A 4K score/MRI-based nomogram for predicting prostate cancer, clinically significant prostate cancer, and unfavorable prostate cancer

An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer

PURPOSE

Conventional prostate magnetic resonance imaging has limited accuracy for clinically significant prostate cancer (csPCa). We performed diffusion basis spectrum imaging (DBSI) before biopsy and applied artificial intelligence models to these DBSI metrics to predict csPCa.

MATERIALS AND METHODS

Between February 2020 and March 2024, 241 patients underwent prostate MRI that included conventional and DBSI-specific sequences before prostate biopsy. We used artificial intelligence models with DBSI metrics as input classifiers and the biopsy pathology as the ground truth. The DBSI-based model was compared with available biomarkers (PSA, PSA density [PSAD], and Prostate Imaging Reporting and Data System [PI-RADS]) for risk discrimination of csPCa defined as Gleason score > 7.

RESULTS

The DBSI-based model was an independent predictor of csPCa (odds ratio [OR] 2.04, 95% CI 1.52-2.73, P < .01), as were PSAD (OR 2.02, 95% CI 1.21-3.35, P = .01) and PI-RADS classification (OR 4.00, 95% CI 1.37-11.6 for PI-RADS 3, P = .01; OR 9.67, 95% CI 2.89-32.7 for PI-RADS 4-5, P < .01), adjusting for age, family history, and race. Within our dataset, the DBSI-based model alone performed similarly to PSAD + PI-RADS (AUC 0.863 vs 0.859, P = .89), while the combination of the DBSI-based model + PI-RADS had the highest risk discrimination for csPCa (AUC 0.894, P < .01). A clinical strategy using the DBSI-based model for patients with PI-RADS 1-3 could have reduced biopsies by 27% while missing 2% of csPCa (compared with biopsy for all).

CONCLUSIONS

Our DBSI-based artificial intelligence model accurately predicted csPCa on biopsy and can be combined with PI-RADS to potentially reduce unnecessary prostate biopsies.

Personalised Prostate Cancer Diagnosis: Evaluating Biomarker-based Approaches to Reduce Unnecessary Magnetic Resonance Imaging and Biopsy Procedures

Background and objective

Efforts made over the last decade for the detection of prostate cancer (PCa) have revolutionised disease diagnostics, and implementation of prebiopsy magnetic resonance imaging (MRI) has received widespread acceptance. However, universal adoption of prebiopsy MRI and the benefits achieved have been limited by availability and equivocal MRI findings. This review aims to evaluate the latest evidence on the role of existing PCa risk calculators (RCs), and blood and urinary biomarkers as part of the diagnostic algorithm to improve the diagnosis of clinically significant PCa (csPCa) and reduce unnecessary MRI procedures and biopsies. We will also evaluate the potential of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) to enhance sensitivity and specificity for PCa diagnosis, complement MRI, and refine biopsy strategies within the diagnostic pathway.

Methods

We performed a narrative review using the PubMed/MEDLINE database, which included papers published between January 2014 and June 2024. The outcome measures included rates of reduced diagnoses of nonsignificant PCa (defined as International Society of Urological Pathology [ISUP] grade group 1) cases, diagnoses of csPCa (defined as ISUP grade group ≥2) cases missed, and MRI scans and prostate biopsies avoided.

Key findings and limitations

In men with abnormal prostate-specific antigen (PSA) levels, further risk stratification using RCs, or blood or urine biomarkers can reduce up to 16-51% MRI scans, while missing 1-16% csPCa cases. In case of equivocal MRI results or Prostate Imaging Reporting and Data System 3 lesions, RCs or biomarkers could reduce up to 72% of biopsies, while missing only 3-13% csPCa cases. PSMA PET has emerging potential to improve csPCa prediction in combination with MRI and may further reduce unnecessary biopsies. A limitation of this study is that this is a narrative but not a systematic review.

Conclusions and clinical implications

RCs and biomarkers have been demonstrated to enhance the performance and efficiency of MRI in detecting csPCa in men with elevated PSA levels. PSMA PET shows promise in detecting csPCa, complementing MRI and refining biopsy indications.

Patient summary

In men with a suspicion of prostate cancer, magnetic resonance imaging prostate scans are effective in predicting clinically relevant cancer, but challenges including availability and equivocal scans exist. A personalised approach by adding one or more of clinical risk calculators, blood or urine biomarkers, or even novel imaging techniques such as positron emission tomography scans may improve cancer prediction further and reduce unnecessary scans and biopsies.

A Simple Nomogram to Predict Clinically Significant Prostate Cancer at MRI-Guided Biopsy in Patients with Mild PSA Elevation and Normal DRE

BACKGROUND

Evidence to help avoid unnecessary prostate biopsies is being actively pursued. Our goal was to develop and internally validate a nomogram for predicting clinically significant prostate cancer (csPC) in men with low suspicion of disease (prostate specific antigen [PSA] < 10 ng/mL, normal digital rectal examination [DRE]), in whom magnetic resonance imaging (MRI) findings are positive.

METHODS

Patients with no prior prostate cancer diagnosis who underwent MRI-ultrasound fusion biopsy of the prostate were retrospectively analyzed. Inclusion criteria were PSA < 10 ng/mL, normal DRE, Prostate Imaging Reporting And Data System (PIRADS) category ≥ 3, and no extraprostatic extension or seminal vesicle invasion reported on MRI. Associations between csPC diagnosis and patient or lesion characteristics were analyzed, and a multivariable model was developed. Internal validation of the model with 5-fold cross-validation and bootstrapping methods was performed.

RESULTS

Among 209 patients, 67 were diagnosed with csPC. Factors incorporated into the model for predicting csPC were age, 5-alpha reductase inhibitor use, PSA, prostate volume, PIRADS > 3, and lesion location in the peripheral zone. The model's ROC AUC was 0.86, with consistent performance at internal validation (0.84 with cross-validation, 0.82 with bootstrapping). With an empirical threshold of <10% csPC probability to omit biopsy, 72 (50.7%) unnecessary biopsies would have been avoided, at the cost of missing 2 (3.0%) csPC cases.

CONCLUSIONS

Our nomogram might serve as a valuable tool in refining selection criteria in men considered for prostate biopsy. The major limitation of the study is its retrospective character. Prospective, external validation of the model is warranted.

Prospective validation study of a combined urine and plasma test for predicting high-grade prostate cancer in biopsy naïve men

OBJECTIVE

Early and accurate diagnosis of prostate cancer (PC) is crucial for effective treatment. Diagnosing  clinically insignificant cancers can lead to overdiagnosis and overtreatment, highlighting the importance of accurately selecting patients for further evaluation based on improved risk prediction tools. Novel biomarkers offer promise for enhancing this diagnostic process. In this study, we aimed to externally validate a previously developed urine and plasma biomarker test in a biopsy-naïve population.

MATERIALS AND METHODS

Urine and blood samples were prospectively collected from 362 biopsy-naïve men with suspected PC before they underwent transrectal prostate biopsies. The expression levels of a 10-gene mRNA panel were quantified using reverse transcription/quantitative polymerase chain reaction of both urine and plasma. These gene expression levels, combined with clinical features and plasma prostate-specific antigen (PSA) levels, were used to predict the presence of International Society of Urological Pathology grade group ≥ 2 PC.

RESULTS

Complete data were available for 314 patients. The sensitivity and specificity of the biomarker test were 87% (95% CI: 79-93%) and 42% (95% CI: 36-49%), respectively. The area under the curve was 0.76 (95% CI: 0.7-0.82) for the biomarker test probability and 0.65 (95% CI: 0.59-0.72) for PSA (p = 0.02). The test's negative predictive value was 89% (CI: 81-94%).

CONCLUSION

This study did not replicate the previously reported high accuracy of the biomarker test, highlighting the need for further refinement and robust external validation to ensure reliable performance across diverse patient populations.

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.

Analysis of the Performance and Accuracy of a PSA and PSA Ratio-Based Nomogram to Predict the Probability of Prostate Cancer in a Cohort of Patients with PIRADS 3 Findings at Multiparametric Magnetic Resonance Imaging

BACKGROUND

PIRADS score 3 represents a challenge in prostate cancer (PCa) detection with MRI. Our study aimed to evaluate the application of a nomogram on a cohort of patients with PIRADS 3.

METHODS

We analyzed 286 patients undergoing fusion prostate biopsy from January 2020 to February 2024. Only PIRADS 3 patients were included. Two nomograms, previously developed and based on clinical variables such as age, total PSA (specifically 2-10 ng/mL) and PSA ratio were applied to estimate the probability (Nomograms A and B) for PCa Grade Group (GG) > 3 and GG < 3.

RESULTS

Out of the 70 patients available for analysis, 14/70 patients (20%) had PCa, 4/14 were GG 1 (28.6%), 1/14 were GG 2 (7.1%), 5/14 were GG 3 (35.8%), 2/14 were GG 4 (14.3%) and 2/14 were GG 5 (14.3%). The median probability of PCa GG > 3 and GG < 3 was 5% and 33%, respectively. A significant difference (p = 0.033) was found between patients with negative versus positive biopsy for Nomogram B. There was a significant difference (p = 0.029) for Nomogram B comparing patients with GG < 3 and GG > 3. Using a cut-off of 40% for Nomogram B, sensitivity and specificity were 70% and 80%, respectively.

CONCLUSIONS

This cohort has a low probability of harboring PCa especially ISUP > 3. Nomogram B has good accuracy for discriminating patients with PCa from those with negative biopsy.

Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform

The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients' risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary.

Variability of mpMRI diagnostic performance according to the upfront individual patient risk of having clinically significant prostate cancer

BACKGROUND

To assess the variation of multiparametric magnetic resonance imaging (mpMRI) positive predictive value (PPV) according to each patient's risk of clinically significant prostate cancer (csPCa) based exclusively on clinical factors.

METHODS

We evaluated 999 patients with positive mpMRI (PI-RADS ≥ 3) receiving targeted (TBx) plus systematic prostate biopsy. We built a multivariable logistic regression analysis (MVA) using clinical risk factors to calculate the individual patients' risk of harboring csPCa at TBx. A second MVA tested the association between individual patients' clinical risk and mpMRI PPV accounting for the PI-RADS score. Finally, we plotted the PPV of each PI-RADS score by the individual patient pretest probability of csPCa using a LOWESS approach.

RESULTS

Overall, TBx found csPCa in 21%, 51%, and 80% of patients with PI-RADS 3, 4, and 5 lesions, respectively. At MVA, age, PSA, digital rectal examination (DRE), and prostate volume were significantly associated with the risk of csPCa at biopsy. DRE yielded the highest odds ratio (OR: 2.88; p < 0.001). The individual patient's clinical risk was significantly associated with mpMRI PPV (OR: 2.49; p < 0.001) using MVA. Plotting the mpMRI PPV according to the predicted clinical risks, we observed that for patients with clinical risk close to 0 versus patients with risk higher than 90%, the mpMRI PPV of PI-RADS 3, 4, and 5 ranged from 0% to 75%, from 0% to 96%, and from 45% to 100%, respectively.

CONCLUSION

mpMRI PPV varies according to the individual pretest patient's risk based on clinical factors. These findings should be considered in the decision-making process for patients with suspect MRI findings referred for a prostate biopsy. Moreover, our data support the need for further studies to create an individualized risk prediction tool.

How to Integrate Prostate Cancer Biomarkers in Urology Clinical Practice: An Update

Nowadays, the management of prostate cancer has become more and more challenging due to the increasing number of available treatment options, therapeutic agents, and our understanding of its carcinogenesis and disease progression. Moreover, currently available risk stratification systems used to facilitate clinical decision-making have limitations, particularly in providing a personalized and patient-centered management strategy. Although prognosis and prostate cancer-specific survival have improved in recent years, the heterogenous behavior of the disease among patients included in the same risk prognostic group negatively impacts not only our clinical decision-making but also oncological outcomes, irrespective of the treatment strategy. Several biomarkers, along with available tests, have been developed to help clinicians in difficult decision-making scenarios and guide management strategies. In this review article, we focus on the scientific evidence that supports the clinical use of several biomarkers considered by professional urological societies (and included in uro-oncological guidelines) in the diagnosis process and specific difficult management strategies for clinically localized or advanced prostate cancer.

Advancements in Biomarkers of Prostate Cancer: A Review

Prostate cancer (PCa) is one of the most prevalent and deadly cancers among men, particularly affecting men of African descent and contributing significantly to cancer-related morbidity and mortality worldwide. The disease varies widely, from slow-developing forms to highly aggressive or potentially fatal variants. Accurate risk stratification is crucial for making therapeutic decisions and designing adequate clinical trials. This review assesses a broad spectrum of diagnostic and prognostic biomarkers, many of which are incorporated into clinical guidelines, including the Prostate Health Index (PHI), 4Kscore, STHLM3, PCA3, SelectMDx, ExoDx Prostate Intelliscore (EPI), and MiPS. It also highlights emerging biomarkers with preclinical support, such as urinary non-coding RNAs and DNA methylation patterns. Additionally, the review explores the role of tumor-associated microbiota in PCa, offering new insights into its potential contributions to disease understanding. By examining the latest advancements in PCa biomarkers, this review enhances understanding their roles in disease management.

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.

A SelectMDx/magnetic resonance imaging-based nomogram to diagnose prostate cancer

BACKGROUND

70%-80% of prostate cancer (PCa) biopsies performed in the US annually may be unnecessary. Specific antigen testing (PSA) and tans rectal ultrasound (TRUS) are imprecise predictive methods for risk of PCa. Novel strategies are critical to guide biopsy decision-making.

AIM

We assessed the utility and accuracy of combining Select MDx and multiparametric magnetic resonance imaging (mpMRI) scores for predicting risk of PCa.

METHODS AND RESULTS

Our study was conducted at Mount Sinai hospital at Urology department in New York City from January 2020 to April 2021. Total 129 men performed select MDx test. Indications for prostate biopsy were high-risk Select MDx score, suspicious DRE, PI-RADS scores 3/4/5 on mpMRI, or any combination of these. Fifty-one percentage of 129 patients underwent systemic or combined systemic and MRI/US (ultrasound) fusion biopsy; All men underwent 3 T MRI of Prostate w/wo contrast using standard protocols prior to biopsy. A single surgeon performed prostate biopsies. Gleason score ≥3 + 3 on biopsy is defined as outcome. Descriptive statistics were calculated as cross tables. Binary logistic regression model is used to determine the outcome. The nomogram was based on the coefficients of the logit function. ROCs were plotted and decision curve analysis was performed. Using both high-risk Select MDx and PI-RADS scores of 4/5, 87% of biopsies could have been avoided, while detecting 64% of PCa and missing 36%. If biopsies were performed on men with positive Select MDx or PI-RADS 4/5 results, 16% of biopsies could have been avoided while detecting all PCa. Combining these scores improved specificity and accuracy for the detection of PCa over either used alone. Study limitations include limited sample size, sole institution study, and risk or overfitting for the proposed model which may limit generalizability.

CONCLUSION

Combining SelectMDx and mpMRI PI-PADS scores of 4/5 may be useful for PCa biopsy decision-making.

Comparative Analysis of PSA Density and an MRI-Based Predictive Model to Improve the Selection of Candidates for Prostate Biopsy

This study is a head-to-head comparison between mPSAD and MRI-PMbdex. The MRI-PMbdex was created from 2432 men with suspected PCa; this cohort comprised the development and external validation cohorts of the Barcelona MRI predictive model. Pre-biopsy 3-Tesla multiparametric MRI (mpMRI) and 2 to 4-core transrectal ultrasound (TRUS)-guided biopsies for suspicious lesions and/or 12-core TRUS systematic biopsies were scheduled. Clinically significant PCa (csPCa), defined as Gleason-based Grade Group 2 or higher, was detected in 934 men (38.4%). The area under the curve was 0.893 (95% confidence interval [CI]: 0.880−0.906) for MRI-PMbdex and 0.764 (95% CI: 0.774−0.783) for mPSAD, with p < 0.001. MRI-PMbdex showed net benefit over biopsy in all men when the probability of csPCa was greater than 2%, while mPSAD did the same when the probability of csPCa was greater than 18%. Thresholds of 13.5% for MRI-PMbdex and 0.628 ng/mL2 for mPSAD had 95% sensitivity for csPCa and presented 51.1% specificity for MRI-PMbdex and 19.6% specificity for mPSAD, with p < 0.001. MRI-PMbdex exhibited net benefit over mPSAD in men with prostate imaging report and data system (PI-RADS) <4, while neither exhibited any benefit in men with PI-RADS 5. Hence, we can conclude that MRI-PMbdex is more accurate than mPSAD for the proper selection of candidates for prostate biopsy among men with suspected PCa, with the exception of men with a PI-RAD S 5 score, for whom neither tool exhibited clinical guidance to determine the need for biopsy.

Proclarix, A New Biomarker for the Diagnosis of Clinically Significant Prostate Cancer: A Systematic Review

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI) has improved the early detection of clinically significant prostate cancer (csPCa). However, an appropriate selection of men for mpMRI or prostate biopsy is still challenging, which is why new biomarkers or predictive models are recommended to determine those patients who will benefit from prostate biopsy. Proclarix is a new test that provides the risk of csPCa based on thrombospondin-1 (THBS1), cathepsin D (CTSD), prostate-specific antigen (PSA), and percentage of free PSA (%fPSA), as well as age. This systematic review analyzes the current clinical status of Proclarix and future development.

EVIDENCE ACQUISITION

A systematic review of the literature was carried out by two independent reviewers. The Medical Subject Heading (MeSH) terms 'prostate', 'thrombospondin-1', 'cathepsin-D' and 'Proclarix' were used. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison and Outcomes (PICO) selection criteria were followed. Finally, four articles analyzed the clinical usefulness of Proclarix.

EVIDENCE SYNTHESIS

Proclarix has been developed in men with PSA levels between 2 and 10 ng/mL, normal digital rectal examination (DRE), and prostate volume (PV)​ ≥ 35 cm³. Proclarix is associated with the PCa grade group and is more effective than %fPSA in detecting csPCa. Two studies analyzed the efficacy of Proclarix in men undergoing guided and systematic biopsies, obtaining similar results to PSA density.

CONCLUSION

Initial studies have shown the potential benefit of Proclarix in patients with specific characteristics. Future studies are needed to verify the clinical usefulness of Proclarix in men with suspected PCa before and after mpMRI.

Clinical Utility of Negative Multiparametric Magnetic Resonance Imaging in the Diagnosis of Prostate Cancer and Clinically Significant Prostate Cancer

Background

Multiparametric magnetic resonance imaging (MRI) is increasingly used to diagnose prostate cancer (PCa). It is not yet established whether all men with negative MRI (Prostate Imaging-Reporting and Data System version 2 score <3) should undergo prostate biopsy or not.

Objective

To develop and validate a prediction model that uses clinical parameters to reduce unnecessary prostate biopsies by predicting PCa and clinically significant PCa (csPCa) for men with negative MRI findings who are at risk of harboring PCa.

Design setting and participants

This was a retrospective analysis of 200 men with negative MRI at risk of PCa who underwent prostate biopsy (2014-2020) with prostate-specific antigen (PSA) >4 ng/ml, 4Kscore of >7%, PSA density ≥0.15 ng/ml/cm³, and/or suspicious digital rectal examination. The validation cohort included 182 men from another centre (University of Miami) with negative MRI who underwent systematic prostate biopsy with the same criteria.

Outcome measurements and statistical analysis

csPCa was defined as Gleason grade group ≥2 on biopsy. Multivariable logistic regression analysis was performed using coefficients of logit function for predicting PCa and csPCa. Nomogram validation was performed by calculating the area under receiver operating characteristic curves (AUC) and comparing nomogram-predicted probabilities with actual rates of PCa and csPCa.

Results and limitations

Of 200 men in the development cohort, 18% showed PCa and 8% showed csPCa on biopsy. Of 182 men in the validation cohort, 21% showed PCa and 6% showed csPCa on biopsy. PSA density, 4Kscore, and family history of PCa were significant predictors for PCa and csPCa. The AUC was 0.80 and 0.87 for prediction of PCa and csPCa, respectively. There was agreement between predicted and actual rates of PCa in the validation cohort. Using the prediction model at threshold of 40, 47% of benign biopsies and 15% of indolent PCa cases diagnosed could be avoided, while missing 10% of csPCa cases. The small sample size and number of events are limitations of the study.

Conclusions

Our prediction model can reduce the number of prostate biopsies among men with negative MRI without compromising the detection of csPCa.

Patient summary

We developed a tool for selection of men with negative MRI (magnetic resonance imaging) findings for prostate cancer who should undergo prostate biopsy. This risk prediction tool safely reduces the number of men who need to undergo the procedure.

A 4K score/MRI-based nomogram for predicting prostate cancer, clinically significant prostate cancer, and unfavorable prostate cancer

Background

The detection of prostate cancer requires histological confirmation in biopsy core. Currently, number of unnecessary prostate biopsies are being performed in the United States. This is due to the absence of appropriate biopsy decision‐making protocol.

Aim

To develop and validate a 4K score/multiparametric magnetic resonance imaging (mpMRI)‐based nomogram to predict prostate cancer (PCa), clinically significant prostate cancer (csPCa), and unfavorable prostate cancer (uPCa).

Methods and Results

Retrospective, single‐center study evaluating a cohort of 574 men with 4K score test >7% or suspicious digital rectal examination (DRE) or Prostate Imaging Reporting and Data System (PI‐RADS) scores 3, 4, or 5 on mpMRI that underwent systematic and/or mpMRI/ultrasound fusion–targeted prostate biopsy between 2016 and 2020. External cohort included 622 men. csPCa and uPCa were defined as Gleason score ≥3 + 4 and ≥4 + 3 on biopsy, respectively. Multivariable logistic regression analysis was performed to build nomogram for predicting PCa, csPCa, and uPCa. Validation was performed by plotting the area under the curve (AUC) and comparing nomogram‐predicted probabilities with actual rates of PCa, csPCa, and uPCa probabilities in the external cohort.

4K score, a PI‐RADS ≥4, prostate volume and prior negative biopsy were significant predictors of PCa, csPCa, and uPCa. AUCs were 0.84, 0.88, and 0.86 for the prediction of PCa, csPCa, and uPCa, respectively. The predicted and actual rates of PCa, csPCa, and uPCa showed agreement across all percentage probability ranges in the validation cohort. Using the prediction model at threshold of 30, 30% of overall biopsies, 41% of benign biopsies, and 19% of diagnosed indolent PCa could be avoided, while missing 9% of csPCa.

Conclusion

This novel nomogram would reduce unnecessary prostate biopsies and decrease detection of clinically insignificant PCa.