopsy.The patients with PSA ≤ 20 ng/mL who underwent prostate biopsy were retrospectively included in this study. The nomogram was developed based on predictors for PCa, which were assessed by multivariable logistic regression analysis. The receiver operating characteristic curve, calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.This retrospective study included 691 patients, who were divided into training set (505 patients) and validation set (186 patients). The nomogram was developed based on the multivariable logistic regression model, including age, total PSA, free PSA, and prostate volume. It had a high area under the curve of 0.857, and was well verified in validation set. Calibration plots and DCA further validated its discrimination and potential clinical benefits. Applying the cut-off value of 15%, our nomogram would avoid 42.5% of unnecessary biopsies while miss only 4.4% of PCa patients.The nomogram provided high predictive accuracy for PCa in patients with PSA ≤ 20 ng/mL at initial biopsy, which could be used to avoid the unnecessary biopsies in clinical practice.

In1-Ghrelin Splicing Variant as a Key Element in the Pathophysiological Association Between Obesity and Prostate Cancer

CONTEXT

Recent studies emphasize the importance of considering the metabolic status to develop personalized medicine approaches. This is especially relevant in prostate cancer (PCa), wherein the diagnostic capability of prostate-specific antigen (PSA) dramatically drops when considering patients with PSA levels ranging from 3 to 10 ng/mL, the so-called grey zone. Hence, additional noninvasive diagnostic and/or prognostic PCa biomarkers are urgently needed, especially in the metabolic-status context.

OBJECTIVE

To assess the potential relation of urine In1-ghrelin (a ghrelin-splicing variant) levels with metabolic-related/pathological conditions (eg, obesity, diabetes, body mass index, insulin and glucose levels) and to define its potential clinical value in PCa (diagnostic/prognostic capacity) and relationship with PCa risk in patients with PSA in the grey zone.

METHODS

Urine In1-ghrelin levels were measured by radioimmunoassay in a clinically, metabolically, pathologically well-characterized cohort of patients without (n = 397) and with (n = 213) PCa with PSA in the grey zone.

RESULTS

Key obesity-related factors associated with PCa risk (BMI, diabetes, glucose and insulin levels) were strongly correlated to In1-ghrelin levels. Importantly, In1-ghrelin levels were higher in PCa patients compared to control patients with suspect of PCa but negative biopsy). Moreover, high In1-ghrelin levels were associated with increased PCa risk and linked to PCa aggressiveness (eg, tumor stage, lymphovascular invasion). In1-ghrelin levels added significant diagnostic value to a clinical model consisting of age, suspicious digital rectal exam, previous biopsy, and PSA levels. Furthermore, a multivariate model consisting of clinical and metabolic variables, including In1-ghrelin levels, showed high specificity and sensitivity to diagnose PCa (area under the receiver operating characteristic curve = 0.740).

CONCLUSIONS

Urine In1-ghrelin levels are associated with obesity-related factors and PCa risk and aggressiveness and could represent a novel and valuable noninvasive PCa biomarker, as well as a potential link in the pathophysiological relationship between obesity and PCa.

A risk model for detecting clinically significant prostate cancer based on bi-parametric magnetic resonance imaging in a Japanese cohort

Selective identification of men with clinically significant prostate cancer (sPC) is a pivotal issue. Development of a risk model for detecting sPC based on the prostate imaging reporting and data system (PI-RADS) for bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters in a Japanese cohort is expected to prove beneficial. We retrospectively analyzed clinical parameters and bpMRI findings from 773 biopsy-naïve patients between January 2011 and December 2016. A risk model was established using multivariate logistic regression analysis and presented on a nomogram. Discrimination of the risk model was compared using the area under the receiver operating characteristic curve. Statistical differences between the predictive model and clinical parameters were analyzed using DeLong test. sPC was detected in 343 men (44.3%). Multivariate logistic regression analysis to predict sPC revealed age (P = 0.002), log prostate-specific antigen (P < 0.001), prostate volume (P < 0.001) and PI-RADS scores (P < 0.001) as significant contributors to the model. Area under the curve was higher for the risk model (0.862), than for age (0.646), log prostate-specific antigen (0.652), prostate volume (0.697) or imaging score (0.822). DeLong test results also showed that the novel risk model performed significantly better than those parameters (P < 0.05). This novel risk model performed significantly better compared with PI-RADS scores and other parameters alone, and is thus expected to prove beneficial in making decisions regarding biopsy on suspicion of sPC.

Changing the Prostate Cancer Detection Paradigm: Clinical Application of European Association of Urology Guideline-recommended Magnetic Resonance Imaging-based Risk Stratification in Men with Suspected Prostate Cancer

BACKGROUND

Multiparametric magnetic resonance imaging using the Prostate Imaging Reporting and Data System version 2.1 allows for a personalized, risk-stratified approach to indicating prostate biopsies (PBx) in order to reduce PBx and concomitant complications in men with suspected prostate cancer (PCa). One way to achieve this goal is to implement the risk-stratified pathway (RSP) using the Rotterdam Prostate Cancer Risk Calculator.

OBJECTIVE

To describe the clinical implementation of the RSP and to examine its impact on the number of PBx and the resulting changes in the PCa detection pattern compared with men undergoing PBx in a detection-focused pathway (DFP) without prior risk assessment.

DESIGN, SETTING, AND PARTICIPANTS

An institutional dataset of 505 consecutive patients with suspected PCa between July 2019 and February 2020 was used.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Chi-square test and Mann-Whitney U test were employed to examine differences in the number of PBx and the PCa detection pattern between the DFP (n = 195, 38.6%) and the RSP (n = 310, 61.4%). To minimize differences in risk stratification, inverse probability of treatment weighting was used.

RESULTS AND LIMITATIONS

After implementing the RSP, the overall biopsy rate could be reduced by 11.2% (100% vs 88.8%, p < 0.001. Additionally, compared with the DFP, the number of biopsy cores per patient was reduced in the RSP (14 [interquartile range {IQR} 14-15] vs 14 [IQR 6-14], p < 0.001) and the detection of clinically significant PCa was increased (44.3% vs 57.7%, p = 0.038). Overdiagnosis of clinically insignificant disease was decreased in the RSP (22.8% vs 12.6%, p = 0.039).

CONCLUSIONS

Implementation of the RSP in clinical practice reduced the number of PBx and brought forth a shift in the PCa detection pattern toward clinically significant disease, while reducing overdiagnosis of clinically insignificant disease.

PATIENT SUMMARY

In this study, we examined the impact of risk stratification on the number of prostate biopsies (PBx) and the consecutive detection pattern in men with suspected prostate cancer (PCa). We found that the risk-stratified pathway reduced the number of PBx while simultaneously shifting the PCa detection pattern toward clinically significant PCa.

Clinical characteristics and overall survival prognostic nomogram for invasive cribriform carcinoma of breast: a SEER population-based analysis

BACKGROUND

The prognositc factors in patient with invasive cribriform carcinoma (ICC) of breast is still remain controversal. The study aims to establish a nomogram to predict the survival outcomes in patients with ICC based on the Surveillance, Epidemiology and End Results (SEER) database.

METHODS

We retrieved SEER database for clinical data about patients including ICC and infiltrating ductal carcinoma (IDC) from 2004 to 2015. Kaplan-Meier survival was used to compare the difference survival outcomes between ICC and IDC. ICC patients were randomly allocated to training cohort and validation cohort. A nomogram was built to predict individual patient's 3-year and 5-year survival status for ICC. The established TMN model and the newly established nomogram was further evaluated by the concordance index (C-index) and the decision curve analysis (DCA).

RESULTS

Comparing the baseline clinical data between IDC and ICC, a significant of smaller tumor mass, less infiltrated lymph nodes, lower metastases rate, better tumor differentiation degree, higher proportion of estrogen receptor (ER) and progesterone receptor (PR) positive and lower rate of chemotherapy and radiotherapy was found in ICC. Age at diagnosis, marriage status, tumor location, T stage, M stage, ER status, surgery were independent significant prognostic factors for the overall survival (OS). A significantly higher C-index was found in nomogram compared with established TNM model in validation cohort.

CONCLUSIONS

The prognosis of ICC patients is better than that of IDC patients. The nomogram is recommended for future patient with ICC to survival analysis.

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

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

Serum prostate-specific antigen (PSA) testing increases the number of persons who undergo prostate biopsy. However, the best possible strategy for selecting patients for prostate biopsy has not yet been defined. The aim of this study was to develop a classification and regression tree (CART) decision model that can be used to predict significant prostate cancer (PCa) in the course of prostate biopsy for patients with serum PSA levels of 10 ng/ml or less.

The following clinicopathological characteristics of patients who had undergone ultrasound-guided transrectal prostate biopsy were collected: age, PSA, digital rectal examination, volume of the prostate, and PSA density (PSAD). CART analysis was carried out by using all predictors. Different aspects of the predictive performances of the prediction model were assessed.

In this retrospective study, significant PCa values were detected in 26 (26.8%) of a total of 97 patients. The CART model had three branching levels based on PSAD as the most decisive variable and age. The model sensitivity was 73.1%, the specificity was 80.3% and the accuracy was 78.3%. Our model showed an area under the receiver operating characteristic curve of 82.6%. The model was well calibrated.

In conclusion, CART analysis determined that PSAD was the key parameter for the identification of patients with a minimal risk for positive biopsies. The model showed a good discrimination capacity that surpassed individual predictors. However, before recommending its use in clinical practice, an evaluation of a larger and more complete database is necessary for the prediction of significant PCa.

Comparing a new risk prediction model with prostate cancer risk calculator apps in a Taiwanese population

PURPOSE

To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps.

METHODS

1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented.

RESULTS

Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong's method: P < 0.001) and high-grade PCa (AUC: 0.869 vs 0.873 vs 0.767, P < 0.001). Using a ≥ 10% risk threshold for high-grade PCa to biopsy, our model could save 67.2% of total biopsies; among these saved biopsies, only 3.4% high-grade PCa would be missed.

CONCLUSION

Our new logistic regression model, similar to the Rotterdam app, outperformed the Coral app in the prediction of PCa and high-grade PCa. Additionally, our model could save unnecessary biopsies and avoid missing clinically significant PCa in the Taiwanese population.

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.

Personalizing prostate cancer diagnosis with multivariate risk prediction tools: how should prostate MRI be incorporated?

Risk-based patient selection for systematic biopsy in prostate cancer diagnosis has been adopted in daily clinical practice, either by clinical judgment and PSA testing, or using multivariate risk prediction tools. The use of multivariable risk prediction tools can significantly reduce unnecessary systematic biopsies, without compromising the detection of clinically significant disease. Increasingly multi-parametric magnetic resonance imaging (MRI) is performed, not only in men with a persistent suspicion of prostate cancer after prior negative systematic biopsy, but also at initial screening before the first biopsy. The combination of MRI and multivariate risk prediction tools could potentially enhance prostate cancer diagnosis using multivariate MRI incorporated risk-based models to decide on the need for prostate MRI, but also using MRI results to adjusted risk-based models, and to guide MRI-directed biopsies. In this review, we discuss the diagnostic work-up for clinically significant prostate cancer, where the combination of MRI and multivariate risk prediction tools is integrated, and how together they can contribute to personalized diagnosis.

A new predictor is comparable to the updated nomogram in predicting the intermediate- and high-risk prostate cancer but outperforms nomogram in reducing the overtreatment for the low-risk Pca

Purposes: To develop a new predictor and update nomogram based on prostate imaging reporting and data system version 2 (PI-RADS V2) in predicting intermediate- and high-risk prostate cancer (IH-Pca) and reducing the overtreatment for low-risk Pca (L-Pca). Methods: All men that underwent trans-rectal ultrasound-guided 12+X-core prostate biopsy between January 2015 and June 2018 were collected and analyzed. The significant risks (SRs) of Pca were selected by univariate and multivariate analysis. All SRs were divided into four groups (0 to 3 points) based on the probability of PI-RADS. Each patient can obtain a total score (TS). The updated nomogram was established by R package version 3.0. The area under the curve (AUC), net reclassification index (NRI), calibration curves and decision curves were used to evaluate the diagnostic performance. Results: There were 1,078 patients, including 640 (59%) men with normal or L-Pca (N-LPca) and 438 (41%) men with IH-Pca. The scores of TS for IH-Pca and N-LPca were 16.13±3.11 and 10.52±3.32, respectively (P<0.01). The discriminative power of TS and nomogram was comparable in predicting IH-Pca (AUCs: 0.88 vs 0.87, P=0.89), and both were greater than PSA and PI-RADS (AUCs: 0.76 vs 0.80). For NRI, NRITS vs nomogram was 1.31% (P=0.55), NRITS vs PSA was 24.13% (P<0.001) and NRITS vs PI-RADS was 13.19% (P<0.001). Compared with PSA, PI-RADS and nomogram, TS can reduce the number of unnecessary biopsies, up to 71%, 60% and 38%, respectively. Conclusion: The new predictor is comparable to the updated nomogram in predicting IH-Pca, and both are better than PSA and PI-RADS. In addition, the new predictor slightly outperforms nomogram in reducing the unnecessary biopsies for L-Pca and being convenient to use.

Prediction Medicine: Biomarkers, Risk Calculators and Magnetic Resonance Imaging as Risk Stratification Tools in Prostate Cancer Diagnosis

This review discusses the most recent evidence for currently available risk stratification tools in the detection of clinically significant prostate cancer (csPCa), and evaluates diagnostic strategies that combine these tools. Novel blood biomarkers, such as the Prostate Health Index (PHI) and 4Kscore, show similar ability to predict csPCa. Prostate cancer antigen 3 (PCA3) is a urinary biomarker that has inferior prediction of csPCa compared to PHI, but may be combined with other markers like TMPRSS2-ERG to improve its performance. Original risk calculators (RCs) have the advantage of incorporating easy to retrieve clinical variables and being freely accessible as a web tool/mobile application. RCs perform similarly well as most novel biomarkers. New promising risk models including novel (genetic) markers are the SelectMDx and Stockholm-3 model (S3M). Prostate magnetic resonance imaging (MRI) has evolved as an appealing tool in the diagnostic arsenal with even stratifying abilities, including in the initial biopsy setting. Merging biomarkers, RCs and MRI results in higher performances than their use as standalone tests. In the current era of prostate MRI, the way forward seems to be multivariable risk assessment based on blood and clinical parameters, potentially extended with information from urine samples, as a triaging test for the selection of candidates for MRI and biopsy.

Multivariate risk prediction tools including MRI for individualized biopsy decision in prostate cancer diagnosis: current status and future directions

Background and purpose

Individualized risk-adapted algorithms in prostate cancer (PCa) diagnosis using predictive prebiopsy variables in addition to prostate-specific antigen value may result in a considerable reduction of unnecessary systematic biopsies. Multi-parametric magnetic resonance imaging (mpMRI) has emerged as a secondary prediction tool that can further improve the detection of clinically significant prostate cancer (csPCa). This review explores the performance of new MRI risk models for indicating a biopsy for prostate cancer diagnosis.

Results and considerations

The area under the receiver-operating characteristic curve for detecting csPCa varies between 0.64 and 0.91 in biopsy-naïve men, and between 0.78 and 0.93 in men with a previous negative biopsy. The utility of multivariate risk prediction tools including MRI suspicion scores as an extra input parameter has the potential to avoid a notable number of biopsies and detection of clinically insignificant PCa at a low price of missing some csPCa. The trade-off depends on the risk threshold that is chosen. In biopsy-naïve men a net benefit was obtained at a risk threshold of above 10% for csPCa in most MRI risk prediction models. All constructed MRI risk models used (referral) patient cohorts with high prevalence of csPCa. Using more representative cohorts from daily clinical screening, net benefit may attenuate at lower risk thresholds. Strengths and limitations of these models are discussed.

Future directions

To assess their wider applicability, in-depth analysis of mpMRI predictive qualities should be further investigated, in combination with required external validation of these models in a multicenter setting with large prospective datasets.

External validation of Imamura nomogram as a tool to predict preoperatively laser semi-rigid ureterolithotripsy outcomes

BACKGROUND

We aimed to validate Imamura nomogram for prediction of stone free rate in patients undergoing ureterolithotripsy (ULT).

METHODS

From January 2013 to June 2016, patients undergoing laser semi-rigid ULT were prospectively enrolled at our center. All patients were preoperatively assessed with clinical history, blood samples, uranalysis and non-contrast enhanced computed tomography (CT). Treatment efficacy was assessed 1 month later by non-contrast enhanced CT. ROC curve was used to evaluate the performance characteristics of Imamura nomogram.

RESULTS

Overall, we enrolled 275 patients. Median age was 55 years (IQR: 46/64), median length of stone was 9.8 mm (IQR: 7.5/12). Pyuria was detected in 6/275 (2.1%) patients. Stones were located at ureteropelvic junction in 55/275 (19%) patients, proximal ureter in 74/275 (26%) patients, middle and distal ureter in 66/275 (24%) patients and 82/275 (30%) patients, respectively. At 1-month follow-up, 209/275 (76%) patients were stone free. Imamura nomogram presented an AUC of 0.67 (95% CI: 0.580-0.761) for the prediction of stone free rate. At the best cut-off value of 75%, sensitivity was 76%, specificity was 55%, positive predictive value (PPV) was 83% and negative predictive value was 45%.

CONCLUSIONS

We firstly validated Imamura nomogram in a European cohort study. It proved a reasonable accuracy (area under curve: 0.67) and a good PPV (83%). Further studies should confirm our results to support the routine clinical use of Imamura nomogram as a tool to predict ULT outcomes.

A Nomogram Based on a TRUS Five-Grade Scoring System for the Prediction of Prostate Cancer and High Grade Prostate Cancer at Initial TRUS-Guided Biopsy

Purpose: To evaluate the efficacy of transrectal ultrasound five-grade scoring system (TRUS-5) in predicting prostate cancer (PCa) and high grade PCa (HGPCa), compared with TRUS two-grade scoring system (TRUS-2), and establish a TRUS-5 based nomogram for the prediction of PCa and HGPCa at initial biopsy (IPBx).

Methods: Data were collected from 862 men who underwent initial TRUS-guided 12-core prostate biopsy. Age, prostate-specific antigen (PSA), percent free PSA, digital rectal examination (DRE), prostate volume (PV), PSA density (PSAD) and TRUS findings were included in the analysis. For TRUS-5, the probability of PCa was quantified on a scale from 1 (benign) to 5 (malignant). TRUS-2 used the grades “normal” and “suspicious”. After univariate and multivariate logistic regression analyses, nomogram models were developed and internally validated based on independent predictors to predict the probability of PCa and HGPCa.

Results: Overall PCa was detected in 42% (362/862) with 26.22% (226/862) showing HGPCa. TRUS-5 significantly outperformed TRUS-2 for the risk prediction of PCa and HGPCa (area under the receiver operating characteristic curve [AUC]: 0.787 vs. 0.694 for PCa, 0.841 vs. 0.713 for HGPCa, P<0.05). The TRUS-5 based nomogram showed higher AUCs (0.905 for PCa, 0.903 for HGPCa) than PSA alone, clinical base model, the TRUS-2 based model, and other predictive models (P<0.05).

Conclusions: TRUS-5 represents a better imaging predictor than TRUS-2 for PCa and HGPCa. Our TRUS-5 based nomogram models performed well for the prediction of PCa and HGPCa at IPBx, which may help to make the decision to biopsy.

Repeat prostate-specific antigen (PSA) test before prostate biopsy: a 20% decrease in PSA values is associated with a reduced risk of cancer and particularly of high-grade cancer

OBJECTIVES

To analyse the impact of repeating a prostate-specific antigen (PSA) level assessment on prostate biopsy decision in a cohort of men undergoing prostate biopsy.

PATIENTS AND METHODS

From 2015 onwards, we consecutively enrolled, at a single institution in Italy, men undergoing 12-core transrectal ultrasonography-guided prostate needle biopsy. Indication for prostate biopsy was a PSA level of ≥4 ng/mL. Demographic, clinical, and histopathological data were collected. The PSA level was tested at enrolment (PSA₁ ) and 4 weeks later on the day before biopsy (PSA₂ ). Variations in PSA level were defined as: stable PSA₂ within a 10% variation, stable PSA₂ within a 20% variation, PSA₂ decreased by ≥10%, PSA₂ decreased by ≥20%, PSA₂ increased by ≥10%, PSA₂ increased by ≥20%, and PSA₂ <4 ng/mL. Percentages and multinomial logistic regression were used to analyse biopsy outcomes. High-grade cancer was defined as Grade group ≥3.

RESULTS

Overall, 331 patients were enrolled. Prostate cancer was diagnosed in 153/331 (46%) patients and of them 80/153 (52%) had high-grade disease. When compared to the rest of the population, patients with a stable PSA within 20% variation had a higher risk of prostate cancer (odds ratio [OR] 1.80, P < 0.05) and high grade disease (OR 2.56, P < 0.05), patients with a PSA2 decreased by ≥20% had a lower risk of prostate cancer (OR 0.37, P < 0.05) and high grade disease (OR 0.13, P < 0.05), whilst patients with a PSA2 increased by ≥10% had an increased risk of high-grade prostate cancer (OR 1.93, P < 0.05). When PSA returned to normal values (<4 ng/mL) both risks of prostate cancer and high-grade disease were reduced (OR 0.33 and 0.01, respectively, P = 0.001).

CONCLUSION

In a cohort of Italian men undergoing prostate biopsy, a reduction of ≥20% in PSA levels significantly reduced the risk of high-grade prostate cancer. Further multicentre studies should validate our present results.

Head-to-head comparison of prostate cancer risk calculators predicting biopsy outcome

Background

Multivariable risk calculators (RCs) predicting prostate cancer (PCa) aim to reduce unnecessary workup (e.g., MRI and biopsy) by selectively identifying those men at risk for PCa or clinically significant PCa (csPCa) (Gleason ≥7). The lack of an adequate comparison makes choosing between RCs difficult for patients, clinicians and guideline developers. We aim to perform a head-to-head comparison of seven well known RCs predicting biopsy outcome.

Methods

Our study comprised 7,119 men from ten independent contemporary cohorts in Europe and Australia, who underwent prostate biopsy between 2007 and 2015. We evaluated the performance of the ERSPC RPCRC, Finne, Chun, ProstataClass, Karakiewicz, Sunnybrook, and PCPT 2.0 (HG) RCs in predicting the presence of any PCa and csPCa. Performance was assessed by discrimination, calibration and net benefit analyses.

Results

A total of 3,458 (48%) PCa were detected; 1,784 (25%) men had csPCa. No particular RC stood out predicting any PCa: pooled area under the ROC-curve (AUC) ranged between 0.64 and 0.72. The ERSPC RPCRC had the highest pooled AUC 0.77 (95% CI: 0.73–0.80) when predicting csPCa. Decision curve analysis (DCA) showed limited net benefit in the detection of csPCa, but that can be improved by a simple calibration step. The main limitation is the retrospective design of the study.

Conclusions

No particular RC stands out when predicting biopsy outcome on the presence of any PCa. The ERSPC RPCRC is superior in identifying those men at risk for csPCa. Net benefit analyses show that a multivariate approach before further workup is advisable.

A comparison of 3 on-line nomograms with the detection of primary circulating prostate cells to predict prostate cancer at initial biopsy

INTRODUCTION

The use of nomograms which include the PSA may improve the predictive power of obtaining a prostate biopsy (PB) positive for cancer. We compare the use of three on-line nomagrams with the detection of primary malignant circulating prostate cells (CPCs) to predict the results of an initial PB in men with suspicion of prostate cancer.

METHODS AND PATIENTS

Consecutive men with suspicion of prostate cancer underwent a 12 core TRUS prostate biopsy; age, total serum PSA, percent free PSA, family history, ethnic origin and prostate ultrasound results were used for risk assessment using the online nomograms. Mononuclear cells were obtained by differential gel centrifugation from 8ml of blood and CPCs were identified using double immunomarcation with anti-PSA and anti-P504S. A CPC was defined as a cell expressing PSA and P504S and defined as negative/positive. Biopsies were classified as cancer/no-cancer. Areas under the curve (AUC) for each parameter were calculated and compared and diagnostic yields were calculated.

RESULTS

1,223 men aged>55 years participated, 467 (38.2%) had a biopsy positive for cancer of whom 114/467 (24.4%) complied with the criteria for active observation. Area under the curve analysis showed CPC detection to be superior (p<0.001), avoiding 57% of potential biopsies while missing 4% of clinically significant prostate cancers.

CONCLUSIONS

The CPC detection was superior to the nomograms in predicting the presence of prostate cancer at initial biopsy; its high negative predictive value potentially reduces the number of biopsies while missing few significant cancers, being superior to the nomograms in this aspect. Being a positive/negative test the detection of CPCs avoids defining a cutoff value which may differ between populations.

Prostate cancer screening in Europe and Asia

Prostate cancer (PCa) is the second most common cancer among men worldwide and even ranks first in Europe. Although Asia is known as the region with the lowest PCa incidence, it has been rising rapidly over the last 20 years mostly due to the introduction of prostate-specific antigen (PSA) testing. Randomized PCa screening studies in Europe show a mortality reduction in favor of PSA-based screening but coincide with high proportions of unnecessary biopsies, overdiagnosis and subsequent overtreatment. Conclusive data on the value of PSA-based screening and hence the balance between harms and benefits in Asia is still lacking. Because of known racial variations, Asian countries should not directly apply the European screening models. Like in the western world also in Asia, new predictive markers, tools and risk stratification strategies hold great potential to improve the early detection of PCa and to reduce the worldwide existing negative aspects of PSA-based PCa screening.

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

OBJECTIVE

to develop a predictive model to estimate the probability of prostate cancer prior to biopsy.

METHODS

from September 2009 to January 2014, 445 men underwent prostate biopsy in a radiology service. We excluded from the study patients with diseases that could compromise the data analysis, who had undergone prostatic resection or used 5-alpha-reductase inhibitors. Thus, we selected 412 patients. Variables included in the model were age, prostate specific antigen (PSA), digital rectal examination, prostate volume and abnormal sonographic findings. We constructed Receiver Operating Characteristic (ROC) curves and calculated the areas under the curve, as well as the model's Positive Predictive Value (PPV) .

RESULTS

of the 412 men, 155 (37.62%) had prostate cancer (PC). The mean age was 63.8 years and the median PSA was 7.22ng/ml. In addition, 21.6% and 20.6% of patients had abnormalities on digital rectal examination and image suggestive of cancer by ultrasound, respectively. The median prostate volume and PSA density were 45.15cm3 and 0.15ng/ml/cm3, respectively. Univariate and multivariate analyses showed that only five studied risk factors are predictors of PC in the study (p<0.05). The PSA density was excluded from the model (p=0.314). The area under the ROC curve for PC prediction was 0.86. The PPV was 48.08% for 95%sensitivity and 52.37% for 90% sensitivity.

CONCLUSION

the results indicate that clinical, laboratory and ultrasound data, besides easily obtained, can better stratify the risk of patients undergoing prostate biopsy.

Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort

PURPOSE

We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort.

MATERIALS AND METHODS

Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots.

RESULTS

PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed.

CONCLUSIONS

KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy and reduce overdiagnosis and overtreatment in clinical settings.

What is the best way not to treat prostate cancer?

INTRODUCTION

Selective treatment approaches for prostate cancer (PCa) are warranted given the highly varied nature of the disease and the consequences associated with definitive therapy.

MATERIALS AND METHODS

We present a stepwise overview of strategies optimized to not treat PCa, ranging from improved screening practices that seek to maximize the yield at initial diagnosis, as well as refinements to clinical risk prediction and the performance of active surveillance.

RESULTS

Improved adherence to screening guidelines offering simplistic, rational practice recommendations are poised to improve the performance of early detection strategies. In addition, measures to improve the quality of PCa screening would include greater integration of novel markers with higher specificity for clinically significant disease, in an effort to stem the tide of over-diagnosis and consequential overtreatment of low-grade tumors. For men diagnosed with PCa, the use of validated, multi-variable risk stratification stands to offer greater certainty in initial management choices: consideration of active surveillance for those with low-risk status, and definitive therapy for men with intermediate and high-risk features. We review the efficacy and nature of active surveillance protocols, and offer a context for refinements that may be anticipated with future study.

CONCLUSIONS

The question of how best to not treat prostate cancer is often more complex than policies of universal treatment, yet is integral to minimize morbidity of over-treatment in patients with low-risk tumors. An array of refined risk stratification instruments, biomarkers, and genomic assays seek to improve the confidence both prior to, and following diagnosis.

The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation

BACKGROUND

There is no single definitive test to identify prostate cancer in men. Biopsies are commonly used to obtain samples of prostate tissue for histopathological examination. However, this approach frequently misses cases of cancer, meaning that repeat biopsies may be necessary to obtain a diagnosis. The PROGENSA(®) prostate cancer antigen 3 (PCA3) assay (Hologic Gen-Probe, Marlborough, MA, USA) and the Prostate Health Index (phi; Beckman Coulter Inc., Brea, CA, USA) are two new tests (a urine test and a blood test, respectively) that are designed to be used to help clinicians decide whether or not to recommend a repeat biopsy.

OBJECTIVE

To evaluate the clinical effectiveness and cost-effectiveness of the PCA3 assay and the phi in the diagnosis of prostate cancer.

DATA SOURCES

Multiple publication databases and trial registers were searched in May 2014 (from 2000 to May 2014), including MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science, Medion, Aggressive Research Intelligence Facility database, ClinicalTrials.gov, International Standard Randomised Controlled Trial Number Register and World Health Organization International Clinical Trials Registry Platform.

REVIEW METHODS

The assessment of clinical effectiveness involved three separate systematic reviews, namely reviews of the analytical validity, the clinical validity of these tests and the clinical utility of these tests. The assessment of cost-effectiveness comprised a systematic review of full economic evaluations and the development of a de novo economic model.

SETTING

The perspective of the evaluation was the NHS in England and Wales.

PARTICIPANTS

Men suspected of having prostate cancer for whom the results of an initial prostate biopsy were negative or equivocal.

INTERVENTIONS

The use of the PCA3 score or phi in combination with existing tests (including histopathology results, prostate-specific antigen level and digital rectal examination), multiparametric magnetic resonance imaging and clinical judgement.

RESULTS

In addition to documents published by the manufacturers, six studies were identified for inclusion in the analytical validity review. The review identified issues concerning the precision of the PCA3 assay measurements. It also highlighted issues relating to the storage requirements and stability of samples intended for analysis using the phi assay. Fifteen studies met the inclusion criteria for the clinical validity review. These studies reported results for 10 different clinical comparisons. There was insufficient evidence to enable the identification of appropriate test threshold values for use in a clinical setting. In addition, the implications of adding either the PCA3 assay or the phi to clinical assessment were not clear. Furthermore, the addition of the PCA3 assay or the phi to clinical assessment plus magnetic resonance imaging was not found to improve discrimination. No published papers met the inclusion criteria for either the clinical utility review or the cost-effectiveness review. The results from the cost-effectiveness analyses indicated that using either the PCA3 assay or the phi in the NHS was not cost-effective.

LIMITATIONS

The main limitations of the systematic review of clinical validity are that the review conclusions are over-reliant on findings from one study, the descriptions of clinical assessment vary widely within reviewed studies and many of the reported results for the clinical validity outcomes do not include either standard errors or confidence intervals.

CONCLUSIONS

The clinical benefit of using the PCA3 assay or the phi in combination with existing tests, scans and clinical judgement has not yet been confirmed. The results from the cost-effectiveness analyses indicate that the use of these tests in the NHS would not be cost-effective.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42014009595.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

External Evaluation of a Novel Prostate Cancer Risk Calculator (ProstateCheck) Based on Data from the Swiss Arm of the ERSPC

PURPOSE

We externally validated a novel prostate cancer risk calculator based on data from the Swiss arm of the ERSPC and assessed whether the risk calculator (ProstateCheck) is superior to the PCPT-RC and SWOP-RC in an independent Swiss cohort.

MATERIALS AND METHODS

Data from all men who underwent prostate biopsy at an academic tertiary care center between 2004 and 2012 were retrospectively analyzed. The probability of having any prostate cancer or high grade prostate cancer (Gleason score 7 or greater) on prostate biopsy was calculated using the ProstateCheck. Risk calculator performance was assessed using calibration and discrimination, and additionally compared with the PCPT-RC and SWOP-RC by decision curve analyses.

RESULTS

Of 1,615 men 401 (25%) were diagnosed with any prostate cancer and 196 (12%) with high grade prostate cancer. Our analyses of the ProstateCheck-RC revealed good calibration in the low risk range (0 to 0.4) and moderate overestimation in the higher risk range (0.4 to 1) for any and high grade prostate cancer. The AUC for the discrimination of any prostate cancer and high grade prostate cancer was 0.69 and 0.72, respectively, which was slightly but significantly higher compared to the PCPT-RC (0.66 and 0.69, respectively) and SWOP-RC (0.64 and 0.70, respectively). Decision analysis, taking into account the harms of transrectal ultrasound measurement of prostate volume, showed little benefit for ProstateCheck-RC, with properties inferior to those of the PCPT-RC and SWOP-RC.

CONCLUSIONS

Our independent external evaluation revealed moderate performance of the ProstateCheck-RC. Its clinical benefit is limited, and inferior to that of the PCPT-RC and SWOP-RC.

PCA3-based nomogram for predicting prostate cancer and high grade cancer on initial transrectal guided biopsy

BACKGROUND

To develop a validated prostate cancer antigen 3 (PCA3) based nomogram that predicts likelihood of overall prostate cancer (PCa) and intermediate/high grade prostate cancer (HGPCa) in men pursuing initial transrectal prostate biopsy (TRUS-PBx).

METHODS

Data were collected on 3,675 men with serum prostate specific antigen level (PSA) ≤ 20 ng/ml who underwent initial prostate biopsy with at least 10 cores sampling at time of the biopsy. Two logistic regression models were constructed to predict overall PCa and HGPCa incorporating age, race, family history (FH) of PCa, PSA at diagnosis, PCA3, total prostate volume (TPV), and digital rectal exam (DRE).

RESULTS

One thousand six hundred twenty (44%) patients had biopsy confirmed PCa with 701 men (19.1%) showing HGPCa. Statistically significant predictors of overall PCa were age (P < 0.0001, OR. 1.51), PSA at diagnosis (P < 0.0001, OR.1.95), PCA3 (P < 0.0001, OR.3.06), TPV (P < 0.0001, OR.0.47), FH (P = 0.003, OR.1.32), and abnormal DRE (P = 0.001, OR. 1.32). While for HGPCa, predictors were age (P < 0.0001, OR.1.77), PSA (P < 0.0001, OR.2.73), PCA3 (P < 0.0001, OR.2.26), TPV (P < 0.0001, OR.0.4), and DRE (P < 0.0001, OR.1.53). Two nomograms were reconstructed for predicted overall PCa probability at time of initial biopsy with a concordance index of 0.742 (Fig. 1), and HGPCa with a concordance index of 0.768 (Fig. 2).

CONCLUSIONS

Our internally validated initial biopsy PCA3 based nomogram is reconstructed based on a large dataset. The c-index indicates high predictive accuracy, especially for high grade PCa and improves the ability to predict biopsy outcomes.

Prevalence and Risk Factors of Prostate Cancer in Chinese Men with PSA 4-10 ng/mL Who Underwent TRUS-Guided Prostate Biopsy: The Utilization of PAMD Score

Purpose. To elucidate the characteristics and risk factors for positive biopsy outcomes in Chinese patients with prostate specific antigen (PSA) 4–10 ng/mL and develop a risk-stratification score model. Methods. The data of 345 patients who underwent transrectal ultrasound-guided prostate biopsy between 2011 and 2013 was retrospectively analyzed. Digital rectal examination (DRE), prostate volume (PV), magnetic resonance imaging (MRI), and smoking status were also collected. Positive biopsy outcomes were defined as prostate cancer (PCa) and high grade PCa (HGPCa, Gleason Score ≥ 7). Results. The median PSA was 7.15 (IQR 5.91–8.45) ng/mL. Overall 138 patients (40.0%) were shown to have PCa, including 100 patients (29.0%) with HGPCa. Smaller PV, elder age, MRI results, and positive DRE were proved to be predictive factors for positive biopsy outcomes in both univariate and multivariate analysis. We developed a “PAMD” score which combined the four factors to categorize patients into three risk groups, and the model performed good predictive sensitivity and specificity. Conclusion. The prevalence of prostate cancer in Chinese patients with PSA 4–10 ng/mL was 40%, including 29% patients with high grade disease. DRE, age, MRI, and PV were predictive factors for positive biopsy outcomes, and the PAMD score model could be utilized for risk-stratification and decision-making.

Optimizing the clinical utility of PCA3 to diagnose prostate cancer in initial prostate biopsy

Background

PCA3 has been included in a nomogram outperforming previous clinical models for the prediction of any prostate cancer (PCa) and high grade PCa (HGPCa) at the initial prostate biopsy (IBx). Our objective is to validate such IBx-specific PCA3-based nomogram. We also aim to optimize the use of this nomogram in clinical practice through the definition of risk groups.

Methods

Independent external validation. Clinical and biopsy data from a contemporary cohort of 401 men with the same inclusion criteria to those used to build up the reference’s nomogram in IBx. The predictive value of the nomogram was assessed by means of calibration curves and discrimination ability through the area under the curve (AUC). Clinical utility of the nomogram was analyzed by choosing thresholds points that minimize the overlapping between probability density functions (PDF) in PCa and no PCa and HGPCa and no HGPCa groups, and net benefit was assessed by decision curves.

Results

We detect 28 % of PCa and 11 % of HGPCa in IBx, contrasting to the 46 and 20 % at the reference series. Due to this, there is an overestimation of the nomogram probabilities shown in the calibration curve for PCa. The AUC values are 0.736 for PCa (C.I.95 %:0.68–0.79) and 0.786 for HGPCa (C.I.95 %:0.71–0.87) showing an adequate discrimination ability. PDF show differences in the distributions of nomogram probabilities in PCa and not PCa patient groups. A minimization of the overlapping between these curves confirms the threshold probability of harboring PCa >30 % proposed by Hansen is useful to indicate a IBx, but a cut-off > 40 % could be better in series of opportunistic screening like ours. Similar results appear in HGPCa analysis. The decision curve also shows a net benefit of 6.31 % for the threshold probability of 40 %.

Conclusions

PCA3 is an useful tool to select patients for IBx. Patients with a calculated probability of having PCa over 40 % should be counseled to undergo an IBx if opportunistic screening is required.

Visualizing Risk Prediction Models

OBJECTIVE

Risk prediction models can assist clinicians in making decisions. To boost the uptake of these models in clinical practice, it is important that end-users understand how the model works and can efficiently communicate its results. We introduce novel methods for interpretable model visualization.

METHODS

The proposed visualization techniques are applied to two prediction models from the Framingham Heart Study for the prediction of intermittent claudication and stroke after atrial fibrillation. We represent models using color bars, and visualize the risk estimation process for a specific patient using patient-specific contribution charts.

RESULTS

The color-based model representations provide users with an attractive tool to instantly gauge the relative importance of the predictors. The patient-specific representations allow users to understand the relative contribution of each predictor to the patient's estimated risk, potentially providing insightful information on which to base further patient management. Extensions towards non-linear models and interactions are illustrated on an artificial dataset.

CONCLUSION

The proposed methods summarize risk prediction models and risk predictions for specific patients in an alternative way. These representations may facilitate communication between clinicians and patients.

Comparison of Two Prostate Cancer Risk Calculators that Include the Prostate Health Index

BACKGROUND

Risk prediction models for prostate cancer (PCa) have become important tools in reducing unnecessary prostate biopsies. The Prostate Health Index (PHI) may increase the predictive accuracy of such models.

OBJECTIVES

To compare two PCa risk calculators (RCs) that include PHI.

DESIGN, SETTING, AND PARTICIPANTS

We evaluated the predictive performance of a previously developed PHI-based nomogram and updated versions of the European Randomized Study of Screening for Prostate Cancer (ERSPC) RCs based on digital rectal examination (DRE): RC3 (no prior biopsy) and RC4 (prior biopsy). For the ERSPC updates, the original RCs were recalibrated and PHI was added as a predictor. The PHI-updated ERSPC RCs were compared with the Lughezzani nomogram in 1185 men from four European sites. Outcomes were biopsy-detectable PC and potentially advanced or aggressive PCa, defined as clinical stage >T2b and/or a Gleason score ≥7 (clinically relevant PCa).

RESULTS AND LIMITATIONS

The PHI-updated ERSPC models had a combined area under the curve for the receiver operating characteristic (AUC) of 0.72 for all PCa and 0.68 for clinically relevant PCa. For the Lughezzani PHI-based nomogram, AUCs were 0.75 for all PCa and 0.69 for clinically relevant PCa. For men without a prior biopsy, PHI-updated RC3 resulted in AUCs of 0.73 for PCa and 0.66 for clinically relevant PCa. Decision curves confirmed these patterns, although the number of clinically relevant cancers was low.

CONCLUSION

Differences between RCs that include PHI are small. Addition of PHI to an RC leads to further reductions in the rate of unnecessary biopsies when compared to a strategy based on prostate-specific antigen measurement.

PATIENT SUMMARY

Risk prediction models for prostate cancer have become important tools in reducing unnecessary prostate biopsies. We compared two risk prediction models for prostate cancer that include the Prostate Health Index. We found that these models are equivalent to each other, and both perform better than the prostate-specific antigen test alone in predicting cancer.

External validation of extended prostate biopsy nomogram

Introduction

Historical nomograms for the prediction of cancer on prostate biopsy, developed in the sextant biopsy era are no more accurate today. The aim of this study was an independent external validation of a 10-core biopsy nomogram by Chun et al. (2007).

Material and methods

A total of 322 patients who presented for their initial biopsy in a tertiary care center and had all the necessary data available were included in the retrospective analysis. To validate the nomogram, receiver operator characteristic (ROC) curves and calibration plots were constructed.

Results

Area under the ROC curve calculated for our data using the nomogram was 0.773, similar to that reported originally. However, the nomogram systematically overestimated prostate cancer risk, which, for our data, could be resolved by subtracting 24 points from the total number of points of the nomogram.

Conclusions

The nomogram yielded overall good predictive accuracy as measured by the area under the ROC curve, but it systematically overestimated PC probability in individual patients. However, we showed how the nomogram could easily be adapted to our patient sample, resolving the bias issue.

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.

Configuration and validation of a novel prostate disease nomogram predicting prostate biopsy outcome: A prospective study correlating clinical indicators among Filipino adult males with elevated PSA level

Objective

To configure and validate a novel prostate disease nomogram providing prostate biopsy outcome probabilities from a prospective study correlating clinical indicators and diagnostic parameters among Filipino adult male with elevated serum total prostate specific antigen (PSA) level.

Methods

All men with an elevated serum total PSA underwent initial prostate biopsy at our institution from January 2011 to August 2014 were included. Clinical indicators, diagnostic parameters, which include PSA level and PSA-derivatives, were collected as predictive factors for biopsy outcome. Multiple logistic-regression analysis involving a backward elimination selection procedure was used to select independent predictors. A nomogram was developed to calculate the probability of the biopsy outcomes. External validation of the nomogram was performed using separate data set from another center for determination of sensitivity and specificity. A receiver-operating characteristic (ROC) curve was used to assess the accuracy in predicting differential biopsy outcome.

Results

Total of 552 patients was included. One hundred and ninety-one (34.6%) patients had benign prostatic hyperplasia, and 165 (29.9%) had chronic prostatitis. The remaining 196 (35.5%) patients had prostate adenocarcinoma. The significant independent variables used to predict biopsy outcome were age, family history of prostate cancer, prior antibiotic intake, PSA level, PSA-density, PSA-velocity, echogenic findings on ultrasound, and DRE status. The areas under the receiver-operating characteristic curve for prostate cancer using PSA alone and the nomogram were 0.688 and 0.804, respectively.

Conclusion

The nomogram configured based on routinely available clinical parameters, provides high predictive accuracy with good performance characteristics in predicting the prostate biopsy outcome such as presence of prostate cancer, high Gleason prostate cancer, benign prostatic hyperplasia, and chronic prostatitis.

Validation of a nomogram for predicting the probability of carcinoma in patients with intraductal papillary mucinous neoplasm in 180 pancreatic resection patients at 3 high-volume centers

OBJECTIVE

We previously published a nomogram for prediction of carcinoma in patients with intraductal papillary mucinous neoplasm (IPMN). The objective of the current study was to validate this nomogram in an external cohort of patients at multiple institutions.

METHODS

The clinical details of 180 patients with IPMN who underwent a pancreatic resection at 3 hospitals were collected. Four significant predictive factors (sex, lesion type, nodule height, and pancreatic juice cytology) were analyzed.

RESULTS

Of the 180 patients, 66 (36.7%) had a main pancreatic duct-type IPMN and 114 (63.3%) had a branch pancreatic duct-type IPMN. The final pathological diagnosis was benign IPMN in 95 (52.8%) patients and malignant IPMN in 85 (47.2%) patients. The area under the receiver operating characteristic curve for the model was 0.760. The area under the receiver operating characteristic curve of the IPMN nomogram for prediction of malignancy was 0.747 in main pancreatic duct-type IPMN and 0.752 in branch pancreatic duct-type IPMN. The sensitivity and specificity of the model were 80.0% and 57.9%, respectively, when the predictive probability of more than 10% was used to indicate the presence of carcinoma.

CONCLUSIONS

This nomogram for predicting the probability of carcinoma in patients with IPMN was accurate in an external validation patient cohort.

Systematic review of clinical features of suspected prostate cancer in primary care

OBJECTIVE

To systematically review the literature and provide an update and integration of existing peer-reviewed guidelines with recent systematic reviews and with primary studies related to the early recognition and management of prostate cancer in primary care.

DATA SOURCES

We searched MEDLINE and EMBASE for relevant articles. The quality of the evidence to support existing guideline recommendations and the consistency of recommendations with updated evidence were assessed. Applicability in a Canadian primary care setting was also evaluated.

STUDY SELECTION

All studies conducted in the primary care setting that provided information on clinical features predictive of prostate cancer were included. Also, studies that assessed the accuracy of nomograms to predict prostate cancer were reviewed.

SYNTHESIS

The findings suggest that lower urinary tract symptoms are not highly predictive of prostate cancer. However, evidence suggests that FPs might be good at discriminating between patients with and without prostate cancer using digital rectal examination and prostate-specific antigen testing. Nomograms might also be useful in assessing patients for aggressive prostate cancers.

CONCLUSION

The results of this review can be used to inform recommendations for referral for suspected prostate cancer in the primary care setting. They could also inform development of prostate cancer diagnostic assessment programs.

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.

The added value of percentage of free to total prostate-specific antigen, PCA3, and a kallikrein panel to the ERSPC risk calculator for prostate cancer in prescreened men

BACKGROUND

Prostate-specific antigen (PSA) testing has limited accuracy for the early detection of prostate cancer (PCa).

OBJECTIVE

To assess the value added by percentage of free to total PSA (%fPSA), prostate cancer antigen 3 (PCA3), and a kallikrein panel (4k-panel) to the European Randomised Study of Screening for Prostate Cancer (ERSPC) multivariable prediction models: risk calculator (RC) 4, including transrectal ultrasound, and RC 4 plus digital rectal examination (4+DRE) for prescreened men.

DESIGN, SETTING, AND PARTICIPANTS

Participants were invited for rescreening between October 2007 and February 2009 within the Dutch part of the ERSPC study. Biopsies were taken in men with a PSA level ≥3.0 ng/ml or a PCA3 score ≥10. Additional analyses of the 4k-panel were done on serum samples.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Outcome was defined as PCa detectable by sextant biopsy. Receiver operating characteristic curve and decision curve analyses were performed to compare the predictive capabilities of %fPSA, PCA3, 4k-panel, the ERSPC RCs, and their combinations in logistic regression models.

RESULTS AND LIMITATIONS

PCa was detected in 119 of 708 men. The %fPSA did not perform better univariately or added to the RCs compared with the RCs alone. In 202 men with an elevated PSA, the 4k-panel discriminated better than PCA3 when modelled univariately (area under the curve [AUC]: 0.78 vs. 0.62; p=0.01). The multivariable models with PCA3 or the 4k-panel were equivalent (AUC: 0.80 for RC 4+DRE). In the total population, PCA3 discriminated better than the 4k-panel (univariate AUC: 0.63 vs. 0.56; p=0.05). There was no statistically significant difference between the multivariable model with PCA3 (AUC: 0.73) versus the model with the 4k-panel (AUC: 0.71; p=0.18). The multivariable model with PCA3 performed better than the reference model (0.73 vs. 0.70; p=0.02). Decision curves confirmed these patterns, although numbers were small.

CONCLUSIONS

Both PCA3 and, to a lesser extent, a 4k-panel have added value to the DRE-based ERSPC RC in detecting PCa in prescreened men.

PATIENT SUMMARY

We studied the added value of novel biomarkers to previously developed risk prediction models for prostate cancer. We found that inclusion of these biomarkers resulted in an increase in predictive ability.

Ethnicity is an independent determinant of age-specific PSA level: findings from a multiethnic Asian setting

Objectives

To study the baseline PSA profile and determine the factors influencing the PSA levels within a multiethnic Asian setting.

Materials and Methods

We conducted a cross-sectional study of 1054 men with no clinical evidence of prostate cancer, prostate surgery or 5α-reductase inhibitor treatment of known prostate conditions. The serum PSA concentration of each subject was assayed. Potential factors associated with PSA level including age, ethnicity, height, weight, family history of prostate cancer, lower urinary tract voiding symptoms (LUTS), prostate volume and digital rectal examination (DRE) were evaluated using univariable and multivariable analysis.

Results

There were 38 men (3.6%) found to have a PSA level above 4 ng/ml and 1016 (96.4%) with a healthy PSA (≤4 ng/ml). The median PSA level of Malay, Chinese and Indian men was 1.00 ng/ml, 1.16 ng/ml and 0.83 ng/ml, respectively. Indians had a relatively lower median PSA level and prostate volume than Malays and Chinese, who shared a comparable median PSA value across all 10-years age groups. The PSA density was fairly similar amongst all ethnicities. Further analysis showed that ethnicity, weight and prostate volume were independent factors associated with age specific PSA level in the multivariable analysis (p<0.05).

Conclusion

These findings support the concept that the baseline PSA level varies between different ethnicities across all age groups. In addition to age and prostate volume, ethnicity may also need to be taken into account when investigating serum PSA concentrations in the multiethnic Asian population.

The development of nomograms for stratification of men at risk of prostate cancer prior to prostate biopsy

A main limitation of early prostate cancer (PCa) detection due to elevated PSA levels is caused by the low specificity of PSA, which is associated with a high proportion of men detected with nonmalignant findings at first or subsequent prostate biopsy (PBX). Multivariate prediction models, such as nomograms, have been developed, providing a more accurate method to prospectively determine the risk of a positive PBX. Combining established clinical risk factors with novel diagnostic markers of PCa appears promising to further improve predictive accuracy estimates. Ideally, these nomograms should be capable of identifying PCa at PBX without missing men with high-grade PCa, and preventing a significant proportion of men without, or with insignificant, PCa from undergoing PBX. The intention is to reduce disease morbidity and mortality by detecting significant PCa at an early stage, and at the same time to avoid overdiagnosis as well as overintervention.