Added Value of Multiparametric Magnetic Resonance Imaging to Clinical Nomograms for Predicting Adverse Pathology in Prostate Cancer

Predictive Value of Multiparametric Magnetic Resonance Imaging in Risk Group Stratification of Prostate Adenocarcinoma

Purpose

The aim of this study was to further assess the clinical utility of multiparametric magnetic resonance imaging (MP-MRI) in prostate cancer (PC) staging following 2023 clinical guideline changes, both as an independent predictor of high-stage (>T3a) or high-risk PC and when combined with patient characteristics.

Methods and Materials

The present study was a retrospective review of 171 patients from 2008 to 2018 who underwent MP-MRI before radical prostatectomy at a single institution. The accuracy of clinical staging was compared between conventional staging and MP-MRI-based clinical staging. Sensitivity, specificity, positive predictive value, and negative predictive value were compared, and receiver operating characteristic curves were generated. Linear regression analyses were used to calculate concordance (C-statistic).

Results

Of the 171 patients, final pathology revealed 95 (55.6%) with T2 disease, 62 (36.3%) with T3a disease, and 14 (8.2%) with T3b disease. Compared with conventional staging, MP-MRI-based staging demonstrated significantly increased accuracy in identifying T3a disease, intermediate risk, and high/very-high-risk PC. When combined with clinical characteristics, MP-MRI-based staging improved the area under the curve from 0.753 to 0.808 (P = .0175), compared with conventional staging.

Conclusions

MP-MRI improved the identification of T3a PC, intermediate-risk PC, and high- or very-high-risk PC. Further, when combined with clinical characteristics, MP-MRI-based staging significantly improved risk stratification, compared with conventional staging. These findings represent further evidence to support the integration of MP-MRI into prostate adenocarcinoma clinical staging guidelines.

Robot-Assisted Radical Prostatectomy in PIRADS 5 Lesions Without Prior Biopsy: Is Biopsy Really Necessary in This Cohort?

Introduction: Conventionally, confirmation of clinically significant prostate cancer (csPCa) (Gleason grade group ≥ 2) involves an initial multiparametric magnetic resonance imaging (mpMRI) followed by biopsy. Prostate biopsy incurs inherent risks of infection, bleeding, patient discomfort, and a 6-week delay before robot-assisted laparoscopic radical prostatectomy (RALP). We explored the feasibility of immediate RALP in men with PIRADS 5 lesions without preceding biopsy. Methodology: After obtaining institutional review board approval, a prospective analysis was conducted on 235 patients with PIRADS 5 lesions on mpMRI from December 2018 to February 2023. Patients were divided into 2 groups as follows: Group NoBiopsy (biopsy not done before RALP, cases, n = 118) and Group YesBiopsy (biopsy done before RALP, controls, n = 117). Baseline preoperative, intraoperative, and postoperative parameters were analyzed. Functional outcomes were monitored at 1, 3-, 6-, 9-, and 12-months follow-up post-RALP. Statistical analysis was performed using SPSS and STATA. Results: Ninety-five percent of cases and 87.17% controls had csPCa on final pathology post-RALP. Multivariable analysis did not find significant association between biopsy status and csPCa. Abnormal digital rectal examination (DRE), family history, preoperative PSA, and MRI lesion volume predicted csPCa. Significant differences were observed in console time (NoBiopsy vs. YesBiopsy, 60 ± 10 vs. 70 ± 9 minutes, p < 0.001) and estimated blood loss (80 ± 20 vs. 100 ± 30 mL, p < 0.01) between groups. At 6 months post-RALP, 96% of men in Group NoBiopsy were continent, compared with 88% of men in Group YesBiopsy (p < 0.04). All men in the study cohort were continent (0 pads) at 12 months post-RALP. Ninety-eight percent of cases and 92% of controls at 9 months and 12 months, respectively, were able to have penetrative sex with or without PDE-5 inhibitors post-RALP. Conclusion: RALP without antecedent prostate biopsy in men with PIRADS 5 lesions demonstrated substantial csPCa detection rates and superior functional outcomes, warranting further validation.

MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis

PURPOSE

We to systematically evaluate the diagnostic performance of MRI radiomics in detecting extracapsular extension (EPE) of prostate cancer (PCa).

METHODS

A literature search of online databases of PubMed, EMBASE, Cochrane Library, Web of Science, and Google Scholar online scientific publication databases was performed to identify studies published up to July 2023. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic (HSROC) model. This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, the quality of included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2) and the radiomics quality score (RQS). Meta-regression and subgroup analyses were performed to explore the impact of varying clinical settings.

RESULTS

A total of ten studies met the inclusion criteria. The pooled sensitivity and specificity were 0.77 (95% CI 0.68-0.84, I2 = 83.5%) and 0.75 (95% CI 0.67-0.82, I2 = 83.5%), respectively, with an area under the HSROC curve of 0.88 (95% CI 0.85-0.91). Study quality was not high while assessing with the RQS. Substantial heterogeneity was observed between studies; however, meta-regression analysis did not reveal any significant contributing factors.

CONCLUSIONS

MRI radiomics demonstrated moderate sensitivity and specificity, offering similar diagnostic performance with previous risk stratifications and models that primarily based on radiologists' subjective experience. However, all studies included were retrospective, thus the performance of radiomics needs to validate in prospective, multicenter studies.

Multiparametric MRI as a Predictor of PSA Response in Patients Undergoing Stereotactic Body Radiation Therapy for Prostate Cancer

Purpose

To maximize the therapeutic ratio, it is important to identify adverse prognostic features in men with prostate cancer, especially among those with intermediate risk disease, which represents a heterogeneous group. These men may benefit from treatment intensification. Prior studies have shown pretreatment mpMRI may predict biochemical failure in patients with intermediate and/or high-risk prostate cancer undergoing conventionally fractionated external beam radiation therapy and/or brachytherapy. This study aims to evaluate pretreatment mpMRI findings as a marker for outcome in patients undergoing stereotactic body radiation therapy (SBRT).

Methods and Materials

We identified all patients treated at our institution with linear accelerator based SBRT to 3625 cGy in 5 fractions, with or without androgen deprivation therapy (ADT) from November 2015 to March 2021. All patients underwent pretreatment Magnetic Resonance Imaging (MRI). Posttreatment Prostate Specific Imaging (PSA) measurements were typically obtained 4 months after SBRT, followed by every 3 to 6 months thereafter. A 2 sample t test was used to compare preoperative mpMRI features with clinical outcomes.

Results

One hundred twenty-three men were included in the study. Pretreatment MRI variables including median diameter of the largest intraprostatic lesion, median number of prostate lesions, and median maximal PI-RADS score, were each predictive of PSA nadir and time to PSA nadir (P < .0001). When separated by ADT treatment, this association remained for patients who were not treated with ADT (P < .001). In patients who received ADT, the pretreatment MRI variables were each significantly associated with time to PSA nadir (P < .01) but not with PSA nadir (P > 0.30). With a median follow-up time of 15.9 months (IQR: 8.5-23.3), only 3 patients (2.4%) experienced biochemical recurrence as defined by the Phoenix criteria.

Conclusions

Our experience shows the significant ability of mpMRI for predicting PSA outcome in prostate cancer patients treated with SBRT with or without ADT. Since PSA nadir has been shown to correlate with biochemical failure, this information may help radiation oncologists better counsel their patients regarding outcome after SBRT and can help inform future studies regarding who may benefit from treatment intensification with, for example, ADT and/or boosts to dominant intraprostatic lesions.

A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis

BACKGROUND

Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM.

METHODS

An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score.

RESULTS

An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing.

CONCLUSIONS

The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

External validation of nomograms including MRI features for the prediction of side-specific extraprostatic extension

BACKGROUND

Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP). Multiple nomograms, which include magnetic resonance imaging (MRI) information, are available predict side-specific EPE. It is crucial that the accuracy of these nomograms is assessed with external validation to ensure they can be used in clinical practice to support medical decision-making.

METHODS

Data of prostate cancer (PCa) patients that underwent robot-assisted RP (RARP) from 2017 to 2021 at four European tertiary referral centers were collected retrospectively. Four previously developed nomograms for the prediction of side-specific EPE were identified and externally validated. Discrimination (area under the curve [AUC]), calibration and net benefit of four nomograms were assessed. To assess the strongest predictor among the MRI features included in all nomograms, we evaluated their association with side-specific EPE using multivariate regression analysis and Akaike Information Criterion (AIC).

RESULTS

This study involved 773 patients with a total of 1546 prostate lobes. EPE was found in 338 (22%) lobes. The AUCs of the models predicting EPE ranged from 72.2% (95% CI 69.1-72.3%) (Wibmer) to 75.5% (95% CI 72.5-78.5%) (Nyarangi-Dix). The nomogram with the highest AUC varied across the cohorts. The Soeterik, Nyarangi-Dix, and Martini nomograms demonstrated fair to good calibration for clinically most relevant thresholds between 5 and 30%. In contrast, the Wibmer nomogram showed substantial overestimation of EPE risk for thresholds above 25%. The Nyarangi-Dix nomogram demonstrated a higher net benefit for risk thresholds between 20 and 30% when compared to the other three nomograms. Of all MRI features, the European Society of Urogenital Radiology score and tumor capsule contact length showed the highest AUCs and lowest AIC.

CONCLUSION

The Nyarangi-Dix, Martini and Soeterik nomograms resulted in accurate EPE prediction and are therefore suitable to support medical decision-making.

Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis

PURPOSE

In recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs.

MATERIALS AND METHODS

The PubMed, Embase, and Cochrane databases were searched up to May 17, 2023, to identify studies on prediction nomograms for EPE of PCa. The risk of bias in studies was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Summary estimates of sensitivity and specificity were obtained with bivariate random-effects model. Heterogeneity was investigated through meta-regression and subgroup analysis.

RESULTS

Forty-eight studies with a total of 57 contingency tables and 20,395 patients were included. No significant publication bias was observed for either the MRI-inclusive nomograms or clinical nomograms. For MRI-inclusive nomograms predicting EPE, the pooled AUC of validation cohorts was 0.80 (95% CI: 0.76, 0.83). For traditional clinical nomograms predicting EPE, the pooled AUCs of the Partin table and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram were 0.72 (95% CI: 0.68, 0.76) and 0.79 (95% CI: 0.75, 0.82), respectively.

CONCLUSION

Preoperative risk stratification is essential for PCa patients; both MRI-inclusive nomograms and traditional clinical nomograms had moderate diagnostic performance for predicting EPE in PCa. This study provides baseline comparative values for EPE prediction for future studies which is useful for evaluating preoperative risk stratification in PCa patients.

CRITICAL RELEVANCE STATEMENT

This meta-analysis firstly evaluated the diagnostic performance of preoperative MRI-inclusive nomograms and clinical nomograms for predicting extraprostatic extension (EPE) in prostate cancer (PCa) (moderate AUCs: 0.72-0.80). We provide baseline estimates for EPE prediction, these findings will be useful in assessing preoperative risk stratification of PCa patients.

KEY POINTS

• MRI-inclusive nomograms and traditional clinical nomograms had moderate AUCs (0.72-0.80) for predicting EPE. • MRI combined clinical nomogram may improve diagnostic accuracy of MRI alone for EPE prediction. • MSKCC nomogram had a higher specificity than Partin table for predicting EPE. • This meta-analysis provided baseline and comparative estimates of nomograms for EPE prediction for future studies.

A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging

Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.

Extracapsular extension of transitional zone prostate cancer miss-detected by multiparametric magnetic resonance imaging

OBJECTIVES

To demonstrate the importance of extracapsular extension (ECE) of transitional zone (TZ) prostate cancer (PCa), examine the causes of its missed detection by Mp-MRI, and develop a new predictive model by integrating multi-level clinical variables.

MATERIALS AND METHODS

This retrospective study included 304 patients who underwent laparoscopic radical prostatectomy after 12 + X needle transperineal transrectal ultrasound (TRUS)-MRI-guided targeted prostate biopsy from 2018 to 2021 in our center was performed.

RESULTS

In this study, the incidence rates of ECE were similar in patients with MRI lesions in the peripheral zone (PZ) and TZ (P = 0.66). However, the missed detection rate was higher in patients with TZ lesions than in those with PZ lesions (P < 0.05). These missed detections result in a higher positive surgical margin rate (P < 0.05). In patients with TZ lesions, detected MP-MRI ECE may have grey areas: the longest diameters of the MRI lesions were 16.5-23.5 mm; MRI lesion volumes were 0.63-2.51 ml; MRI lesion volume ratios were 2.75-8.86%; PSA were 13.85-23.05 ng/ml. LASSO regression was used to construct a clinical prediction model for predicting the risk of ECE in TZ lesions from the perspective of MRI and clinical features, including four variables: the longest diameter of MRI lesions, TZ pseudocapsule invasion, ISUP grading of biopsy pathology, and number of positive biopsy needles.

CONCLUSIONS

Patients with MRI lesions in the TZ have the same incidence of ECE as those with lesions in the PZ, but a higher missed detection rate.

MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review

PURPOSE

Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature.

METHODS

We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores.

RESULTS

We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science.

CONCLUSION

Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.

Impact of prebiopsy MRI on prostate cancer staging: Results from the Norwegian Prostate Cancer Registry

Objectives

The aim of this study is to evaluate the 2015 introduction of prebiopsy magnetic resonance imaging of the prostate (MRI-P) as the standard of care for diagnosing prostate cancer (PCa) by the Norwegian public health care authorities. There were three specific objectives of this study: first, to evaluate the consequences of using different TNM manuals for clinical T-staging (cT-staging) in a national setting; second, to determine if the data reveals that MRI-P based cT-staging is superior to digital rectal examination (DRE)-based cT-staging compared with pathological T-stage (pT-stage) post radical prostatectomy; and third, to assess whether treatment allocations have changed over time.

Materials and Methods

All patients registered in the Norwegian Prostate Cancer Registry between 2004 and 2021 were retrieved and 5538 were eligible for inclusion. Concordance between clinical T-stage (cT-stage) and pT-stage was assessed by percentage agreement, Cohen's kappa and Gwet's agreement.

Results

MR visualisation of lesions influences reporting of tumour extension beyond DRE findings. Agreement between cT-stage and pT-stage declined from 2004 to 2009, which coincided with an increase in the percentage being pT3. From 2010, agreement increased, which aligned with changes in cT-staging and the introduction of MRI-P. From 2017, regarding the reporting of cT-DRE and cT-Total (overall cT-stage), agreement diminished for cT-DRE but remained relatively stable (>60%) for cT-Total. Regarding treatment allocation, the study suggests that staging with MRI-P has shifted treatment towards radiotherapy in locally advanced high-risk disease.

Conclusion

Introduction of MRI-P has affected cT-stage reporting. Agreement between cT-stage and pT-stage appears to have improved. This study suggests that use of MRI-P influences treatment decisions in certain patient subgroups.

Using PSMA imaging for prognostication in localized and advanced prostate cancer

The use of prostate-specific membrane antigen (PSMA)-directed applications in modern prostate cancer management has evolved rapidly over the past few years, helping to establish new treatment pathways and provide further insights into prostate cancer biology. However, the prognostic implications of PSMA-PET have not been studied systematically, owing to rapid clinical implementation without long follow-up periods to determine intermediate-term and long-term oncological outcomes. Currently available data suggest that traditional prognostic factors and survival outcomes are associated with high PSMA expression (both according to immunohistochemistry and PET uptake) in men with localized and biochemically recurrent disease. Treatment with curative intent (primary and/or salvage) often fails when PSMA-positive metastases are present; however, the sensitivity of PSMA-PET in detecting all metastases is poor. Low PSMA-PET uptake in recurrent disease is a favourable prognostic factor; however, it can be associated with poor prognosis in conjunction with high ¹⁸F-fluorodeoxyglucose uptake in metastatic castration-resistant prostate cancer. Clinical trials embedding PSMA-PET for guiding management with reliable oncological outcomes are needed to support ongoing clinical use.

Comparison of Biparametric and Multiparametric Prostate Magnetic Resonance Imaging in Predicting Oncologic Outcomes After Radical Prostatectomy

Objective

This study aimed to evaluate the difference in predicting the pathological stage of retropubic radical prostatectomy (RRP) and biochemical recurrence (BCR) in patients with Prostate Imaging Reporting and Data System (PIRADS) scores of 3 and 4 on biparametric prostate magnetic resonance imaging (bpMRI) compared to patients who upgraded from PIRADS 3 to PIRADS 4 based on the contrast-enhanced PIRADS version 2.1.

Methods

This study evaluated 107 patients who underwent RRP and had preoperative multiparametric prostate magnetic resonance imaging (mpMRI) and were followed regularly. Group 1 included 31 patients evaluated as PIRADS 3 in both bpMRI and mpMRI, group 2 included 31 patients evaluated as PIRADS 3 in bpMRI and PIRADS 4 in mpMRI, and group 3 included 45 patients evaluated as PIRADS 4 without contrast. Comparisons were made between groups 1 and 2 and between groups 2 and 3.

Results

No significant difference was found between the groups in terms of demographic data, preoperative or postoperative radiology, and pathology findings. Extraprostatic extension positivity and BCR were more common in group 2 compared to group 1 although not significant. Multivariate regression analysis was performed to determine the risk factors in predicting BCR, which revealed the positivity of seminal vesicle invasion and high pathological stage in the pathology report as significant factors. Prostate-specific antigen (PSA) and PSA density were higher in group 3 than in group 2, but without significance.

Conclusions

This study revealed that mpMRI did not contribute in predicting BCR after RRP compared to bpMRI.

Transperineal Ultrasound Before and After Prostatectomy: Technical Approach and Description

This study assessed the feasibility of dynamic transperineal ultrasound (TPUS) pre/post-radical prostatectomy (RP). Ninety-eight patients were scanned pre-operatively and at four time-points post-operatively. TPUS was performed in 98 patients using an abdominal transducer at rest, during pelvic floor contraction (PFC) and Valsalva (VS) maneuver in supine and standing positions. Urodynamic evaluations included bladder neck angle at rest/PFC/VS, and degree of bladder neck movement. Pre-operative and post-operative measurements were technically feasible in >85% (supine) and >90% (standing) of patients. TPUS offers a reliable non-invasive dynamic assessment of the pelvic floor post-prostatectomy and may prove a useful adjunct for guiding exercises to preserve continence.

MRI Extraprostatic Extension Grade: Accuracy and Clinical Incremental Value in the Assessment of Extraprostatic Cancer

Objective

The purpose was to compare the accuracy of extraprostatic extension (EPE) grade on MRI predicting EPE with Partin tables, Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and combined models and to analyze the clinical incremental value of EPE grade.

Materials and Methods

105 prostate cancer patients confirmed by pathology after radical prostatectomy in our hospital from 2017 to 2021 were selected. The clinical stage, PSA, Gleason score, number of positive biopsy cores, and percentage of positive biopsy cores were recorded. Evaluate EPE grade according to EPE grade criteria, and calculate the probability of predicting EPE with Partin tables and MSKCCn. EPE grade is combined with Partin tables and MSKCCn to construct EPE grade+Partin tables and EPE grade+MSKCCn models. Calculate the area under the curve (AUC), sensitivity, and specificity of EPE grade, Partin tables, MSKCCn, EPE grade+Partin tables, and EPE grade+MSKCCn and compare their diagnostic efficacy. The clinical decision curve was used to analyze the clinical net income of each prediction scheme.

Results

The AUC of EPE grade was 0.79, Partin tables was 0.50, MSKCCn was 0.78, the EPE grade+Partin table model was 0.79, and the EPE grade+MSKCCn model was 0.83. After EPE grade was combined with Partin tables and MSKCCn, the diagnostic efficiency of clinical model was significantly improved (P < 0.05). There was no significant difference in the diagnostic efficacy of the combined model compared with the single EPE grade (P > 0.05). The calibration curve of the combined model shows that it has a good calibration degree for EPE. In the analysis of the decision curve, the net income of the EPE grade is higher than that of Partin tables and MSKCCn and is equal to the EPE grade+Partin tables and is slightly lower than that of EPE grade+MSKCCn. The clinical net income of the combined model is obviously higher than that of individual clinical models.

Conclusion

The accuracy of EPE classification in predicting prostate cancer EPE is high, and combined with the clinical model, it can significantly improve the diagnostic efficiency of the clinical model and increase the clinical benefit.

Multiparametric MRI for Staging of Prostate Cancer: A Multicentric Analysis of Predictive Factors to Improve Identification of Extracapsular Extension before Radical Prostatectomy

Simple Summary

In this multicentric study, we tested the accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting extracapsular extension (ECE) out of the prostate in order to plan surgical sparing of neurovascular bundles in radical prostatectomy. Univariate and multivariate logistic regression analyses were performed to identify other risk factors for ECE. We found that it has a good ability to exclude extracapsular extension but a poor ability to identify it correctly. Risk factors other than mpMRI that predicted ECE were as follows: prostatic specific antigen, digital rectal examination, ratio of positive cores, and biopsy grade group. We suggest that using mpMRI exclusively should not be recommended to decide on surgical approaches.

Abstract

The correct identification of extracapsular extension (ECE) of prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI) is crucial for surgeons in order to plan the nerve-sparing approach in radical prostatectomy. Nerve-sparing strategies allow for better outcomes in preserving erectile function and urinary continence, notwithstanding this can be penalized with worse oncologic results. The aim of this study was to assess the ability of preoperative mpMRI to predict ECE in the final prostatic specimen (PS) and identify other possible preoperative predictive factors of ECE as a secondary end-point. We investigated a database of two high-volume hospitals to identify men who underwent a prostate biopsy with a pre-biopsy mpMRI and a subsequent RP. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mpMRI in predicting ECE were calculated. A univariate analysis was performed to find the association between image staging and pathological staging. A multivariate logistic regression was performed to investigate other preoperative predictive factors. A total of 1147 patients were selected, and 203 out of the 1147 (17.7%) patients were classified as ECE according to the mpMRI. ECE was reported by pathologists in 279 out of the 1147 PS (24.3%). The PPV was 0.58, the NPV was 0.72, the sensitivity was 0.32, and the specificity was 0.88. The multivariate analysis found that PSA (OR 1.057, C.I. 95%, 1.016–1.100, p = 0.006), digital rectal examination (OR 0.567, C.I. 95%, 0.417–0.770, p = 0.0001), ratio of positive cores (OR 9.687, C.I. 95%, 3.744–25.006, p = 0.0001), and biopsy grade in prostate biopsy (OR 1.394, C.I. 95%, 1.025–1.612, p = 0.0001) were independent factors of ECE. The mpMRI has a great ability to exclude ECE, notwithstanding that low sensitivity is still an important limitation of the technique.

Magnetic Resonance Imaging-Targeted Biopsy and Pretherapeutic Prostate Cancer Risk Assessment: a Systematic Review: Biopsie ciblée par Imagerie par résonance magnétique et évaluation pré-thérapeutique du risque de cancer de la prostate : revue systématique

INTRODUCTION

Multiparametric magnetic resonance imaging (MRI) has been included in prostate cancer (PCa) diagnostic pathway and may improve disease characterization. The aim of this systematic review is to assess the added value of MRI-targeted biopsy (TB) in pre-therapeutic risk assessment models over existing tools based on systematic biopsy (SB) for localized PCa.

EVIDENCE ACQUISITION

A systematic search was conducted using Pubmed (Medline), Scopus and ScienceDirect databases according to Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. We included studies through October 2021 reporting on TB in pretherapeutic risk assessment models.

EVIDENCE SYNTHESIS

We identified 24 eligible studies including 24'237 patients for the systematic review. All included studies were retrospective and conducted in patients undergoing radical prostatectomy. Nine studies reported on the risk of extraprostatic extension, seven on the risk of lymph node invasion, three on the risk of biochemical recurrence and nine on the improvement of PCa risk stratification. Overall, the combination of TB with imaging, clinical and biochemical parameters outperformed current pretherapeutic risk assessment models. External validation studies are lacking for certain endpoints and the absence of standardization among TB protocols, including number of TB cores and fusion systems, may limit the generalizability of the results.

CONCLUSION

TB should be incorporated in pretherapeutic risk assessment models to improve clinical decision making. Further high-quality studies are required to determine models' generalizability while there is an urgent need to reach consensus on a standardized TB protocol. Long-term outcomes after treatment are also awaited to confirm the superiority of such models over classical risk classifications only based on SB. © 2022 Elsevier Masson SAS. All rights reserved.

Current Opinion on the Use of Magnetic Resonance Imaging in Staging Prostate Cancer: A Narrative Review

Accurate staging is critical for treatment planning and prognosis in men with prostate Cancer. Prostate magnetic imaging resonance (MRI) may aid in the staging evaluation by verifying organ-confined status, assessing the status of the pelvic lymph nodes, and establishing the local extent of the tumor in patients being considered for therapy. MRI has a high specificity for diagnosing extracapsular extension, and therefore may impact the decision to perform nerve sparing prostatectomy, along with seminal vesicle invasion and lymph node metastases; however, its sensitivity remains limited. Current guidelines vary significantly regarding endorsing the use of MRI for staging locoregional disease. For high-risk prostate cancer, most guidelines recommend cross sectional imaging, including MRI, to evaluate for more extensive disease that may merit change in radiation field, extended androgen deprivation therapy, or guiding surgical planning. Although MRI offers reasonable performance characteristics to evaluate bone metastases, guidelines continue to support the use of bone scintigraphy. Emerging imaging technologies, including coupling positron emission tomography (PET) with MRI, have the potential to improve the accuracy of prostate cancer staging with the use of novel radiotracers.

External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era

Introduction

To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND).

Materials and Methods

After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis.

Results

A median of 13 (IQR 9–18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (p = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA.

Conclusion

The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.

Integration of Prostate Biopsy Results with Pre-Biopsy Multiparametric Magnetic Resonance Imaging Findings Improves Local Staging of Prostate Cancer

PURPOSE

To assess the added value of histological information for local staging of prostate cancer (PCa) by comparing the accuracy of multiparametric MRI alone (mpMRI) and mpMRI with biopsy Gleason grade (mpMRI+Bx).

METHODS

133 consecutive patients who underwent preoperative 3T-MRI and subsequent radical prostatectomy for PCa were included in this single-centre retrospective study. mpMRI imaging was reviewed independently by two uroradiologists for the presence of extracapsular extension (ECE) and seminal vesicle invasion (SVI) on a 5-point Likert scale. For second reads, the radiologists received results of targeted fused MR/US biopsy (mpMRI+Bx) prior to re-staging.

RESULTS

The median patient age was 63 years (interquartile range (IQR) 58-67 years) and median PSA was 6.5 ng/mL (IQR 5.0-10.0 ng/mL). Extracapsular extension was present in 85/133 (63.9%) patients and SVI was present in 22/133 (16.5%) patients. For ECE prediction, mpMRI showed sensitivity and specificity of 63.5% and 81.3%, respectively, compared to 77.7% and 81.3% achieved by mpMRI+Bx. At an optimal cut-off value of Likert score ≥ 3, areas under the curves (AUCs) was .85 for mpMRI+Bx and .78 for mpMRI, P < .01. For SVI prediction, AUC was .95 for mpMRI+Bx compared to .92 for mpMRI; P = .20. Inter-reader agreement for ECE and SVI prediction was substantial for mpMRI (k range, .78-.79) and mpMRI+Bx (k range, .74-.79).

CONCLUSIONS

MpMRI+Bx showed superior diagnostic performance with an increased sensitivity for ECE prediction but no significant difference for SVI prediction. Inter-reader agreement was substantial for both protocols. Integration of biopsy information adds value when staging prostate mpMRI.

Diagnostic Performance of Extraprostatic Extension Grading System for Detection of Extraprostatic Extension in Prostate Cancer: A Diagnostic Systematic Review and Meta-Analysis

Purpose

To evaluate the diagnostic performance of the extraprostatic extension (EPE) grading system for detection of EPE in patients with prostate cancer (PCa).

Materials and Methods

We performed a literature search of Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar to identify eligible articles published before August 31, 2021, with no language restrictions applied. We included studies using the EPE grading system for the prediction of EPE, with histopathological results as the reference standard. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR) were calculated with the bivariate model. Quality assessment of included studies was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

Results

A total of 4 studies with 1,294 patients were included in the current systematic review. The pooled sensitivity and specificity were 0.82 (95% CI 0.76–0.87) and 0.63 (95% CI 0.51–0.73), with the area under the hierarchical summary receiver operating characteristic (HSROC) curve of 0.82 (95% CI 0.79–0.85). The pooled LR+, LR−, and DOR were 2.20 (95% CI 1.70–2.86), 0.28 (95% CI 0.22–0.36), and 7.77 (95% CI 5.27–11.44), respectively. Quality assessment for included studies was high, and Deeks’s funnel plot indicated that the possibility of publication bias was low (p = 0.64).

Conclusion

The EPE grading system demonstrated high sensitivity and moderate specificity, with a good inter-reader agreement. However, this scoring system needs more studies to be validated in clinical practice.

Use of high-resolution micro-ultrasound to predict extraprostatic extension of prostate cancer prior to surgery: a prospective single-institutional study

PURPOSE

We aim to evaluate the accuracy of micro-ultrasound (microUS) in predicting extraprostatic extension (EPE) of Prostate Cancer (PCa) prior to surgery.

METHODS

Patients with biopsy-proven PCa scheduled for robot-assisted radical prostatectomy (RARP) were prospectively recruited. The following MRI-derived microUS features were evaluated: capsular bulging, visible breach of the prostate capsule (visible extracapsular extension; ECE), presence of hypoechoic halo, and obliteration of the vesicle-prostatic angle. The ability of each feature to predict EPE was determined.

RESULTS

Overall, data from 140 patients were examined. All predictors were associated with non-organ-confined disease (p < 0.001). Final pathology showed that 79 patients (56.4%) had a pT2 disease and 61 (43.3%) ≥ pT3. Rate of non-organ-confined disease increased from 44% in those individuals with only 1 predictor (OR 7.71) to 92.3% in those where 4 predictors (OR 72.00) were simultaneously observed. The multivariate logistic regression model including clinical parameters showed an area under the curve (AUC) of 82.3% as compared to an AUC of 87.6% for the model including both clinical and microUS parameters. Presence of ECE at microUS predicted EPE with a sensitivity of 72.1% and a specificity of 88%, a negative predictive value of 80.5% and positive predictive value of 83.0%, with an AUC of 80.4%.

CONCLUSIONS

MicroUS can accurately predict EPE at the final pathology report in patients scheduled for RARP.

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 Magnetic Resonance Imaging Analyses, Clinical Parameters, and Preoperative Nomograms in the Prediction of Extraprostatic Extension

Introduction: Proper planning of laparoscopic radical prostatectomy (RP) in patients with prostate cancer (PCa) is crucial to achieving good oncological results with the possibility of preserving potency and continence. Aim: The aim of this study was to identify the radiological and clinical parameters that can predict the risk of extraprostatic extension (EPE) for a specific site of the prostate. Predictive models and multiparametric magnetic resonance imaging (mpMRI) data from patients qualified for RP were compared. Material and methods: The study included 61 patients who underwent laparoscopic RP. mpMRI preceded transrectal systematic and cognitive fusion biopsy. Martini, Memorial Sloan-Kettering Cancer Center (MSKCC), and Partin Tables nomograms were used to assess the risk of EPE. The area under the curve (AUC) was calculated for the models and compared. Univariate and multivariate logistic regression analyses were used to determine the combination of variables that best predicted EPE risk based on final histopathology. Results: The combination of mpMRI indicating or suspecting EPE (odds ratio (OR) = 7.49 (2.31–24.27), p < 0.001) and PSA ≥ 20 ng/mL (OR = 12.06 (1.1–132.15), p = 0.04) best predicted the risk of EPE for a specific side of the prostate. For the prediction of ipsilateral EPE risk, the AUC for Martini’s nomogram vs. mpMRI was 0.73 (p < 0.001) vs. 0.63 (p = 0.005), respectively (p = 0.131). The assessment of a non-specific site of EPE by MSKCC vs. Partin Tables showed AUC values of 0.71 (p = 0.007) vs. 0.63 (p = 0.074), respectively (p = 0.211). Conclusions: The combined use of mpMRI, the results of the systematic and targeted biopsy, and prostate-specific antigen baseline can effectively predict ipsilateral EPE (pT3 stage).

An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

Background

Biparametric MRI (comprising T2-weighted MRI and apparent diffusion coefficient maps) is increasingly being used to characterise prostate cancer. Although previous studies have combined Prostate Imaging–Reporting & Data System (PI-RADS)-based MRI findings with routinely available clinical variables and with deep learning-based imaging predictors, respectively, for prostate cancer risk stratification, none have combined all three. We aimed to construct an integrated nomogram (referred to as ClaD) combining deep learning-based imaging predictions, PI-RADS scoring, and clinical variables to identify clinically significant prostate cancer on biparametric MRI.

Methods

In this retrospective multicentre study, we included patients with prostate cancer, with histopathology or biopsy reports and a screening or diagnostic MRI scan in the axial view, from four cohorts in the USA (from University Hospitals Cleveland Medical Center, Icahn School of Medicine at Mount Sinai, Cleveland Clinic, and Long Island Jewish Medical Center) and from the PROSTATEx Challenge dataset in the Netherlands. We constructed an integrated nomogram combining deep learning, PI-RADS score, and clinical variables (prostate-specific antigen, prostate volume, and lesion volume) using multivariable logistic regression to identify clinically significant prostate cancer on biparametric MRI. We used data from the first three cohorts to train the nomogram and data from the remaining two cohorts for independent validation. We compared the performance of our ClaD integrated nomogram with that of integrated nomograms combining clinical variables with either the deep learning-based imaging predictor (referred to as DIN) or PI-RADS score (referred to as PIN) using area under the receiver operating characteristic curves (AUCs). We also compared the ability of the nomograms to predict biochemical recurrence on a subset of patients who had undergone radical prostatectomy. We report cross-validation AUCs as means for the training set and used AUCs with 95% CIs to assess the performance on the test set. The difference in AUCs between the models were tested for statistical significance using DeLong’s test. We used log-rank tests and Kaplan-Meier curves to analyse survival.

Findings

We investigated 592 patients (823 lesions) with prostate cancer who underwent 3T multiparametric MRI at five hospitals in the USA between Jan 8, 2009, and June 3, 2017. The training data set consisted of 368 patients from three sites (the PROSTATEx Challenge cohort [n=204], University Hospitals Cleveland Medical Center [n=126], and Icahn School of Medicine at Mount Sinai [n=38]), and the independent validation data set consisted of 224 patients from two sites (Cleveland Clinic [n=151] and Long Island Jewish Medical Center [n=73]). The ClaD clinical nomogram yielded an AUC of 0·81 (95% CI 0·76–0·85) for identification of clinically significant prostate cancer in the validation data set, significantly improving performance over the DIN (0·74 [95% CI 0·69–0·80], p=0·0005) and PIN (0·76 [0·71–0·81], p<0·0001) nomograms. In the subset of patients who had undergone radical prostatectomy (n=81), the ClaD clinical nomogram resulted in a significant separation in Kaplan-Meier survival curves between patients with and without biochemical recurrence (HR 5·92 [2·34–15·00], p=0·044), whereas the DIN (1·22 [0·54–2·79], p=0·65) and PIN nomograms did not (1·30 [0·62–2·71], p=0·51).

Interpretation

Risk stratification of patients with prostate cancer using the integrated ClaD nomogram could help to identify patients with prostate cancer who are at low risk, very low risk, and favourable intermediate risk, who might be candidates for active surveillance, and could also help to identify patients with lethal prostate cancer who might benefit from adjuvant therapy.

Funding

National Cancer Institute of the US National Institutes of Health, National Institute for Biomedical Imaging and Bioengineering, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, US Department of Defense, US National Institute of Diabetes and Digestive and Kidney Diseases, The Ohio Third Frontier Technology Validation Fund, Case Western Reserve University, Dana Foundation, and Clinical and Translational Science Collaborative.

Risk of adverse pathological features for intermediate risk prostate cancer: Clinical implications for definitive radiation therapy

BACKGROUND

Intermediate risk prostate cancer represents a largely heterogeneous group with diverse disease extent. We sought to establish rates of adverse pathological features important for radiation planning by analyzing surgical specimens from men with intermediate risk prostate cancer who underwent immediate radical prostatectomy, and to define clinical pathologic features that may predict adverse outcomes.

MATERIALS AND METHODS

A total of 1552 men diagnosed with intermediate risk prostate cancer who underwent immediate radical prostatectomy between 1/1/2005 and 12/31/2015 were reviewed. Inclusion criteria included available preoperative PSA level, pathology reports of transrectal ultrasound-guided prostate biopsy, and radical prostatectomy. Incidences of various pathological adverse features were evaluated. Patient characteristics and clinical disease features were analyzed for their predictive values.

RESULTS

Fifty percent of men with high risk features (defined as PSA >10 but <20 or biopsy primary Gleason pattern of 4) had pathological upstage to T3 or higher disease. The incidence of upgrade to Gleason score of 8 or higher and the incidence of lymph node positive disease was low. Biopsy primary Gleason pattern of 4, and PSA greater than 10 but less than 20, affected adverse pathology in addition to age and percent positive biopsy cores. Older age and increased percentage of positive cores were significant risk factors of adverse pathology.

CONCLUSION

Our findings underscore the importance of comprehensive staging beyond PSA level, prostate biopsy, and CT/bone scan for men with intermediate risk prostate cancer proceeding with radiation in the era of highly conformal treatment.

Evaluation of image-based prognostic parameters of post-prostatectomy urinary incontinence: A literature review

Prostate cancer is the second most common male cancer, and radical prostatectomy is a highly effective treatment for intermediate and high-risk disease. However, post-prostatectomy urinary incontinence remains a major functional side-effect in patients undergoing radical prostatectomy. Despite recent improvements in preoperative imaging quality and surgical techniques, it remains challenging to predict or prevent occurrence of this complication. The aim of this research was to review the current published literature on pre- and postoperative imaging evaluation of the prostate and pelvic structures, to identify added value in the prediction of post-prostatectomy urinary incontinence. A computerized bibliographic search of the PubMed library was carried out to identify imaging-based articles evaluating the pelvic floor and surrounding structures pre- and/or postradical prostatectomy to predict post-prostatectomy urinary incontinence. A total of 32 articles were included. Of these, 29 papers assessed the importance of magnetic resonance imaging evaluation, with a total of 16 parameters evaluated. The most common parameters were intravesical protrusion, the membranous urethral length, prostatic volume and periurethral fibrosis. Preoperative membranous urethral length and its preservation after surgery showed the strongest correlation with urinary incontinence. Three studies evaluated ultrasound, with all carried out postoperatively. This technique benefits from a dynamic evaluation, and the results are promising for proximal urethral hypermobility and the degree of bladder neck funneling on the Valsalva maneuver. Several imaging studies evaluated the predictors of post-prostatectomy urinary incontinence, with preoperative membranous urethral length offering the most promise. However, the current literature is limited by the single-center nature of studies, and the heterogeneity in patient populations and methodologies used.

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer

BACKGROUND

To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data.

METHODS

Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings.

RESULTS

Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all).

CONCLUSIONS

In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.

Comparison of biparametric versus multiparametric prostate MRI for the detection of extracapsular extension and seminal vesicle invasion in biopsy naïve patients

PURPOSE

To compare biparametric MRI (bpMRI) with multiparametric MRI (mpMRI) staging accuracy in assessing extracapsular extension (ECE) and seminal vesicle invasion (SVI).

METHOD

Biopsy-naïve patients undergoing 3 T-MRI before radical prostatectomy for clinically significant prostate cancer were included in this single-centre retrospective study. Two uroradiologists separately evaluated bpMRI and mpMRI for presence of ECE and SVI using a 5-point Likert scale (1: ECE/SVI highly unlikely, 5: ECE/SVI highly likely).

RESULTS

110 men of median age 63 years and PSA 8.5 ng/mL were included. ECE and SVI was confirmed histologically in 71/110 (64.5 %) and 18/110 (16.4 %) patients, respectively. Sensitivity and specificity of bpMRI versus mpMRI for predicting ECE was 59.1 % and 87.2 % versus 66.2 % and 84.6 %, respectively. For SVI detection, the sensitivity and specificity for bpMRI versus mpMRI was 66.7 % and 92.4 % versus 83.3 % and 97.8 %, respectively. At an optimal cut-off Likert score ≥3 for ECE prediction, mpMRI area under the receiver operating curve (AUC) was 0.80 (95 % confidence interval (CI) 0.72-0.87) versus 0.78 (95 % CI 0.69-0.86) for bpMRI (p = 0.52) and for SVI, mpMRI AUC was 0.91 (95 % CI 0.84-0.96) versus 0.86 (95 % CI 0.78-0.92) for bpMRI (p = 0.02), respectively. Inter-reader agreement for both ECE and SVI prediction was substantial, with a marginally higher k-value for mpMRI (k range, 0.67-0.75) than bpMRI (k range, 0.65-0.69).

CONCLUSIONS

Diagnostic performance of bpMRI and mpMRI was comparable for detection of ECE, however, mpMRI with contrast was superior for SVI detection and improved the inter-reader agreement.

Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer

Background

Accurate identification of pelvic lymph node metastasis (PLNM) in patients with prostate cancer (PCa) is crucial for determining appropriate treatment options. Here, we built a PLNM-Risk calculator to obtain a precisely informed decision about whether to perform extended pelvic lymph node dissection (ePLND).

Methods

The PLNM-Risk calculator was developed in 280 patients and verified internally in 71 patients and externally in 50 patients by integrating a set of radiologists’ interpretations, clinicopathological factors and newly refined imaging indicators from MR images with radiomics machine learning and deep transfer learning algorithms. Its clinical applicability was compared with Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms.

Findings

The PLNM-Risk achieved good diagnostic discrimination with areas under the receiver operating characteristic curve (AUCs) of 0.93 (95% CI, 0.90-0.96), 0.92 (95% CI, 0.84-0.97) and 0.76 (95% CI, 0.62-0.87) in the training/validation, internal test and external test cohorts, respectively. If the number of ePLNDs missed was controlled at < 2%, PLNM-Risk provided both a higher number of ePLNDs spared (PLNM-Risk 59.6% vs MSKCC 44.9% vs Briganti 38.9%) and a lower number of false positives (PLNM-Risk 59.3% vs MSKCC 70.1% and Briganti 72.7%). In follow-up, patients stratified by the PLNM-Risk calculator showed significantly different biochemical recurrence rates after surgery.

Interpretation

The PLNM-Risk calculator offers a noninvasive clinical biomarker to predict PLNM for patients with PCa. It shows improved accuracy of diagnosis support and reduced overtreatment burdens for patients with findings suggestive of PCa.

Funding

This work was supported by the Key Research and Development Program of Jiangsu Province (BE2017756) and the Suzhou Science and Technology Bureau-Science and Technology Demonstration Project (SS201808).

Predicting early outcomes in patients with intermediate- and high-risk prostate cancer using prostate-specific membrane antigen positron emission tomography and magnetic resonance imaging

Objectives

To identify predictors of early oncological outcomes in patients who opt for robot‐assisted laparoscopic radical prostatectomy (RARP) for localized prostate cancer (PCa), including conventional prognostic variables as well as multiparametric magnetic resonance imaging (mpMRI) and prostate‐specific membrane antigen (PSMA) positron emission tomography (PET).

Patients and Methods

This observational study included 493 patients who underwent RARP and extended pelvic lymph node dissection (ePLND) for unfavourable intermediate‐ or high‐risk PCa. Outcome measurement was biochemical progression of disease, defined as any postoperative prostate‐specific antigen (PSA) value ≥0.2 ng/mL, or the start of additional treatment. Cox regression analysis was performed to assess predictors for biochemical progression, including initial PSA value, biopsy Grade Group (GG), T‐stage on mpMRI, and lymph node status on PSMA PET imaging (miN0 vs miN1).

Results

The median (interquartile range) total follow‐up of all included patients without biochemical progression was 12.6 (7.5–22.7) months. When assessing biochemical progression after surgery, initial PSA value (per doubling; odds ratio [OR] 1.22, 95% confidence interval [CI] 1.07–1.40; P = 0.004), biopsy GG ≥4 vs GG 1–2 (OR 1.83, 95% CI 1.18–2.85; P = 0.007), T‐stage on mpMRI (rT3a vs rT2: OR 2.13, 95% CI 1.39–3.27; P = 0.001; ≥rT3b vs rT2: OR 4.78, 95% CI 3.20–7.16; P < 0.001) and miN1 on PSMA PET imaging (OR 2.94, 95% CI 2.02–4.27; P < 0.001) were independent predictors of early biochemical progression of disease.

Conclusion

Initial PSA value, biopsy GG ≥4, ≥rT3 disease on mpMRI and miN1 disease on PSMA PET were predictors of early biochemical progression after RARP. Identifying these patients with an increased risk of early biochemical progression after surgery may have major implications for patient counselling in radical treatment decisions and on patient selection for modern (neo‐)adjuvant and systematic treatments.

Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study

PURPOSE

A balance between preserving urinary continence as well as sexual potency and achieving negative surgical margins is of clinical relevance while implementary difficulty. Accurate detection of extracapsular extension (ECE) of prostate cancer (PCa) is thus crucial for determining appropriate treatment options. We aimed to develop and validate an artificial intelligence (AI)-based tool for detecting ECE of PCa using multiparametric magnetic resonance imaging (mpMRI).

METHODS

Eight hundred and forty nine consecutive PCa patients who underwent mpMRI and prostatectomy without previous radio- or hormonal therapy from two medical centers were retrospectively included. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts' prior knowledge (PAGNet) from 596 training patients. Model validation was performed in 150 internal and 103 external patients. Performance comparison was made between AI, two experts using a criteria-based ECE grading system, and expert-AI interaction.

RESULTS

An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% confidence interval [CI], 0.827-0.884), 0.807 (95% CI, 0.735-0.867), and 0.728 (95% CI, 0.631-0.811) in training, internal, and external validation data, respectively. The performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When experts' interpretations were adjusted by AI assessments, the performance of two experts was improved.

CONCLUSION

Our AI tool, showing improved accuracy, offers a promising alternative to human experts for ECE staging using mpMRI.

Preoperative multiparametric MRI does not lower positive surgical margin rate in a large series of patients undergoing robot-assisted radical prostatectomy

To optimize functional outcomes after robot-assisted radical prostatectomy (RARP), surgical preservation of the neurovascular bundle is desired. However, nerve-sparing surgery (NSS) is only feasible in the absence of extraprostatic tumour extension (T-stage 3) to avoid the risk of positive surgical margins (PSM). Multiparametric magnetic-resonance imaging (MRI) is increasingly performed for primary prostate cancer and provides information on local tumour stage. In this study, we evaluated whether the availability of information from MRI influenced the incidence of PSM. A total of 523 patients undergoing RARP for localized prostate cancer in a single Dutch reference centre for prostate-cancer surgery were retrospectively evaluated (2013-2017). Patient characteristics and postoperative outcomes were retrieved. Patients were stratified according to the presence of a preoperative MRI. The incidence of PSM and proportion of patients receiving NSS was analysed using Chi-square tests and logistic regression analysis. N = 139 of 523 (26.6%) patients had a preoperative MRI scan available. Patients with MRI had identical preoperative characteristics compared to the patients without MRI, except for a higher percentage of patients having a prostate-specific antigen value ≥ 20 ng/mL (20.1% versus 9.4%, p = 0.004). PSM were present in 107/384 (27.9%) patients without MRI compared to 36/139 (25.9%) patients with an MRI scan before surgery (p = 0.66). Unilateral NSS was performed more often in the MRI group (26.6% vs. 11.7%), but NSS on both sides was more frequently performed in patients without MRI (57.6% versus 69.8%) (p < 0.001). MRI was not associated with PSM in multivariate analysis (p = 0.265). Preoperative mpMRI imaging was not associated with lower rates of positive surgical margins in patients undergoing RARP for localized prostate cancer.

External Validation of the Extraprostatic Extension Grade on MRI and Its Incremental Value to Clinical Models for Assessing Extraprostatic Cancer

Objectives

To externally validate the extraprostatic extension (EPE) grade criteria on MRI and analyze the incremental value of EPE grade to clinical models of prostate cancer.

Methods

A consecutive 130 patients who underwent preoperative prostate MRI followed by radical prostatectomy between January 2015 to January 2020 in our institution were retrospectively enrolled. The EPE grade, Cancer of the Prostate Risk Assessment (CAPRA), and Memorial Sloan Kettering Cancer Center nomogram (MSKCCn) score for each patient were assigned. Significant clinicopathological factors in univariate and multivariate analyses were combined with EPE grade to build the Clinical + EPE grade model, and the CAPRA and MSKCCn score were also combined with EPE grade to build the CAPRA + EPE grade and MSKCCn + EPE grade model, respectively. The area under the curve (AUC), sensitivity and specificity of these models were calculated to evaluate their diagnostic performance. Calibration and decision curve analyses were used to analyze their calibration performance and clinical utility.

Results

The AUC for predicting EPE was 0.767–0.778 for EPE grade, 0.704 for CAPRA, and 0.723 for MSKCCn. After combination with EPE grade, the AUCs of these clinical models increased significantly than using clinical models along (P < 0.05), but was comparable with using EPE grade alone (P > 0.05). The calibration curves of EPE grade, clinical models and combined models showed that these models are well-calibrated for EPE. In the decision curve analysis, EPE grade showed slightly higher net benefit than MSKCCn and CAPRA.

Conclusion

The EPE grade showed good performance for evaluating EPE in our cohort and possessed well clinical utility. Further combinations with the EPE grade could improve the diagnostic performance of clinical models.

Risk of adverse pathology at prostatectomy in the era of MRI and targeted biopsies; rethinking active surveillance for intermediate risk prostate cancer patients

PURPOSE

Men with intermediate risk (IR) prostate cancer (CaP) are often excluded from active surveillance (AS) due to higher rates of adverse pathology (AP). We determined our rate of AP in men who underwent multiparametric MRI (MpMRI) with combined biopsy (CB) consisting of targeted biopsy (TB) and systematic biopsy (SB) prior to radical prostatectomy (RP).

METHODS

A retrospective review was conducted of men with Gleason Grade Group (GG) 2 disease who underwent RP after SB alone or after preoperative MRI with CB. AP was defined as either pathologic stage T3a (AP ≥ T3a) or pathologic stage T3b (AP ≥ T3b) and/or GG upgrading. Rates of AP were determined for both groups and those who fit the National Comprehensive Cancer Network (NCCN) definition of favorable IR (FIR) or the low volume IR (LVIR) criteria. Multivariable logistic regression was used to determine predictive factors.

RESULTS

The overall rate of AP ≥ T3b was 21.2% in the SB group vs. 8.6% in the MRI with CB group, P = 0.006. This rate was lowered to 6.8% and 5.6% when men met the definition of NCCN FIR or LVIR, respectively. Suspicion for extraprostatic extension (EPE) (OR 7.65, 95% CI 1.77-33.09, P = 0.006) and positive cores of GG 2 on SB (OR 1.43, 95% CI 1.05-1.96, P = 0.023) were significant for predicting AP ≥ T3b.

CONCLUSIONS

Rates of AP at RP after MRI with CB are lower than studies prior to the adoption of this technology, suggesting that more men with IR disease may be considered for AS. However, increasing cores positive on SB and MRI findings suggestive of EPE remain unsafe.

Nomograms in Urologic Oncology: Lights and Shadows

Decision-making in urologic oncology involves integrating multiple clinical data to provide an answer to the needs of a single patient. Although the practice of medicine has always been an “art” involving experience, clinical data, scientific evidence and judgment, the creation of specialties and subspecialties has multiplied the challenges faced every day by physicians. In the last decades, with the field of urologic oncology becoming more and more complex, there has been a rise in tools capable of compounding several pieces of information and supporting clinical judgment and experience when approaching a difficult decision. The vast majority of these tools provide a risk of a certain event based on various information integrated in a mathematical model. Specifically, most decision-making tools in the field of urologic focus on the preoperative or postoperative phase and provide a prognostic or predictive risk assessment based on the available clinical and pathological data. More recently, imaging and genomic features started to be incorporated in these models in order to improve their accuracy. Genomic classifiers, look-up tables, regression trees, risk-stratification tools and nomograms are all examples of this effort. Nomograms are by far the most frequently used in clinical practice, but are also among the most controversial of these tools. This critical, narrative review will focus on the use, diffusion and limitations of nomograms in the field of urologic oncology.

Influence of pelvic lymph node dissection and node-positive disease on biochemical recurrence, secondary treatment, and survival after radical prostatectomy in men with prostate cancer

BACKGROUND

The benefit of pelvic lymph node dissection (PLND) at radical prostatectomy (RP) remains unclear given the low prevalence of known nodal disease (pN1) and concerns about its therapeutic utility.

OBJECTIVE

To characterize the impact of PLND and secondary treatment on oncologic outcomes.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study of men who underwent primary RP with PLND for prostate cancer (PCa) at our institution since 2003. Men stratified by nodal status.

OUTCOME MEASURES AND STATISTICAL ANALYSIS

Outcomes include biochemical recurrence-free survival (bRFS), overall survival, and PCa-specific mortality (PCSM). Multivariable Cox regression models used for each outcome.

RESULTS AND LIMITATIONS

Of 1,543 men who underwent primary RP, 174 (11%) had pN1 disease. Median follow-up was 34 months (interquartile range, 15-62). Seven-year outcomes were similar whether less than or ≥14 LNs dissected. Among node-positive patients, 29% had undetectable (UDT) prostate-specific antigen (PSA), 11% had UDT PSA + adjuvant therapy, and 60% had detectable PSA, and 7-year bRFS differed (75% for UDT PSA, 90% for UDT + adjuvant therapy, 38% for detectable PSA, p < .01). Survival outcomes did not differ. In multivariable analysis, detectable PSA (vs. UDT, HR 5.2, 95% CI 2.0-13.3) associated with worse bRFS. After salvage treatment, 7-year outcomes did not differ between groups. Study limited by retrospective review.

Value of clinical parameters and MRI with PI-RADSV2 in predicting seminal vesicle invasion of prostate cancer

OBJECTIVE

To investigate the usefulness of magnetic resonance imaging (MRI) with Prostate Imaging Reporting and Data System version 2 (PI-RADS_V2) and clinical parameters in predicting seminal vesicle invasion (SVI).

MATERIAL AND METHODS

In this retrospective study, we identified 569 prostate cancer patients who underwent radical prostatectomy with MRI before surgery. SVI was interpreted with PI-RADS_V2. Clinical parameters such as the prostate-specific antigen (PSA) and Gleason score (GS) were analyzed for the prediction of SVI. Logistic regression models and receiver operating characteristic (ROC) curves were used to evaluate SVI based on clinical parameters and MRI with PI-RADS_V2.

RESULTS

The median age at presentation was 67 years (43-85 years). The median PSA level was 6.1 ng/mL (2.2-72.8 ng/mL). There were 113 patients with a biopsy GS of ≥ 8. A total of 34 patients (6.0%) were interpreted to have SVI by MRI of which 20 were true positive, and 52 patients (9.1%) had true SVI in the final pathologic analysis. In multivariable analysis, PSA (HR: 1.03, 95% CI: 1.00-1.07), biopsy GS ≥ 8 (HR: 4.14, 95% CI: 2.12-8.09), and MRI with PI-RADS_V2 (HR: 14.67, 95% CI: 6.34-33.93) were significantly associated with pathologic SVI. The area under the curve of the model based on the clinical parameters PSA and GS plus MRI (0.862) was significantly larger than that of the model based on clinical parameters alone (0.777, p < 0.001).

CONCLUSIONS

MRI with PI-RADS_V2 using the clinical parameters PSA and GS was effective in predicting SVI.

Development and External Validation of a Novel Nomogram to Predict Side-specific Extraprostatic Extension in Patients with Prostate Cancer Undergoing Radical Prostatectomy

BACKGROUND

Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP).

OBJECTIVE

To develop and externally validate nomograms including multiparametric magnetic resonance imaging (mpMRI) information to predict side-specific EPE.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective analysis of 1870 consecutive prostate cancer patients who underwent robot-assisted RP from 2014 to 2018 at three institutions.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Four multivariable logistic regression models were established, including combinations of patient-based and side-specific variables: prostate-specific antigen (PSA) density, highest ipsilateral International Society of Urological Pathology (ISUP) biopsy grade, ipsilateral percentage of positive cores on systematic biopsy, and side-specific clinical stage assessed by both digital rectal examination and mpMRI. Discrimination (area under the curve [AUC]), calibration, and net benefit of these models were assessed in the development cohort and two external validation cohorts.

RESULTS AND LIMITATIONS

On external validation, AUCs of the four models ranged from 0.80 (95% confidence interval [CI] 0.68-0.88) to 0.83 (95% CI 0.72-0.90) in cohort 1 and from 0.77 (95% CI 0.62-0.87) to 0.78 (95% CI 0.64-0.88) in cohort 2. The three models including mpMRI staging information resulted in relatively higher AUCs compared with the model without mpMRI information. No major differences between the four models regarding net benefit were established. The model based on PSA density, ISUP grade, and mpMRI T stage was superior in terms of calibration. Using this model with a cut-off of 20%, 1980/2908 (68%) prostatic lobes without EPE would be found eligible for nerve sparing, whereas non-nerve sparing would be advised in 642/832 (77%) lobes with EPE.

CONCLUSIONS

Our analysis resulted in a simple and robust nomogram for the prediction of side-specific EPE, which should be used to select patients for nerve-sparing RP.

PATIENT SUMMARY

We developed a prediction model that can be used to assess accurately the likelihood of tumour extension outside the prostate. This tool can guide patient selection for safe nerve-sparing surgery.

Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer

Objective

To develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies.

Patients and Methods

We retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostate-specific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator.

Results

A total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit.

Conclusion

We developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients.

Diagnostic performance of a nomogram incorporating cribriform morphology for the prediction of adverse pathology in prostate cancer at radical prostatectomy

The aim of the present study was to develop a novel nomogram that incorporated clinical factors, imaging parameters and biopsy pathological factors (including cribriform morphology) to predict adverse pathology in prostate cancer (PCa). A total of 223 patients with PCa, who had undergone preoperative multi-parametric magnetic resonance imaging and had a biopsy of Gleason pattern (GP) 4, absence of GP 5 and pure Grade Group (GG) 3 [Gleason score (GS) 3+4, GS 4+3, GS 4+4], were retrospectively enrolled onto the study. The contribution of GG to the biopsy and Prostate Imaging Reporting and Data System (PI-RADS) score for PCa harboring adverse pathology were analyzed. Univariate and multivariate logistic regression analyses were performed to determine significant pathology predictors of adverse pathology for nomogram development. The nomogram was internally validated using bootstrapping with 1,000 iterations. The diagnostic performance of the nomogram was analyzed by receiver operating characteristics (ROC) analysis and decision curve analysis (DCA). A higher biopsy GG and PI-RADS score were associated with an increased likelihood of adverse pathology. Prostate specific antigen density (PSAD), biopsy GG, cribriform morphology on biopsy and PI-RADS score were significant predictors and were included in the nomogram. The ROC area under the curve of the nomogram was 0.88 (95% confidence interval, 0.84-0.91), with a high specificity (0.91) and moderate sensitivity (0.72). The novel nomogram was shown to have a higher net benefit for the prediction of adverse pathology in PCa, compared with any individual factors determined by DCA. Overall, a novel nomogram incorporating PSAD, PI-RADS score, biopsy GG and cribriform morphology on biopsy was shown to perform well in the prediction of PCa harboring adverse pathology at the time of radical prostatectomy.

Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy

Background

To determine the added value of preoperative prostate multiparametric MRI (mpMRI) supplementary to clinical variables and their role in predicting post prostatectomy adverse findings and biochemically recurrent cancer (BCR).

Methods

All consecutive patients treated at HUS Helsinki University Hospital with robot assisted radical prostatectomy (RALP) between 2014 and 2015 were included in the analysis. The mpMRI data, clinical variables, histopathological characteristics, and follow-up information were collected. Study end-points were adverse RALP findings: extraprostatic extension, seminal vesicle invasion, lymph node involvement, and BCR. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram, Cancer of the Prostate Risk Assessment (CAPRA) score and the Partin score were combined with any adverse findings at mpMRI. Predictive accuracy for adverse RALP findings by the regression models was estimated before and after the addition of MRI results. Logistic regression, area under curve (AUC), decision curve analyses, Kaplan-Meier survival curves and Cox proportional hazard models were used.

Results

Preoperative mpMRI data from 387 patients were available for analysis. Clinical variables alone, MSKCC nomogram or Partin tables were outperformed by models with mpMRI for the prediction of any adverse finding at RP. AUC for clinical parameters versus clinical parameters and mpMRI variables were 0.77 versus 0.82 for any adverse finding. For MSKCC nomogram versus MSKCC nomogram and mpMRI variables the AUCs were 0.71 and 0.78 for any adverse finding. For Partin tables versus Partin tables and mpMRI variables the AUCs were 0.62 and 0.73 for any adverse finding. In survival analysis, mpMRI-projected adverse RP findings stratify CAPRA and MSKCC high-risk patients into groups with distinct probability for BCR.

Conclusions

Preoperative mpMRI improves the predictive value of commonly used clinical variables for pathological stage at RP and time to BCR. mpMRI is available for risk stratification prebiopsy, and should be considered as additional source of information to the standard predictive nomograms.

Role of Multiparametric Magnetic Resonance Imaging in Predicting Pathologic Outcomes in Prostate Cancer

Multiparametric magnetic resonance imaging (mpMRI) and the introduction of standardized protocols for its interpretation have had a significant impact on the field of prostate cancer (PC). Multiple randomized controlled trials have shown that the sensitivity for detection of clinically significant PC is increased when mpMRI results are the basis for indication of a prostate biopsy. The added value with regards to sensitivity has been strongest for patients with persistent suspicion for PC after a prior negative biopsy. Although enhanced sensitivity of mpMRI is convincing, studies that have compared mpMRI with prostatectomy specimens prepared by whole-mount section analysis have shown a significant number of lesions that were not detected by mpMRI. In this context, the importance of an additional systematic biopsy (SB) is still being debated. While SB in combination with targeted biopsies leads to an increased detection rate, most of the tumors detected by SB only are considered clinically insignificant. Currently, multiple risk calculation tools are being developed that include not only clinical parameters but mpMRI results in addition to clinical parameters in order to improve risk stratification for PC, such as the Partin tables. In summary, mpMRI of the prostate has become a standard procedure recommended by multiple important guidelines for the diagnostic work-up of patients with suspicion of PC.

Radiomics Based on Multiparametric Magnetic Resonance Imaging to Predict Extraprostatic Extension of Prostate Cancer

Background: To develop a radiomics model based on multiparametric MRI (mpMRI) for preoperative prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa). Methods: Ninety-five pathology-confirmed PCa patients with 115 lesions (49 positive and 66 negative) were retrospectively enrolled. A 3.0T MR scanner was used to perform T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Radiomics features extracted from T2WI, DWI, apparent diffusion coefficient (ADC), and DCE were used to build a radiomics model. Patients' clinical and pathological variables were also obtained to build a clinical model. The radiomics model and clinical model were further integrated to build a combined nomogram. All lesions were randomly divided into the training group (82 lesions) and the validation group (33 lesions). A least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to build the radiomics model. The diagnostic performance of different models was assessed by calculating the area under the curve (AUC) and compared using the Delong test. The calibration curve and decision curve analyses were used to assess the calibration and clinical usefulness of the radiomics model. Results: The AUC values for the radiomics model in the training and validation group were 0.919 and 0.865, respectively, with a good calibration performance. The decision curve analysis confirmed the clinical utility of the radiomics model. The accuracy, sensitivity, and specificity were 81.8, 71.4, and 89.5% in the validation group. In the validation group, the radiomics model outperformed the clinical model (AUC = 0.658, P = 0.020), and was comparable with the combined nomogram (AUC = 0.857, P = 0.644). Conclusion: The radiomics model based on mpMRI could different EPE and non-EPE lesions with satisfactory diagnostic performance, and this model might assist in predicting EPE before prostatectomy.

Use of multiparametric magnetic resonance imaging (mpMRI) in localized prostate cancer

Introduction: Prostate magnetic resonance imaging (MRI) is commonly used for localized disease mainly to detect intraprostatic lesions and to guide biopsies. Despite its documented success in clinical practice, limitations still exist for prostate MRI. In this review, we discuss common clinical uses of prostate MRI, its limitations, and potential solutions for those limitations.Areas covered: Current uses of prostate MRI and challenges discussed. Literature search in PubMed was completed using the keywords "prostate MRI, prostate cancer."Expert opinion: Prostate MRI is a useful method for localization, biopsy, and treatment guidance of prostate cancer. Certain limitations of prostate MRI such as false negatives due to spatial resolution and relatively low repeatability between different radiologists can potentially be solved by investing more on education training and artificial intelligence technology.

Combined systematic versus stand-alone multiparametric MRI-guided targeted fusion biopsy: nomogram prediction of non-organ-confined prostate cancer

OBJECTIVE

Based on unfavorable oncological and functional outcomes of non-organ-confined (NOC) prostate cancer (PCa), defined as ≥ pT3, pN1 or both, we aimed to develop a NOC prediction tool based on multiparametric MRI-guided targeted fusion biopsy (TBx).

MATERIALS AND METHODS

Analyses were restricted to 594 patients with simultaneous PCa detection at systematic biopsy (SBx), TBx and subsequent radical prostatectomy (RP) at our institution. Development (n = 396; cohort 1) and validation cohorts (n = 198; cohort 2) were used to develop and validate the NOC nomogram. A head-to-head comparison was performed between stand-alone TBx model and combined TBx/SBx model. Second validation was performed in patients with positive TBx, but negative SBx (n = 193; cohort 3).

RESULTS

The most parsimonious TBx model included three independent predictors of NOC: pretreatment PSA (OR 1.05 95% CI: 1.01-1.08), highest TBx-detected Gleason pattern (3 + 3 [REF] vs. ≥ 4 + 5; OR 9.3 95% CI 3.8-22) and presence of TBx-detected perineural invasion (OR 2.2 95% CI: 1.3-3.6). The combined TBx/SBx model had the same predictors. For the stand-alone TBx and combined TBx/SBx model, external validation yielded accuracy of 76.5% (95% CI: 69.3-83.1) and 76.6% (95% CI: 69.4-83.6) within cohort 2. The external validation of the stand-alone TBx model yielded 72.4% (95% CI: 65.0-79.6) accuracy within cohort 3.

CONCLUSION

Our stand-alone TBx-based nomogram can identify PCa patients at the risk of NOC, using three simple variables, with the similar accuracy as the TBx/SBx-based model. It is non-inferior to combined TBx/SBx-based model and performs with sufficient accuracy in specific patients with positive TBx, but negative SBx.

Correlation of Prostate-Imaging Reporting and Data Scoring System scoring on multiparametric prostate magnetic resonance imaging with histopathological factors in radical prostatectomy material in Turkish prostate cancer patients: a multicenter study of the Urooncology Association

Background

Histopathological features after radical prostatectomy (RP) provide important information for the prognosis of prostate cancer (PCa). The possible correlations between Prostate-Imaging Reporting and Data Scoring System (PIRADS) scores in multiparametric magnetic resonance imaging (mpMRI) may also be predictive for prognosis. In this study, we aimed to evaluate the correlation of PIRADS scores with histopathological data.

Methods

A total of 177 patients who underwent preoperative mpMRI and RP for PCa from eight institutions were included in the study. Correlation of PIRADS score in preoperative mpMRI with adverse histopathological factors in RP specimen was investigated using univariate and multivariate analyses.

Results

The relationship between PIRADS score and postoperative extracapsular extension, lymphovascular invasion, and seminal vesicle involvement was significant (P < 0.001, P = 0.032, and P = 0.007, respectively). Although the PIRADS score was significantly correlated with the number of dissected lymph nodes (p = 0.026), it had no significant correlation with the number of positive nodes (P = 0.611). Total Gleason score, extracapsular extension, seminal vesicle invasion, and number of lymph nodes were found to be independent factors, which correlated with high PIRADS scores in ordinal logistic regression analysis.

Conclusion

PIRADS scoring system in mpMRI showed a statistically significant correlation with adverse histopathological factors in RP specimen. A higher PIRADS score may help to predict a higher Gleason score, indicating clinically important PCa as well as poor prognotic factors such as extracapsular extension, lymphovascular invasion, and seminal vesicle invasion that may indicate a higher risk of recurrence and the need for additional treatment.

Decreased accuracy of the prostate cancer EAU risk group classification in the era of imaging-guided diagnostic pathway: proposal for a new classification based on MRI-targeted biopsies and early oncologic outcomes after surgery

PURPOSE

To assess the performance of EAU risk classification in PCa patients according to the biopsy pathway (standard versus MRI guided) and to develop a new, more accurate, targeted biopsy (TB)-based classification.

MATERIALS AND METHODS

We included 1345 patients consecutively operated by radical prostatectomy (RP) since 2014, when MRI and TB were introduced in the diagnostic pathway. Patients underwent systematic biopsy (SB) only (n = 819) or SB and TB (n = 526) prior to RP during the same time period. Pathological and biochemical outcomes were compared between PCa men undergoing SB (SB cohort) and a combination of TB and SB (TB cohort). Kaplan-Meier and Cox regression models were used to assess biochemical recurrence-free survival (RFS).

RESULTS

Both cohorts were comparable regarding final pathology and RFS (p = 0.538). The EAU risk classification accurately predicted outcomes in SB cohort, but did not significantly separate low from intermediate risk in TB cohort (p = 0.791). In TB cohort, the new proposed three-group risk classification significantly improved the recurrence risk prediction compared with the EAU risk classification: HR 4 (versus HR 1.2, p = 0.009) for intermediate, and HR 15 (versus HR 6.5, p < 0.001) in high-risk groups, respectively. A fourth group defining very high-risk cases (≥ T2c clinical stage or grade group 5) was also proposed.

CONCLUSIONS

The new classification integrating TB findings we propose meaningfully improves the recurrence prediction after surgery in patients undergoing a TB-based diagnostic pathway, compared with standard EAU risk classification which is still relevant for patients undergoing only SB. External validation is needed.