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

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques

In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RADS) is combined with clinical variables predominantly based on logistic regression models. This study explores modeling using regularization techniques such as ridge regression, LASSO, elastic net, classification tree, tree ensemble models like random forest or XGBoost, and neural networks to predict CsPCa in a dataset of 4799 patients in Catalonia (Spain). An 80-20% split was employed for training and validation. We used predictor variables such as age, prostate-specific antigen (PSA), prostate volume, PSA density (PSAD), digital rectal exam (DRE) findings, family history of PCa, a previous negative biopsy, and PI-RADS categories. When considering a sensitivity of 0.9, in the validation set, the XGBoost model outperforms others with a specificity of 0.640, followed closely by random forest (0.638), neural network (0.634), and logistic regression (0.620). In terms of clinical utility, for a 10% missclassification of CsPCa, XGBoost can avoid 41.77% of unnecessary biopsies, followed closely by random forest (41.67%) and neural networks (41.46%), while logistic regression has a lower rate of 40.62%. Using SHAP values for model explainability, PI-RADS emerges as the most influential risk factor, particularly for individuals with PI-RADS 4 and 5. Additionally, a positive digital rectal examination (DRE) or family history of prostate cancer proves highly influential for certain individuals, while a previous negative biopsy serves as a protective factor for others.

Acceptability and perceptions of personalised risk-based cancer screening among health-care professionals and the general public: a systematic review and meta-analysis

BACKGROUND

Personalised risk-based screening (PRBS) can enhance the efficiency of cancer screening programnes, but little is known about support for its implementation among the general public and health-care professionals. We aimed to summarise the acceptability and perceptions of PRBS for breast, cervical, colorectal, lung, and prostate cancer screening among these groups.

METHODS

We conducted a systematic review and meta-analysis of original research studies reporting on breast, cervical, colorectal, lung, and prostate cancer screening; personalised risk assessments to guide PRBS; and the acceptability of and receptibility towards these approaches among the general public, health-care professionals, or both. We searched MEDLINE, Embase, Cochrane Central, PsycINFO, and CINAHL Plus for articles published between Jan 1, 2010, and April 30, 2024. Studies not reporting on the outcomes of interest and with insufficient data for analysis were excluded. Six reviewers independently screened articles, and risk of bias was assessed using the Mixed Methods Appraisal Tool. Qualitative data were analysed thematically. Quantitative data were analysed with use of random-effects meta-analysis for outcomes that had at least two studies. The study protocol was registered at PROSPERO, CRD42022354287.

FINDINGS

Our search identified 4491 unique records. After screening, 63 studies were included in our analysis, of which 36 (57%) included the general public, 21 (33%) included health-care professionals, and six (11%) included both. The majority of studies focused on breast cancer screening (43 [68%] studies), and were from North America (28 [44%]) and Europe (28 [44%]). Qualitative findings were analysed thematically, and the extracted quantitative findings were synthesised under the following topics: acceptability and perceptions of personalised risk assessments among the general public; acceptability and perceptions of PRBS among the general public; acceptability and perceptions of PRBS among health-care professionals; and barriers and facilitators to PRBS implementation among health-care professionals. The general public and health-care professionals generally found PRBS acceptable, but they needed more information about how risk was calculated and the accuracy of risk scores. Additionally, both groups were cautious about reducing screening frequencies for individuals at low risk and cited barriers such as the time and resources needed to implement an effective PRBS programme. The pooled estimate for acceptability of PRBS was 78% (95% CI 66-88) among the general public and 86% (64-99) among health-care professionals.

INTERPRETATION

The general public and health-care professionals both viewed personalised risk assessments as providing valuable information and PRBS as a logical next step to increase the quality of patient care and improve cancer mortality. However, implementation barriers at the public, health-care professional, and system level need to be addressed.

FUNDING

National Cancer Institute and Cancer Prevention and Research Institute of Texas.

PSA testing in primary care: is it time to change our practice?

BACKGROUND

Historical prostate-specific antigen (PSA)-based screening studies reduced prostate cancer-related deaths but also led to overdiagnosis/overtreatment. Since then, opportunistic PSA testing has increased, and late-stage diagnoses and prostate-cancer related deaths are rising.

OBJECTIVES

To review current trends regarding PSA testing in primary care and propose a collaborative approach to improve early prostate cancer detection.

DISCUSSION

Opportunistic PSA testing patterns vary among General Practitioners (GPs) and Family Doctors (FDs) based on differing guidelines, patient pressure, time constraints and personal views/preferences. However, an organised, risk-adapted strategy, as outlined by the European Association of Urology's guidelines, could facilitate the early diagnosis of significant prostate cancer whilst sparing those unlikely to experience disease-related symptoms from further tests (overdiagnosis) as well as the psychosocial consequences of a cancer diagnosis. This could be achieved by the introduction of national prostate cancer screening programmes, which has been endorsed in the European Commission's publication of the EU Cancer Screening Recommendations. In this scenario, GPs/FDs would still play an important role in supporting men throughout the decision pathway. However, as some men may still request a PSA test from their GP/FD, patient information as well as clear guidance and support to GPs/FDs are needed, including appropriate skills training to facilitate effective counselling and informed decision-making, and the use of risk calculators to inform referral decisions.

CONCLUSION

GPs/FDs play an important role in counselling healthy men eligible to consider PSA testing. However, clear guidance, training and support is required for them to assume this role.

Discrimination of serum samples of prostate cancer and benign prostatic hyperplasia with 1H-NMR metabolomics

Prostate cancer continues to be a prominent health concern for men globally. Current screening techniques, primarily the prostate-specific antigen (PSA) test and digital rectal examination (DRE), possess inherent limitations, with prostate biopsy being the definitive diagnostic procedure. The invasive nature of the biopsy and other drawbacks of current screening tests create the need for non-invasive and more accurate diagnostic methods. This study utilized 1H-NMR (Proton Nuclear Magnetic Resonance) based serum metabolomics to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Serum samples from 40 PCa and 41 BPH patients were analysed using 1H-NMR spectroscopy. PepsNMR was utilized for preprocessing the raw NMR data, and the binned spectra were examined for patterns distinguishing PCa and BPH. Principal component analysis (PCA) showed a moderate separation between PCa and BPH, highlighting the distinct metabolic profiles of both conditions. A logistic regression model was then developed, which demonstrated good performance in distinguishing between the two conditions. The results showed significant variance in multiple metabolites between PCa and BPH, such as isovaleric acid, ethylmalonic acid, formate, and glutamic acid. This research underlines the potential of 1H-NMR-based serum metabolomics as a promising tool for improved prostate cancer screening, offering an alternative to the limitations of current screening methods.

Novel machine learning algorithm in risk prediction model for pan-cancer risk: application in a large prospective cohort

Objective

To develop and validate machine-learning models that predict the risk of pan-cancer incidence using demographic, questionnaire and routine health check-up data in a large Asian population.

Methods and analysis

This study is a prospective cohort study including 433 549 participants from the prospective MJ cohort including a male cohort (n=208 599) and a female cohort (n=224 950).

Results

During an 8-year median follow-up, 5143 cancers occurred in males and 4764 in females. Compared with Lasso-Cox and Random Survival Forests, XGBoost showed superior performance for both cohorts. The XGBoost model with all 155 features in males and 160 features in females achieved an area under the curve (AUC) of 0.877 and 0.750, respectively. Light models with 31 variables for males and 11 variables for females showed comparable performance: an AUC of 0.876 (95% CI 0.858 to 0.894) in the overall population and 0.818 (95% CI 0.795 to 0.841) in those aged ≥40 years in the male cohort and an AUC of 0.746 (95% CI 0.721 to 0.771) in the overall population and 0.641 (95% CI 0.605 to 0.677) in those aged ≥40 years in the female cohort. High-risk individuals have at least ninefold higher risk of pan-cancer incidence compared with low-risk groups.

Conclusion

We developed and internally validated the first machine-learning models based on routine health check-up data to predict pan-cancer risk in the general population and achieved generally good discriminatory ability with a small set of predictors. External validation is warranted before the implementation of our risk model in clinical practice.

Comparison between the European Randomized Study for Screening of Prostate Cancer (ERSPC) and Prostate Biopsy Collaborative Group (PBCG) risk calculators: Prediction of clinically significant Prostate Cancer risk in a cohort of patients from Argentina

OBJECTIVE

To compare the performance of the risk calculators of the European Randomized Study for Screening of Prostate Cancer (ERSPC) and the Prostate Biopsy Collaborative Group (PBCG) in predicting the risk of presenting clinically significant prostate cancer.

MATERIAL AND METHODS

Retrospectively, patients who underwent prostate biopsy at Sanatorio Allende Cerro, Ciudad de Córdoba, Argentina, were identified from January 2018 to December 2021. The probability of having prostate cancer was calculated with the two calculators separately and then the results were compared to establish which of the two performed better. For this, areas under the curve (AUC) were analyzed.

RESULTS

250 patients were included, 140 (56%) presented prostate cancer, of which 92 (65.71%) had clinically significant prostate cancer (Gleason score ≥7). The patients who presented cancer were older, had a higher prostate-specific antigen (PSA) value, and had a smaller prostate size. The AUC to predict the probability of having clinically significant prostate cancer was 0.79 and 0.73 for PBCG-RC and ERSPC-RC respectively (P=0.0084).

CONCLUSION

In this cohort of patients, both prostate cancer risk calculators performed well in predicting clinically significant prostate cancer risk, although the PBCG-RC showed better accuracy.

Metabolic profiling in tissues and urine of patients with prostatic lesions and the diagnostic value of urine extracellular vesicles metabolites in prostate cancer

BACKGROUND

Prostate cancer (PCa) lacks convenient and highly specific diagnostic markers. Although the value of extracellular vesicles (EV) in oncology is widely recognized, the diagnostic value of EV metabolites requires further exploration. This study aimed to explore the diagnostic value of urine EV (u-EV) metabolomics in PCa.

METHODS

We first detected metabolites in paired tissues cells (cells), tissue EV (t-EVs), u-EVs, and urine samples in cohort 1 (8 PCa vs. 5 benign prostatic hypertrophy, BPH) to prob the feasibility of EV metabolites as diagnostic markers. We then analyzed the value of u-EVs as markers for PCa diagnosis and typing in the expanded sample cohort (60 PCa vs. 40 BPH).

RESULTS

U-EV metabolites were more consistent with those in tissue-derived samples (cells and t-EVs) than those in urine, and more differential metabolites between BPH and PCa were identified in u-EV. Subsequently, we used a random forest model to construct a panel of six metabolites for PCa, which showed an area under the curve (AUC) of 0.833 in training cohort and 0.844 in validation cohort. We also found significantly differentially expressed metabolites between PCa subtypes (Gleason ≤ 7 vs. Gleason > 7 and localized vs. metastasis), demonstrating the value of EV metabolites in PCa typing and prognostic assessment.

CONCLUSION

Metabolomic analysis of u-EVs is a promising source of noninvasive markers for PCa diagnosis.

Influential factors on urine EV DNA methylation detection and its diagnostic potential in prostate cancer

The value of Extracellular vesicles (EVs) diagnostic markers is widely recognized. However, current research on EV DNA remains limited. This study investigates the biological properties, preprocessing factors, and diagnostic potential of EV DNA. We found that DNA positive vesicles account for 23.3% ± 6.7% of the urine total EV, with a large amount of DNA attached to the outside. EV DNA fragments are large, there is no significant effect on uEV DNA when store urine less than 6 h at 4°C. In addition, the influence of different EV extraction methods on methylation detection is also minor. More importantly, RASSF1A methylation in urine total EV DNA can distinguish between PCa and BPH, with an AUC of 0.874. Our results suggest the potential of urine EV DNA as a novel marker for PCa diagnosis. This provides a new idea for the study of urinary tumor markers.

SIGNIFICANCE OF OSTEOPONTIN FOR PREDICTING AGGRESSIVENESS OF PROSTATE CANCER

BACKGROUND

Effective prediction of the course of prostate cancer (PCa) and the stratification of treatment tactics largely depend on the use of prognostic markers that reflect the molecular and biological features of tumors. In view of the important role of matricellular proteins in the modulation of the growing tumor and metastasis of the hormone-dependent neoplasms, the aim of the work was to study the expression of osteopontin (OPN) at the protein and mRNA levels in the PCa tissue in order to assess the significance of this protein for predicting the aggressiveness of PCa.

MATERIALS AND METHODS

The work is based on the analysis of the results of the examination and treatment of 83 patients with PCa of stages II-IV. The study of OPN expression at the level of mRNA and protein in the PCa tissue was carried out using methods of the real time polymerase chain reaction and immunohistochemistry, respectively.

RESULTS

The OPN expression in the PCa tissue was 1.6 times (p < 0.05) higher in patients with regional lymph node metastases compared to patients without metastases. In patients with a Gleason score of < 7, the OPN expression in the tumor tissue was 1.4 times lower (p < 0.05) than in patients with poorly differentiated PCa. In patients with a high risk of tumor progression, the OPN expression level was 1.4 and 2.1 times higher (p < 0.05) compared to patients with a moderate and low risk of PCa progression. The patients with a high OPN expression level in the PCa tissue had significantly decreased 2-year recurrence-free survival rate (by 25%).

CONCLUSIONS

The obtained results indicate the expediency of using OPN expression indicators in the tumor tissue to predict the PCa aggressiveness and assess the risk of its recurrence.

Navigating through the Controversies and Emerging Paradigms in Early Detection of Prostate Cancer: Bridging the Gap from Classic RCTs to Modern Population-Based Pilot Programs

Over the last three decades, the European Randomized Study of Screening for Prostate Cancer (ERSPC) and the US-based Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening have steered the conversation around the early detection of prostate cancer. These two randomized trials assessed the effect of screening on prostate cancer disease-specific mortality. Elevated PSA levels were followed by a systematic sextant prostate biopsy. Standard repeat testing intervals were applied. After controversies from 2009 to 2016 due to contradicting results of the two trials, the results aligned in 2016 and showed that early PSA detection reduces prostate cancer-specific mortality. However, overdiagnosis rates of up to 50% were reported, and this sparked an intense debate on harms and benefits for almost 20 years. The balance between harms and benefits is highly debated and has initiated further research to investigate new ways of early detection. In the meantime, the knowledge and tools for the diagnostic algorithm improved. This is a continuously ongoing effort which focuses on individual risk-based screening algorithms that preserve the benefits of the purely PSA-based screening algorithms, while reducing the side effects. An important push towards investigating new techniques for early detection came from the European Commission on the 20th of September 2022. The European Commission published its updated recommendation to investigate prostate, lung, and gastric cancer early detection programs. This opened a new window of opportunity to move away from the trial setting to population-based early detection settings. With this review, we aim to review 30 years of historical evidence of prostate cancer screening, which led to the initiation of the 'The Prostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project, which aims to encourage the early detection and diagnosis of PCa through customized and risk-based screening programs.

Beyond blood biomarkers: the role of SelectMDX in clinically significant prostate cancer identification

INTRODUCTION

New potential biomarkers to pre-intervention identification of a clinically significant prostate cancer (csPCa) will prevent overdiagnosis and overtreatment and limit quality of life impairment of PCa patients.

AREAS COVERED

We have developed a comprehensive review focusing our research on the increasing knowledge of the role of SelectMDX® in csPCa detection. Areas identified as clinically relevant are the ability of SelectMDX® to predict csPCa in active surveillance setting, its predictive ability when combined with multiparametric MRI and the role of SelectMDX® in the landscape of urinary biomarkers.

EXPERT OPINION

Several PCa biomarkers have been developed either alone or in combination with clinical variables to improve csPCa detection. SelectMDX® score includes genomic markers, age, PSA, prostate volume, and digital rectal examination. Several studies have shown consistency in the ability to improve detection of csPCa, avoidance of unnecessary prostate biopsies, helpful in decision-making for clinical benefit of PCa patients with future well designed, and impactful studies.

Screening for prostate cancer: evidence, ongoing trials, policies and knowledge gaps

Long-term screening with serum prostate-specific antigen (PSA) and systematic prostate biopsies can reduce prostate cancer mortality but leads to unacceptable overdiagnosis. Over the past decade, diagnostic methods have improved and the indolent nature of low-grade prostate cancer has been established. These advances now enable more selective detection of potentially lethal prostate cancer. This non-systematic review summarises relevant diagnostic advances, previous and ongoing screening trials, healthcare policies and important remaining knowledge gaps. Evidence synthesis and conclusions: The strong association between low serum PSA values and minimal long-term risk of prostate cancer death allows for adjusting screening intervals. Use of risk calculators, biomarkers and MRI to select men with a raised PSA value for biopsy and lesion-targeting rather than systematic prostate biopsies reduce the detection of low-grade cancer and thereby overdiagnosis. These improvements recently led the European Union to recommend its member states to evaluate the feasibility and effectiveness of organised screening programmes for prostate cancer. Nonetheless, important knowledge gaps remain such as the performance of modern diagnostic methods in long-term screening programmes and their impact on mortality. The knowledge gaps are currently being addressed in three large randomised screening trials. Population-based pilot programmes will contribute critical practical experience.

Systematic Evaluation of Studies in the Fields of Diagnosis and Management of Prostate Cancer in Coronavirus Disease 2019 Era

Background: Prostate cancer is one of the most common cancers worldwide. The proper management of this cancer during the coronavirus disease 2019 (COVID-19) or similar outbreaks could be a serious challenge. Proper timing of surgery, radiotherapy, and other medical modalities are essential in providing the most effective treatment. Objectives: This systematic review aimed at evaluating the proper management of prostate cancer during the COVID-19 outbreak. Methods: This study was conducted from 2019 to 2022. An internet search was conducted using the keywords: Diagnosis, management, radical prostatectomy, radiotherapy, hormone ablation therapy, chemotherapy and prostate cancer, and COVID-19. The visited databases included PubMed, Scopus, Web of Sciences, Google Scholar, and Scientific Information Database. The review was performed based on the preferred reporting items for a systematic review and meta-analyses (PRISMA) guidelines. Results: Postponing the biopsy for up to three months and adopting of non-invasive diagnostic methods were likely reasonable during the COVID-19 pandemic. Patients with cancer were more prone to severe injuries and were more likely to have serious complications. Surgery, radiation therapy, brachytherapy, palliative radiation, hormone ablation therapy, and chemotherapy were among the pre-institutional treatments that had to be performed according to medical protocols as well as health and professional guidelines. Conclusions: It was recommended that the prostate cancer screening should not be performed for asymptomatic men during the COVID-19 outbreak. It was also suggested that the treatment should be performed in the shortest possible time and in the safest way.

Underlying Features of Prostate Cancer-Statistics, Risk Factors, and Emerging Methods for Its Diagnosis

Prostate cancer (PCa) is the most frequently occurring type of malignant tumor and a leading cause of oncological death in men. PCa is very heterogeneous in terms of grade, phenotypes, and genetics, displaying complex features. This tumor often has indolent growth, not compromising the patient's quality of life, while its more aggressive forms can manifest rapid growth with progression to adjacent organs and spread to lymph nodes and bones. Nevertheless, the overtreatment of PCa patients leads to important physical, mental, and economic burdens, which can be avoided with careful monitoring. Early detection, even in the cases of locally advanced and metastatic tumors, provides a higher chance of cure, and patients can thus go through less aggressive treatments with fewer side effects. Furthermore, it is important to offer knowledge about how modifiable risk factors can be an effective method for reducing cancer risk. Innovations in PCa diagnostics and therapy are still required to overcome some of the limitations of the current screening techniques, in terms of specificity and sensitivity. In this context, this review provides a brief overview of PCa statistics, reporting its incidence and mortality rates worldwide, risk factors, and emerging screening strategies.

Foggia Prostate Cancer Risk Calculator 2.0: A Novel Risk Calculator including MRI and Bladder Outlet Obstruction Parameters to Reduce Unnecessary Biopsies

Risk calculator (RC) combining PSA with other clinical information can help to better select patients at risk of prostate cancer (PCa) for prostate biopsy. The present study aimed to develop a new Pca RC, including MRI and bladder outlet obstruction parameters (BOOP). The ability of these parameters in predicting PCa and clinically significant PCa (csPCa: ISUP GG ≥ 2) was assessed by binary logistic regression. A total of 728 patients were included from two institutions. Of these, 395 (54.3%) had negative biopsies and 161 (22.11%) and 172 (23.6%) had a diagnosis of ISUP GG1 PCa and csPCa. The two RC ultimately included age, PSA, DRE, prostate volume (pVol), post-voided residual urinary volume (PVR), and PIRADS score. Regarding BOOP, higher prostate volumes (csPCa: OR 0.98, CI 0.97,0.99) and PVR ≥ 50 mL (csPCa: OR 0.27, CI 0.15, 0.47) were protective factors for the diagnosis of any PCa and csPCa. AUCs after internal validation were 0.78 (0.75, 0.82) and 0.82 (0.79, 0.86), respectively. Finally, decision curves analysis demonstrated higher benefit compared to the first-generation calculator and MRI alone. These novel RC based on MRI and BOOP may help to better select patient for prostate biopsy after prostate MRI.

Screening for prostate cancer: protocol for updating multiple systematic reviews to inform a Canadian Task Force on Preventive Health Care guideline update

PURPOSE

To inform updated recommendations by the Canadian Task Force on Preventive Health Care on screening for prostate cancer in adults aged 18 years and older in primary care. This protocol outlines the planned scope and methods for a series of systematic reviews.

METHODS

Updates of two systematic reviews and a de novo review will be conducted to synthesize the evidence on the benefits and harms of screening for prostate cancer with a prostate-specific antigen (PSA) and/or digital rectal examination (DRE) (with or without additional information) and patient values and preferences. Outcomes for the benefits of screening include reduced prostate cancer mortality, all-cause mortality, and incidence of metastatic prostate cancer. Outcomes for the harms of screening include false-positive screening tests, overdiagnosis, complications due to biopsy, and complications of treatment including incontinence (urinary or bowel), and erectile dysfunction. The quality of life or functioning (overall and disease-specific) and psychological effects outcomes are considered as a possible benefit or harm. Outcomes for the values and preferences review include quantitative or qualitative information regarding the choice to screen or intention to undergo screening. For the reviews on benefits or harms, we will search for randomized controlled trials, quasi-randomized, and controlled studies in MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. For the review on values and preferences, we will search for experimental or observational studies in MEDLINE, Embase, and PsycInfo. For all reviews, we will also search websites of relevant organizations, gray literature, and reference lists of included studies. Title and abstract screening, full-text review, data extraction, and risk of bias assessments will be completed independently by pairs of reviewers with any disagreements resolved by consensus or by consulting with a third reviewer. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach will be used to assess the certainty of the evidence for each outcome.

DISCUSSION

The series of systematic reviews will be used by the Canadian Task Force on Preventive Health Care to update their 2014 guideline on screening for prostate cancer in adults aged 18 years and older. Systematic review registration This review has been registered with PROSPERO (CRD42022314407) and is available on the Open Science Framework (osf.io/dm32k).

Magnetic Resonance Imaging-Based Predictive Models for Clinically Significant Prostate Cancer: A Systematic Review

Simple Summary

Magnetic resonance imaging (MRI) has allowed the early detection of PCa to evolve towards clinically significant PCa (csPCa), decreasing unnecessary prostate biopsies and overdetection of insignificant tumours. MRI identifies suspicious lesions of csPCa, predicting the semi-quantitative risk through the prostate imaging report and data system (PI-RADS), and enables guided biopsies, increasing the sensitivity of csPCa. Predictive models that individualise the risk of csPCa have also evolved adding PI-RADS score (MRI-PMs), improving the selection of candidates for prostate biopsy beyond the PI-RADS category. During the last five years, many MRI-PMs have been developed. Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness through a systematic review. We have found high heterogeneity between MRI technique, PI-RADS versions, biopsy schemes and approaches, and csPCa definitions. MRI-PMs outperform the selection of candidates for prostate biopsy beyond MRI alone and PMs based on clinical predictors. However, few developed MRI-PMs are externally validated or have available risk calculators (RCs), which constitute the appropriate requirements used in routine clinical practice.

Abstract

MRI can identify suspicious lesions, providing the semi-quantitative risk of csPCa through the Prostate Imaging-Report and Data System (PI-RADS). Predictive models of clinical variables that individualise the risk of csPCa have been developed by adding PI-RADS score (MRI-PMs). Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness. A systematic review was performed after a literature search performed by two independent investigators in PubMed, Cochrane, and Web of Science databases, with the Medical Subjects Headings (MESH): predictive model, nomogram, risk model, magnetic resonance imaging, PI-RADS, prostate cancer, and prostate biopsy. This review was made following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria and studied eligibility based on the Participants, Intervention, Comparator, and Outcomes (PICO) strategy. Among 723 initial identified registers, 18 studies were finally selected. Warp analysis of selected studies was performed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical predictors in addition to the PI-RADS score in developed MRI-PMs were age, PCa family history, digital rectal examination, biopsy status (initial vs. repeat), ethnicity, serum PSA, prostate volume measured by MRI, or calculated PSA density. All MRI-PMs improved the prediction of csPCa made by clinical predictors or imaging alone and achieved most areas under the curve between 0.78 and 0.92. Among 18 developed MRI-PMs, 7 had any external validation, and two RCs were available. The updated PI-RADS version 2 was exclusively used in 11 MRI-PMs. The performance of MRI-PMs according to PI-RADS was only analysed in a single study. We conclude that MRI-PMs improve the selection of candidates for prostate biopsy beyond the PI-RADS category. However, few developed MRI-PMs meet the appropriate requirements in routine clinical practice.

How well do polygenic risk scores identify men at high risk for prostate cancer? Systematic review and meta-analysis

OBJECTIVES

Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men.

PATIENTS AND METHODS

Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Meta-regression was used to assess the impact of number of single-nucleotide polymorphisms (SNPs) incorporated in PRS on AUC. Heterogeneity is expressed as I² scores. Publication bias was evaluated using funnel plots and Egger tests.

RESULTS

The ability of PRS to identify men with PCa was modest (pooled AUC 0.63, 95% CI 0.62-0.64) with moderate consistency (I² 64%). Combining PRS with clinical variables increased the pooled AUC to 0.74 (0.68-0.81). Meta-regression showed only negligible increase in AUC for adding incremental SNPs. Despite moderate heterogeneity, publication bias was not evident.

CONCLUSION

Typically, PRS accuracy is comparable to PSA or family history with a pooled AUC value 0.63 indicating mediocre performance for PRS alone.

Serum PSA-based early detection of prostate cancer in Europe and globally: past, present and future

In the pre-PSA-detection era, a large proportion of men were diagnosed with metastatic prostate cancer and died of the disease; after the introduction of the serum PSA test, randomized controlled prostate cancer screening trials in the USA and Europe were conducted to assess the effect of PSA screening on prostate cancer mortality. Contradictory outcomes of the trials and the accompanying overdiagnosis resulted in recommendations against prostate cancer screening by organizations such as the United States Preventive Services Task Force. These recommendations were followed by a decline in PSA testing and a rise in late-stage diagnosis and prostate cancer mortality. Re-evaluation of the randomized trials, which accounted for contamination, showed that PSA-based screening does indeed reduce prostate cancer mortality; however, the debate about whether to screen or not to screen continues because of the considerable overdiagnosis that occurs using PSA-based screening. Meanwhile, awareness among the population of prostate cancer as a potentially lethal disease stimulates opportunistic screening practices that further increase overdiagnosis without the benefit of mortality reduction. However, in the past decade, new screening tools have been developed that make the classic PSA-only-based screening an outdated strategy. With improved use of PSA, in combination with age, prostate volume and with the application of prostate cancer risk calculators, a risk-adapted strategy enables improved stratification of men with prostate cancer and avoidance of unnecessary diagnostic procedures. This combination used with advanced detection techniques (such as MRI and targeted biopsy), can reduce overdiagnosis. Moreover, new biomarkers are becoming available and will enable further improvements in risk stratification.

High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer-A Modeling Study

Simple Summary

Hormonal therapy for prostate cancer is often applied past the point of resistance, hence losing any future clinical value to the evolution of resistant strains. If the undesirable outcome of the treatment is forewarned, then clinicians can have an opportunity to adjust the treatment, which can result in better management of the cancer. Using a mechanistic mathematical model, we introduce two methods to enhance the accuracy of classical biomarkers for hormonal therapy failure. Our results show the value in measuring both prostate-specific antigen and androgen during hormonal treatment, which can potentially allow for better management of prostate cancer.

Abstract

Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer.

Volatilomics: An Emerging and Promising Avenue for the Detection of Potential Prostate Cancer Biomarkers

Simple Summary

The lack of highly specific and sensitive biomarkers for the early detection of prostate cancer (PCa) is a major barrier to its management. Volatilomics emerged as a non-invasive, simple, inexpensive, and easy-to-use approach for cancer screening, characterization of disease progression, and follow-up of the treatment’s success. We provide a brief overview of the potential of volatile organic metabolites (VOMs) for the establishment of PCa biomarkers from non-invasive matrices. Endogenous VOMs have been investigated as potential biomarkers since changes in these VOMs can be characteristic of specific disease processes. Recent studies have shown that the conjugation of the prostate-specific antigen (PSA) screening with other methodologies, such as risk calculators, biomarkers, and imaging tests, can attenuate overdiagnosis and under-detection issues. This means that the combination of volatilomics with other methodologies could be extremely valuable for the differentiation of clinical phenotypes in a group of patients, providing more personalized treatments.

Abstract

Despite the spectacular advances in molecular medicine, including genomics, proteomics, transcriptomics, lipidomics, and personalized medicine, supported by the discovery of the human genome, prostate cancer (PCa) remains the most frequent malignant tumor and a leading cause of oncological death in men. New methods for prognostic, diagnostic, and therapy evaluation are mainly based on the combination of imaging techniques with other methodologies, such as gene or protein profiling, aimed at improving PCa management and surveillance. However, the lack of highly specific and sensitive biomarkers for its early detection is a major hurdle to this goal. Apart from classical biomarkers, the study of endogenous volatile organic metabolites (VOMs) biosynthesized by different metabolic pathways and found in several biofluids is emerging as an innovative, efficient, accessible, and non-invasive approach to establish the volatilomic biosignature of PCa patients, unravelling potential biomarkers. This review provides a brief overview of the challenges of PCa screening methods and emergent biomarkers. We also focus on the potential of volatilomics for the establishment of PCa biomarkers from non-invasive matrices.

External validation of two mpMRI-risk calculators predicting risk of prostate cancer before biopsy

Purpose

Risk calculators (RC) aim to improve prebiopsy risk stratification. Their latest versions now include multiparametric magnetic resonance imaging (mpMRI) findings. For their implementation into clinical practice, critical external validations are needed.

Methods

We retrospectively analyzed the patient data of 554 men who underwent ultrasound-guided targeted and systematic prostate biopsies at 2 centers. We validated the mpMRI-RCs of Radtke et al. (RC-R) and Alberts et al. (RC-A), previously shown to predict prostate cancer (PCa) and clinically significant PCa (csPCa). We assessed these RCs’ prediction accuracy by analyzing the receiver-operating characteristics (ROC) curve and evaluated their clinical utility using Decision Curve Analysis (DCA), including Net-Benefit and Net-Reduction curves.

Results

We found that the Area Under the ROC Curve (AUC) for predicting PCa was 0.681 [confidence interval (CI) 95% 0.635–0.727] for RC-A. The AUCs for predicting csPCa were 0.635 (CI 95% 0.583–0.686) for RC-A and 0.676 (CI 95% 0.627–0.725) for RC-R. For example, at a risk threshold of 12%, RC-A needs to assess 334 and RC-R 500 patients to detect one additional true positive PCa or csPCa patient, respectively. At the same risk threshold of 12%, RC-A only needs to assess 6 and RC-R 16 patients to detect one additional true negative PCa or csPCa patient.

Conclusion

The mpMRI-RCs, RC-R and RC-A, are robust and valuable tools for patient counseling. Although they do not improve PCa and csPCa detection rates by a clinically meaningful margin, they aid in avoiding unnecessary prostate biopsies. Their implementation could reduce overdiagnosis and reduce PCa screening morbidity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-022-04119-8.

External Validation of the Prostate Biopsy Collaborative Group Risk Calculator and the Rotterdam Prostate Cancer Risk Calculator in a Swedish Population-based Screening Cohort

Background

External validation of risk calculators (RCs) is necessary to determine their clinical applicability beyond the setting in which these were developed.

Objective

To assess the performance of the Rotterdam Prostate Cancer RC (RPCRC) and the Prostate Biopsy Collaborative Group RC (PBCG-RC).

Design, setting, and participants

We used data from the prospective, population-based STHLM3 screening study, performed in 2012–2015. Participants with prostate-specific antigen ≥3 ng/ml who underwent systematic prostate biopsies were included.

Outcome measurements and statistical analysis

Probabilities for clinically significant prostate cancer (csPCa), defined as International Society of Urological Pathology grade ≥2, were calculated for each participant. External validity was assessed by calibration, discrimination, and clinical usefulness for both original and recalibrated models.

Results and limitations

Out of 5841 men, 1054 (18%) had csPCa. Distribution of risk predictions differed between RCs; median risks for csPCa using the RPCRC and PBCG-RC were 3.3% (interquartile range [IQR] 2.1–7.1%) and 20% (IQR 15–28%), respectively. The correlation between RC risk estimates on individual level was moderate (Spearman’s r = 0.55). Using the RPCRC’s recommended risk threshold of ≥4% for finding csPCa, 36% of participants would get concordant biopsy recommendations. At 10% risk cut-off, RCs agreed in 23% of cases. Both RCs showed good discrimination, with areas under the curves for the RPCRC of 0.74 (95% confidence interval [CI] 0.72–0.76) and the PBCG-RC of 0.70 (95% CI 0.68–0.72). Calibration was adequate using the PBCG-RC (calibration slope: 1.13 [95% CI 1.03–1.23]), but the RPCRC underestimated the risk of csPCa (calibration slope: 0.73 [0.68–0.79]). The PBCG-RC showed a net benefit in a decision curve analysis, whereas the RPCRC showed no net benefit at clinically relevant risk threshold levels. Recalibration improved clinical benefit, and differences between RCs decreased.

Conclusions

Assessment of calibration is essential to ensure the clinical value of risk prediction tools. The PBCG-RC provided clinical benefit in its current version online. On the contrary, the RPCRC cannot be recommended in this setting.

Patient summary

Predicting the probability of finding prostate cancer on biopsy differed between two assessed risk calculators. After recalibration, the agreement of the models improved, and both were shown to be clinically useful.

Application of Proteogenomics to Urine Analysis towards the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study

Simple Summary

Prostate cancer (PCa) is one of the most common cancers. Due to the limited and invasive approaches for PCa diagnosis, it is crucial to identify more accurate and non-invasive biomarkers for its detection. The aim of our study was to non-invasively uncover new protein targets for detecting PCa using a proteomics and proteogenomics approach. This work identified several dysregulated mutant protein isoforms in urine from PCa patients, some of them predicted to have a protective or an adverse role in these patients. These results are promising given urine’s non-invasive nature and offers an auspicious opportunity for research and development of PCa biomarkers.

Abstract

To identify new protein targets for PCa detection, first, a shotgun discovery experiment was performed to characterize the urinary proteome of PCa patients. This revealed 18 differentially abundant urinary proteins in PCa patients. Second, selected targets were clinically tested by immunoblot, and the soluble E-cadherin fragment was detected for the first time in the urine of PCa patients. Third, the proteogenome landscape of these PCa patients was characterized, revealing 1665 mutant protein isoforms. Statistical analysis revealed 6 differentially abundant mutant protein isoforms in PCa patients. Analysis of the likely effects of mutations on protein function and PPIs involving the dysregulated mutant protein isoforms suggests a protective role of mutations HSPG2*Q1062H and VASN*R161Q and an adverse role of AMBP*A286G and CD55*S162L in PCa patients. This work originally characterized the urinary proteome, focusing on the proteogenome profile of PCa patients, which is usually overlooked in the analysis of PCa and body fluids. Combined analysis of mass spectrometry data using two different software packages was performed for the first time in the context of PCa, which increased the robustness of the data analysis. The application of proteogenomics to urine proteomic analysis can be very enriching in mutation-related diseases such as cancer.

A Head-to-head Comparison of Prostate Cancer Diagnostic Strategies Using the Stockholm3 Test, Magnetic Resonance Imaging, and Swedish National Guidelines: Results from a Prospective Population-based Screening Study

Background

Strategies for early detection of prostate cancer aim to detect clinically significant prostate cancer (csPCa) and avoid detection of insignificant cancers and unnecessary biopsies. Swedish national guidelines (SNGs), years 2019 and 2020, involve prostate-specific antigen (PSA) testing, clinical variables, and magnetic resonance imaging (MRI). The Stockholm3 test and MRI have been suggested to improve selection of men for prostate biopsy. Performance of SNGs compared with the Stockholm3 test or MRI in a screening setting is unclear.

Objective

To compare strategies based on previous and current national guidelines, Stockholm3, and MRI to select patients for biopsy in a screening-by-invitation setting.

Design, setting, and participants

All participants underwent PSA test, and men with PSA ≥3 ng/ml underwent Stockholm3 testing and MRI. Men with Stockholm3 ≥11%, Prostate Imaging Reporting and Data System score ≥3 on MRI, or indication according to SNG-2019 or SNG-2020 were referred to biopsy.

Outcome measurements and statistical analysis

The primary outcome was the detection of csPCa at prostate biopsy, defined as an International Society of Urological Pathology (ISUP) grade of ≥2.

Results and limitations

We invited 8764 men from the general population, 272 of whom had PSA ≥3 ng/ml. The median PSA was 4.1 (interquartile range: 3.4–5.8), and 136 of 270 (50%) who underwent MRI lacked any pathological lesions. In total, 37 csPCa cases were diagnosed. Using SNG-2019, 36 csPCa cases with a high biopsy rate (179 of 272) were detected and 49 were diagnosed with ISUP 1 cancers. The Stockholm3 strategy diagnosed 32 csPCa cases, with 89 biopsied and 27 ISUP 1 cancers. SNG-2020 detected 32 csPCa and 33 ISUP 1 cancer patients, with 99 men biopsied, and the MRI strategy detected 30 csPCa and 35 ISUP 1 cancer cases by biopsying 123 men. The latter two strategies generated more MRI scans than the Stockholm3 strategy (n = 270 vs 33).

Conclusions

Previous guidelines provide high detection of significant cancer but at high biopsy rates and detection of insignificant cancer. The Stockholm3 test may improve diagnostic precision compared with the current guidelines or using only MRI.

Patient summary

The Stockholm3 test facilitates detection of significant cancer, and reduces the number of biopsies and detection of insignificant cancer.

Development of a nomogram combining multiparametric magnetic resonance imaging and PSA-related parameters to enhance the detection of clinically significant cancer across different region

OBJECTIVE

Prostate cancer (PCa) is the most prevalent cancer among males. This study attempted to develop a clinically significant prostate cancer (csPCa) risk nomogram including Prostate Imaging-Reporting and Data System (PI-RADS) score and other clinical indexes for initial prostate biopsy in light of the different prostate regions, and internal validation was further conducted.

PATIENTS AND METHODS

A retrospective study was performed including 688 patients who underwent ultrasound-guided transperineal magnetic resonance imaging fusion prostate biopsy from December 2016 to July 2019. We constructed nomograms combining PI-RADS score and clinical variables (prostate-specific antigen [PSA], prostate volume (PV), age, free/total PSA, and PSA density) through univariate and multivariate logistic regression to identify patients eligible for biopsy. The performance of the predictive model was evaluated by bootstrap resampling. The area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis was appointed to quantify the accuracy of the primary nomogram model for csPCa. Calibration curves were used to assess the agreement between the biopsy specimen and the predicted probability of the new nomogram. The χ² test was also applied to evaluate the heterogeneity between fusion biopsy and systematic biopsy based on different PI-RADS scores and prostate regions.

RESULTS

A total of 320 of 688 included patients were diagnosed with csPCa. csPCa was defined as Gleason score ≥7. The ROC and concordance-index both presented good performance. The nomogram reached an AUC of 0.867 for predicting csPCa at the peripheral zone; meanwhile, AUC for transitional and apex zones were 0.889 and 0.757, respectively. Statistical significance was detected between fusion biopsy and systematic biopsy for PI-RADS score >3 lesions and lesions at the peripheral and transitional zones.

CONCLUSION

We produced a novel nomogram predicting csPCa in patients with suspected imaging according to different locations. Our results indicated that PI-RADS score combined with other clinical parameters showed a robust predictive capacity for csPCa before prostate biopsy. The new nomogram, which incorporates prebiopsy data including PSA, PV, age, and PI-RADS score, can be helpful for clinical decision-making to avoid unnecessary biopsy.

Predictors of clinically significant prostate cancer in biopsy-naïve and prior negative biopsy men with a negative prostate MRI: improving MRI-based screening with a novel risk calculator

Purpose:

A pre-biopsy decision aid is needed to counsel men with a clinical suspicion for clinically significant prostate cancer (csPCa), despite normal prostate magnetic resonance imaging (MRI).

Methods:

A risk calculator (RC) for csPCa (International Society of Urological Pathology grade group (ISUP) ⩾ 2) presence in men with a negative-MRI (Prostate Imaging–Reporting and Data System (PI-RADS) ⩽ 2) was developed, and its performance was compared with RCs of the European Randomized Study of Screening for Prostate Cancer (ERSPC), Prostate Biopsy Collaborative Group (PBCG), and Prospective Loyola University mpMRI (PLUM). All biopsy-naïve and prior negative biopsy men with a negative-MRI followed by systematic prostate biopsy were included from October 2015 to September 2021. The RC was developed using multivariable logistic regression with the following parameters: age (years), family history of PCa (first- or second-degree family member), ancestry (African Caribbean/other), digital rectal exam (benign/malignant), MRI field strength (1.5/3.0 Tesla), prior negative biopsy status, and prostate-specific antigen (PSA) density (ng/ml/cc). Performance of RCs was compared using receiver operating characteristic (ROC) curve analysis.

Results:

A total of 232 men were included for analysis, of which 18.1% had csPCa. Parameters associated with csPCa were family history of PCa (p < 0.0001), African Caribbean ancestry (p = 0.005), PSA density (p = 0.002), prior negative biopsy (p = 0.06), and age at biopsy (p = 0.157). The area under the curve (AUC) of the developed RC was 0.76 (95% CI 0.68–0.85). This was significantly better than the RCs of the ERSPC (AUC: 0.59; p = 0.001) and PBCG (AUC: 0.60; p = 0.002), yet similar to PLUM (AUC: 0.69; p = 0.09).

Conclusion:

The developed RC (Prostate Biopsy Cohort Amsterdam (‘PROBA’ RC), integrated predictors for csPCa at prostate biopsy in negative-MRI men and outperformed other widely used RCs. These findings require external validation before introduction in daily practice.

CRPMKB: a knowledge base of cancer risk prediction models for systematic comparison and personalized applications

MOTIVATION

In the era of big data and precision medicine, accurate risk assessment is a prerequisite for the implementation of risk screening and preventive treatment. A large number of studies have focused on the risk of cancer, and related risk prediction models have been constructed, but there is a lack of effective resource integration for systematic comparison and personalized applications. Therefore, the establishment and analysis of the cancer risk prediction model knowledge base (CRPMKB) is of great significance.

RESULTS

The current knowledge base contains 802 model data. The model comparison indicates that the accuracy of cancer risk prediction was greatly affected by regional differences, cancer types and model types. We divided the model variables into four categories: environment, behavioral lifestyle, biological genetics and clinical examination, and found that there are differences in the distribution of various variables among different cancer types. Taking 50 genes involved in the lung cancer risk prediction models as an example to perform pathway enrichment analyses and the results showed that these genes were significantly enriched in p53 Signaling and Aryl Hydrocarbon Receptor Signaling pathways which are associated with cancer and specific diseases. In addition, we verified the biological significance of overlapping lung cancer genes via STRING database. CRPMKB was established to provide researchers an online tool for the future personalized model application and developing. This study of CRPMKB suggests that developing more targeted models based on specific demographic characteristics and cancer types will further improve the accuracy of cancer risk model predictions.

AVAILABILITY AND IMPLEMENTATION

CRPMKB is freely available at http://www.sysbio.org.cn/CRPMKB/. The data underlying this article are available in the article and in its online supplementary material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Prostate Microbiota and Prostate Cancer: A New Trend in Treatment

Although the incidence and mortality of prostate cancer have gradually begun to decline in the past few years, it is still one of the leading causes of death from malignant tumors in the world. The occurrence and development of prostate cancer are affected by race, family history, microenvironment, and other factors. In recent decades, more and more studies have confirmed that prostate microflora in the tumor microenvironment may play an important role in the occurrence, development, and prognosis of prostate cancer. Microorganisms or their metabolites may affect the occurrence and metastasis of cancer cells or regulate anti-cancer immune surveillance. In addition, the use of tumor microenvironment bacteria in interventional targeting therapy of tumors also shows a unique advantage. In this review, we introduce the pathway of microbiota into prostate cancer, focusing on the mechanism of microorganisms in tumorigenesis and development, as well as the prospect and significance of microorganisms as tumor biomarkers and tumor prevention and treatment.

A Polygenic Risk Score Predicts Incident Prostate Cancer Risk in Older Men but Does Not Select for Clinically Significant Disease

Despite the high prevalence of prostate cancer in older men, the predictive value of a polygenic risk score (PRS) remains uncertain in men aged ≥70 years. We used a 6.6 million-variant PRS to predict the risk of incident prostate cancer in a prospective study of 5701 men of European descent aged ≥70 years (mean age 75 years) enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) clinical trial. The study endpoint was prostate cancer, including metastatic or non-metastatic disease, confirmed by an expert panel. After excluding participants with a history of prostate cancer at enrolment, we used a multivariable Cox proportional hazards model to assess the association between the PRS and incident prostate cancer risk, adjusting for covariates. Additionally, we examined the distribution of Gleason grade groups by PRS group to determine if a higher PRS was associated with higher grade disease. We tested for interaction between the PRS and aspirin treatment. Logistic regression was used to independently assess the association of the PRS with prevalent (pre-trial) prostate cancer, reported in medical histories. During a median follow-up time of 4.6 years, 218 of the 5701 participants (3.8%) were diagnosed with prostate cancer. The PRS predicted incident risk with a hazard ratio (HR) of 1.52 per standard deviation (SD) (95% confidence interval (CI) 1.33-1.74, p < 0.001). Men in the top quintile of the PRS distribution had an almost three times higher risk of prostate cancer than men in the lowest quintile (HR = 2.99 (95% CI 1.90-4.27), p < 0.001). However, a higher PRS was not associated with a higher Gleason grade groups. We found no interaction between aspirin treatment and the PRS for prostate cancer risk. The PRS was also associated with prevalent prostate cancer (odds ratio = 1.80 per SD (95% CI 1.65-1.96), p < 0.001).While a PRS for prostate cancer is strongly associated with incident risk in men aged ≥70 years, the clinical utility of the PRS as a biomarker is currently limited by its inability to select for clinically significant disease.

Role of 68Ga-PSMA-PET/CT for the detection of primary prostate cancer prior to biopsy: a prospective study

Introduction

Prostate-specific membrane antigen (PSMA) positron emission tomography/ computed tomography (PET-CT) is widely used as a staging tool for patients with prostate cancer (PCa). The objective of the study is to assess the diagnostic accuracy of 68Ga-PSMA-PET/CT for PCa, which may help us avoid unnecessary biopsies in patients with intermediate prostate-specific antigen (PSA) levels.

Material and methods

In this prospective study, 81 patients suspected of PCa, with either raised PSA between 4-20 ng/ml or abnormal digital rectal examination (DRE) findings were included. 68Ga-PSMA-PET/CT was performed for all patients followed by transrectal ultrasound (TRUS) guided prostate biopsy. SUVmax (maximum standardized uptake value) was measured and correlated with biopsy results.

Results

Out of 81 patients, 31 (38.3%) patients were found to have malignancy on biopsy. Median SUVmax of biopsy positive patients was 10.4 (IQR 6.5-16.1) and biopsy negative patients (n=50) was 3.5 (IQR 1-4.9), (p <0.001). At a cut-off of 6.15, 68GA-PSMA-PET/CT demonstrated sensitivity of 84%, specificity of 80%, positive predictive value of 72.2%, negative predictive value of 88.9% and accuracy of 81.5% with an AUC of 0.876 (95% CI: 0.799-0.953, p <0.001).

Conclusions

The 68Ga-PSMA-PET/CT helps to localize suspicious lesions and improving the detection of primary prostate cancer. Our findings indicate a significant correlation of SUVmax values with biopsy results. We were also able to determine a cut-off value of SUVmax below which prostate biopsy can be avoided in selected patients.

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.

Prostate-specific Antigen Testing as Part of a Risk-Adapted Early Detection Strategy for Prostate Cancer: European Association of Urology Position and Recommendations for 2021

BACKGROUND

Recommendations against prostate-specific antigen (PSA) testing in 2012 have increased advanced-stage diagnosis and prostate cancer-specific mortality rates.

OBJECTIVE

To present the position of the European Association of Urology (EAU) in 2021 and provide recommendations for the use of PSA testing as part of a risk-adapted strategy for the early detection of prostate cancer.

EVIDENCE ACQUISITION

The authors combined their review of relevant literature, including the EAU prostate cancer guidelines 2021 update, with their own knowledge to provide an expert opinion, representing the EAU's position in 2021.

EVIDENCE SYNTHESIS

The EAU has developed a risk-adapted early prostate cancer detection strategy for well-informed men based on PSA testing, risk calculators, and multiparametric magnetic resonance imaging, which can differentiate significant from insignificant prostate cancer. This approach largely avoids the overdiagnosis/overtreatment of men unlikely to experience disease-related symptoms during their lifetime and facilitates an early diagnosis of men with significant cancer to receive active treatment. It also reduces advanced-stage diagnosis, thereby potentially reducing prostate cancer-specific mortality and improving quality of life. Education is required among urologists, general practitioners, radiologists, policy makers, and healthy men, including endorsement by the European Commission to adapt the European Council's screening recommendations in its 2022 plan and requests to individual countries for its incorporation into national cancer plans.

CONCLUSIONS

This risk-adapted approach for the early detection of prostate cancer will reverse current unfavourable trends and ultimately save lives.

PATIENT SUMMARY

The European Association of Urology has developed a patient information leaflet and algorithm for the early diagnosis of prostate cancer. It can identify men who do not need magnetic resonance imaging or a biopsy and those who would not show any symptoms versus those with more aggressive disease who require further tests/treatment. We need to raise awareness of this algorithm to ensure that all well-informed men at risk of significant prostate cancer are offered a prostate-specific antigen test.

A European Model for an Organised Risk-stratified Early Detection Programme for Prostate Cancer

CONTEXT

Overdiagnosis as the argument to stop prostate cancer (PCa) screening is less valid since the introduction of new technologies such as risk calculators (RCs) and magnetic resonance imaging (MRI). These new technologies result in fewer unnecessary biopsy procedures and fewer cases of both overdiagnosis and underdetection. Therefore, we can now adequately respond to the growing and urgent need for a structured risk assessment to detect PCa early.

OBJECTIVE

To provide expert discussion on the existing evidence for a previously published risk-stratified strategy regarding an organised population-based early detection programme for PCa.

EVIDENCE ACQUISITION

The proposed algorithm for early detection of PCa emerged from expert consensus by the authors based on available evidence derived from a nonsystematic review of the current literature using Medline/PubMed, Cochrane Library database, ClinicalTrials.gov, ISRCTN Registry, and the European Association of Urology guidelines on PCa.

EVIDENCE SYNTHESIS

Although not confirmed by the highest level of evidence, current literature and guidelines point towards an algorithm for early detection of PCa that starts with risk-based prostate-specific antigen (PSA) testing, followed by multivariable risk stratification with RCs. All men who are classified to be at intermediate and high risk are then offered prostate MRI. The combined data from RCs and MRI results can be used to select men for prostate biopsy. Low-risk men return to a risk-based safety net that includes individualised PSA-interval tests and, if necessary, repeated MRI. Depending on local availability, the use of the different risk stratification tools may be adapted.

CONCLUSIONS

We present a risk-stratified algorithm for an organised population-based early detection programme for clinically significant PCa. Although the proposed strategy has not yet been analysed prospectively, it exploits and may even improve the most important available benefits of "PSA-only" screening studies, while at the same time reduces unnecessary biopsies and overdiagnosis by using new risk stratification tools.

PATIENT SUMMARY

This paper presents a personalised strategy that enables selective early detection of prostate cancer by combining prostate-specific antigen (interval) testing' prediction models (risk calculators), and magnetic resonance imaging scans. This will likely lead to reduced prostate cancer-related morbidity and mortality, while reducing the need for prostate biopsy and limiting overdiagnosis.

Definition of Outcome-Based Prostate-Specific Antigen (PSA) Thresholds for Advanced Prostate Cancer Risk Prediction

Simple Summary

In this study, we used a well calibrated risk prediction model to define prostate-specific antigen (PSA) thresholds for identifying or excluding advanced prostate cancer (PCa) as an aid to personalize management of the diagnostic workup. PSA concentrations ≤ 4.1 (<65 years old) and ≤3.7 μg/L (≥65 years old) excluded an advanced PCa in patients without glandular inflammation, while PSA > 5.7 (<65) and >6.1 μg/L (≥65) suggested a biopsy referral. In the presence of glandular inflammation, PSA does not provide a valid estimate for risk of advanced cancer since the marker variability is higher and the pre-test probability of PCa is low in this group. The proposed PSA thresholds may allow an individualized approach to the diagnostic workup, assisting patients in making an informed decision. However, patients with asymptomatic prostatitis cannot benefit from the use of this model since they cannot be pre-biopsy identified.

Abstract

We defined prostate-specific antigen (PSA) thresholds from a well calibrated risk prediction model for identifying and excluding advanced prostate cancer (PCa). We retrieved 902 biopsied patients with a pre-biopsy PSA determination (Roche assay). A logistic regression model predictive for PCa including the main effects [i.e., PSA, age, histological evidence of glandular inflammation (GI)] was built after testing the accuracy by calibration plots and Hosmer-Lemeshow test for goodness of fit. PSA thresholds were derived by assuming a diagnostic sensitivity of 95% (rule-out) and 80% (rule-in) for overall and advanced/poorly differentiated PCa. In patients without GI, serum PSA concentrations ≤ 4.1 (<65 years old) and ≤3.7 μg/L (≥65 years old) excluded an advanced PCa (defined as Gleason score ≥ 7 at biopsy), with a negative predictive value of 95.1% [95% confidence interval (CI): 83.0–98.7] and 88.8% (CI: 80.2–93.9), respectively, while PSA > 5.7 (<65) and >6.1 μg/L (≥65) should address biopsy referral. In presence of GI, PSA did not provide a valid estimate for risk of advanced cancer because of its higher variability and the low pre-test probability of PCa. The proposed PSA thresholds may support biopsy decision except for patients with asymptomatic prostatitis who cannot be pre-biopsy identified.

External validation of two MRI-based risk calculators in prostate cancer diagnosis

BACKGROUND

The diagnosis of (significant) prostate cancer ((s)PC) is impeded by overdiagnosis and unnecessary biopsy. Risk calculators (RC) have been developed to mitigate these issues. Contemporary RCs integrate clinical characteristics with mpMRI findings.

OBJECTIVE

To validate two of these models-the MRI-ERSPC-RC-3/4 and the risk model of van Leeuwen.

METHODS

265 men with clinical suspicion of PC were enrolled. Every patient received a prebiopsy mpMRI, which was reported according to PI-RADS v2.1, followed by MRI/TRUS fusion-biopsy. Cancers with ISUP grade ≥ 2 were classified as sPC.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Statistical analysis was performed by comparing discrimination, calibration, and clinical utility RESULTS: There was no significant difference in discrimination between the RCs. The MRI-ERSPC-RC-3/4-RC showed a nearly ideal calibration-slope (0.94; 95% CI 0.68-1.20) than the van Leeuwen model (0.70; 95% CI 0.52-0.88). Within a threshold range up to 9% for a sPC, the MRI-ERSPC-RC-3/4-RC shows a greater net benefit than the van Leeuwen model. From 10 to 15%, the van Leeuwen model showed a higher net benefit compared to the MRI-ERSP-3/4-RC. For a risk threshold of 15%, the van Leeuwen model would avoid 24% vs. 14% compared to the MRI-ERSPC-RC-3/4 model; 6% vs. 5% sPC would be overlooked, respectively.

CONCLUSION

Both risk models supply accurate results and reduce the number of biopsies and basically no sPC were overlooked. The van Leeuwen model suggests a better balance between unnecessary biopsies and overlooked sPC at thresholds range of 10-15%. The MRI-ERSPC-RC-3/4 risk model provides better overall calibration.

Rotterdam mobile phone app including MRI data for the prediction of prostate cancer: A multicenter external validation

OBJECTIVES

The Rotterdam Prostate Cancer Risk calculator (RPCRC) has been validated in the past years. Recently a new version including multiparametric magnetic resonance imaging (mpMRI) data has been released. The aim of our study was to analyze the performance of the mpMRI RPCRC app.

METHODS

A series of men undergoing prostate biopsies were enrolled in eleven Italian centers. Indications for prostate biopsy included: abnormal Prostate specific antigen levels (PSA>4 ng/ml), abnormal DRE and abnormal mpMRI. Patients' characteristics were recorded. Prostate cancer (PCa) risk and high-grade PCa risk were assessed using the RPCRC app. The performance of the mpMRI RPCRC in the prediction of cancer and high-grade PCa was evaluated using receiver operator characteristics, calibration plots and decision curve analysis.

RESULTS

Overall, 580 patients were enrolled: 404/580 (70%) presented PCa and out of them 224/404 (55%) presented high-grade PCa. In the prediction of cancer, the RC presented good discrimination (AUC = 0.74), poor calibration (p = 0.01) and a clinical net benefit in the range of probabilities between 50 and 90% for the prediction of PCa (Fig. 1). In the prediction of high-grade PCa, the RC presented good discrimination (AUC = 0.79), good calibration (p = 0.48) and a clinical net benefit in the range of probabilities between 20 and 80% (Fig. 1).

CONCLUSIONS

The Rotterdam prostate cancer risk App accurately predicts the risk of PCa and particularly high-grade cancer. The clinical net benefit is wide for high-grade cancer and therefore its implementation in clinical practice should be encouraged. Further studies should assess its definitive role in clinical practice.

Predicting the risk of prostate cancer in asymptomatic men: a cohort study to develop and validate a novel algorithm

Background

Diagnosis of prostate cancer at an early stage can potentially identify tumours when intervention may improve treatment options and survival.

Aim

To develop and validate an equation to predict absolute risk of prostate cancer in asymptomatic men with prostate specific antigen (PSA) tests in primary care.

Design and setting

Cohort study using data from English general practices, held in the QResearch database.

Method

Routine data were collected from 1098 QResearch English general practices linked to mortality, hospital, and cancer records for model development. Two separate sets of practices were used for validation. In total, there were 844 455 men aged 25–84 years with PSA tests recorded who were free of prostate cancer at baseline in the derivation cohort; the validation cohorts comprised 292 084 and 316 583 men. The primary outcome was incident prostate cancer. Cox proportional hazards models were used to derive 10-year risk equations. Measures of performance were determined in both validation cohorts.

Results

There were 40 821 incident cases of prostate cancer in the derivation cohort. The risk equation included PSA level, age, deprivation, ethnicity, smoking status, serious mental illness, diabetes, BMI, and family history of prostate cancer. The risk equation explained 70.4% (95% CI = 69.2 to 71.6) of the variation in time to diagnosis of prostate cancer (R²) (D statistic 3.15, 95% CI = 3.06 to 3.25; Harrell’s C-index 0.917, 95% CI = 0.915 to 0.919). Two-step approach had higher sensitivity than a fixed PSA threshold at identifying prostate cancer cases (identifying 68.2% versus 43.9% of cases), high-grade cancers (49.2% versus 40.3%), and deaths (67.0% versus 31.5%).

Conclusion

The risk equation provided valid measures of absolute risk and had higher sensitivity for incident prostate cancer, high-grade cancers, and prostate cancer mortality than a simple approach based on age and PSA threshold.

Biomarkers for prostate cancer: prostate-specific antigen and beyond

In recent years, several new biomarkers supplementing the role of prostate-specific antigen (PSA) have become available for men with prostate cancer. Although widely used in an ad hoc manner, the role of PSA in screening asymptomatic men for prostate cancer is controversial. Several expert panels, however, have recently recommended limited PSA screening following informed consent in average-risk men, aged 55-69 years. As a screening test for prostate cancer however, PSA has limited specificity and leads to overdiagnosis which in turn results in overtreatment. To increase specificity and reduce the number of unnecessary biopsies, biomarkers such as percent free PSA, prostate health index (PHI) or the 4K score may be used, while Progensa PCA3 may be measured to reduce the number of repeat biopsies in men with a previously negative biopsy. In addition to its role in screening, PSA is also widely used in the management of patients with diagnosed prostate cancer such as in surveillance following diagnosis, monitoring response to therapy and in combination with both clinical and histological criteria in risk stratification for recurrence. For determining aggressiveness and predicting outcome, especially in low- or intermediate-risk men, tissue-based multigene tests such as Decipher, Oncotype DX (Prostate), Prolaris and ProMark, may be used. Emerging therapy predictive biomarkers include AR-V7 for predicting lack of response to specific anti-androgens (enzalutamide, abiraterone), BRAC1/2 mutations for predicting benefit from PARP inhibitor and PORTOS for predicting benefit from radiotherapy. With the increased availability of multiple biomarkers, personalised treatment for men with prostate cancer is finally on the horizon.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Tumor Microenvironment Analysis Identified Subtypes Associated With the Prognosis and the Tumor Response to Immunotherapy in Bladder Cancer

Background: The efficiency of immune checkpoint inhibitors (ICIs) in bladder cancer (BLCA) treatment has been widely validated; however, the tumor response to ICIs was generally low. It is critical and urgent to find biomarkers that can predict tumor response to ICIs. The tumor microenvironment (TME), which may play important roles to either dampen or enhance immune responses, has been widely concerned. Methods: The cancer genome atlas BLCA (TCGA-BLCA) cohort (n = 400) was used in this study. Based on the proportions of 22 types of immune cells calculated by CIBERSORT, TME was classified by K-means Clustering and differentially expressed genes (DEGs) were determined. Based on DEGs, patients were classified into three groups, and cluster signature genes were identified after reducing redundant genes. Then TMEscore was calculated based on cluster signature genes, and the samples were classified to two subtypes. We performed somatic mutation and copy number variation analysis to identify the genetic characteristics of the two subtypes. Correlation analysis was performed to explore the correlation between TMEscore and the tumor response to ICIs as well as the prognosis of BLCA. Results: According to the proportions of immune cells, two TME clusters were determined, and 1,144 DEGs and 138 cluster signature genes were identified. Based on cluster signature genes, samples were classified into TMEscore-high (n = 199) and TMEscore-low (n = 201) subtypes. Survival analysis showed patients with TMEscore-high phenotype had better prognosis. Among the 45 differentially expressed micro-RNAs (miRNAs) and 1,033 differentially expressed messenger RNAs (mRNAs) between the two subtypes, 16 miRNAs and 287 mRNAs had statistically significant impact on the prognosis of BLCA. Furthermore, there were 94 genes with significant differences between the two subtypes, and they were enriched in RTK-RAS, NOTCH, WNT, Hippo, and PI3K pathways. The Tumor Immune Dysfunction and Exclusion (TIDE) score of TMEscore-high BLCA was statistically lower than that of TMEscore-low BLCA. Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of TMEscore and tumor mutation burden (TMB) is 0.6918 and 0.5374, respectively. Conclusion: We developed a method to classify BLCA patients to two TME subtypes, TMEscore-high and TMEscore-low, and we found TMEscore-high subtype of BLCA had a good prognosis and a good response to ICIs.

Retrospective cohort study evaluating clinical, biochemical and pharmacological prognostic factors for prostate cancer progression using primary care data

OBJECTIVES

To confirm the association of previously reported prognostic factors with future progression of localised prostate cancer using primary care data and identify new potential prognostic factors for further assessment in prognostic model development and validation.

DESIGN

Retrospective cohort study, employing Cox proportional hazards regression controlling for age, prostate specific antigen (PSA), and Gleason score, was stratified by diagnostic stage.

SETTING

Primary care in England.

PARTICIPANTS

Males with localised prostate cancer diagnosedbetween 01/01/1987 and 31/12/2016 within the Clinical Practice ResearchDatalink database, with linked data from the National Cancer Registration andAnalysis Service and Office for National Statistics.

PRIMARY AND SECONDARY OUTCOMES

Primary outcome measure was prostate cancer mortality. Secondary outcome measures were all-cause mortality and commencing systemic therapy. Up-staging after diagnosis was not used as a secondary outcome owing to significant missing data.

RESULTS

10 901 men (mean age 74.38±9.03 years) with localised prostate cancer were followed up for a mean of 14.12 (±6.36) years. 2331 (21.38%) men underwent systemic therapy and 3450 (31.65%) died, including 1250 (11.47%) from prostate cancer. Factors associated with an increased risk of prostate cancer mortality included age; high PSA; current or ex-smoker; ischaemic heart disease; high C reactive protein; high ferritin; low haemoglobin; high blood glucose and low albumin.

CONCLUSIONS

This study identified several new potential prognostic factors for prostate cancer progression, as well as confirming some known prognostic factors, in an independent primary care data set. Further research is needed to develop and validate a prognostic model for prostate cancer progression.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Does Multiparametric Magnetic Resonance of Prostate Outperform Risk Calculators in Predicting Prostate Cancer in Biopsy Naïve Patients?

Background

European Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa).

Objectives

To compare the performance of mpMRI, a RC we recently developed and two commonly used RC not including mpMRI in predicting the risk of PCa, as well as the added value of mpMRI to each RC.

Design, Setting, and Participants

Single-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI.

Outcome Measurements and Statistical Analysis

Patients’ probabilities of any PCa and clinically significant PCa (csPC, defined as Gleason-Score ≥3 + 4) were computed according to mpMRI, European Randomized Study of Screening for Prostate Cancer RC (ERSPC-RC), the Prostate Biopsy Collaborative Group RC (PBCG-RC) and the Foggia Prostate Cancer RC (FPC-RC). Logistic regression, AUC, and Decision curve analysis (DCA) were used to assess the accuracy of tested models.

Results and Limitation

The FPC-RC outperformed mpMRI in diagnosing both any PCa (AUC 0.76 vs 0.69) and csPCa (AUC 0.80 vs 0.75). Conversely mpMRI showed a higher accuracy in predicting any PCa compared to the PBCG-RC and the ERSPC-RC but similar performances in predicting csPCa. At multivariable analysis predicting csPCa and any PCa, the addition of mpMRI findings improved the accuracy of each calculator. DCA showed that the FPC-RC provided a greater net benefit than mpMRI and the other RCs. The addition of mpMRI findings improved the net benefit provided by each calculator.

Conclusions

mpMRI was outperformed by the novel FPC-RC and showed similar performances compared to the PBCG and ERSPC RCs in predicting csPCa. The addition of mpMRI findings improved the diagnostic accuracy of each of these calculators

Clinical utility of current biomarkers for prostate cancer detection

Although prostate-specific antigen (PSA) remains the most used test to detect prostate cancer (PCa), the limited specificity and an elevated rate of overdiagnosis are the main problems associated with PSA testing. Over the last three decades, a large body of evidence has indicated that PSA screening methods for PCa are problematic, although PSA screening significantly reduces PCa-specific mortality. A number of novel biomarkers have been introduced to overcome these limitations of PSA in the clinical setting. These biomarkers have demonstrated an increased ability to select patients for biopsy and identify men at risk for clinically significant PCa. Although a number of assays require further validation, initial data are promising. Forthcoming results will ultimately determine the clinical utility and commercial availability of these assays. Extensive efforts have recently been made to identify and commercialize novel PCa biomarkers for more effective detection of PCa, either alone or in combination with currently available clinical tools. This review highlights the role of existing and promising serum and urinary biomarkers for the detection and prognostication of PCa before prostate biopsy.