The Key Combined Value of Multiparametric Magnetic Resonance Imaging, and Magnetic Resonance Imaging-targeted and Concomitant Systematic Biopsies for the Prediction of Adverse Pathological Features in Prostate Cancer Patients Undergoing Radical Prostatectomy

Imaging Assessment of Prostate Cancer Extra-prostatic Extension: From Histology to Controversies

Prostate cancer (PCa) is the most common non-skin malignancy among men and the fourth leading cause of cancer-related deaths globally. Accurate staging of PCa, particularly the assessment of extra-prostatic extension (EPE), is critical for prognosis and treatment planning. EPE, typically evaluated using magnetic resonance imaging (MRI), is associated with higher risks of positive surgical margins, biochemical recurrence, metastasis, and reduced overall survival. Despite the widespread use of MRI, there is no consensus on diagnosing EPE via imaging. There are 2 main scores assessing EPE by MRI: the European Society of Urogenital Radiology score and an MRI-based EPE grading system from an American group. While both are widely recognized, their differences can lead to varying interpretations in specific cases. This paper clarifies the anatomical considerations in diagnosing locally advanced PCa, explores EPE's impact on treatment and prognosis, and evaluates the relevance of MRI findings according to different criteria. Accurate EPE diagnosis remains challenging due to MRI limitations and inconsistencies in interpretation. Understanding these variations is crucial for optimal patient management.

Accuracy of the LaserSAFE technique for detecting positive surgical margins during robot-assisted radical prostatectomy: blind assessment and inter-rater agreement analysis

INTRODUCTION AND OBJECTIVES

Fluorescence confocal microscopy (FCM) is a new imaging modality capable of generating digital microscopic resolution scans of fresh surgical specimens, and holds potential as an alternative to frozen section (FS) analysis for intra-operative assessment of surgical margins. Previously, we described the LaserSAFE technique as an application of FCM for margin assessment in robot-assisted radical prostatectomy (RARP) using the Histolog® scanner. This study describes the accuracy and inter-rater agreement of FCM imaging compared to corresponding paraffin-embedded analysis (PA) among four blinded pathologists for the presence of positive surgical margins (PSM).

MATERIALS AND METHODS

RARP specimens from patients enrolled in the control arm of the NeuroSAFE PROOF study (NCT03317990) were analysed from April 2022 to February 2023. Prostate specimens were imaged using the Histolog® scanner before formalin fixation and PA. Four trained assessors, blinded to PA, reviewed and analysed FCM images of the posterolateral prostatic surface.

RESULTS

A total of 31 prostate specimens were included in the study. PA per lateral side of the prostate identified 11 instances of positive margins. Among the four histopathologists included in our study, FCM achieved a sensitivity of 73-91 and specificity of 94-100% for the presence of PSM. Fleiss' Kappa for inter-rater agreement on PSM was 0.78 (95% confidence interval = 0.64-0.92), indicating substantial agreement.

CONCLUSION

This blinded analysis of FCM versus PA among histopathologists with different experience levels demonstrated high accuracy and substantial inter-rater agreement for diagnosing PSM. This supports the role of the FCM as an alternative to FS.

Preoperative multiparametric magnetic resonance imaging based risk stratification system for predicting biochemical recurrence after radical prostatectomy

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) is used as a current marker in preoperative staging and surgical decision-making, but current evidence on predicting post-surgical oncological outcomes based on preoperative mpMRI findings is limited. In this study We aimed to develop a risk classification based on mpMRI and mpMRI-derived biopsy findings to predict early biochemical recurrence (BCR) after radical prostatectomy.

METHODS

Between January 2017 and January 2023, the data of 289 patients who underwent mpMRI, transrectal ultrasound-guided cognitive and fusion targeted biopsies, and subsequent radical prostatectomy (RP) with or without pelvic lymph node dissection in a single center were retrospectively re-evaluated. BCR was defined as a prostate specific-antigen (PSA) ≥ 0.2 ng/mL at least twice after RP. Multivariate logistic regression models tested the predictors of BCR. The regression tree analysis stratified patients into risk groups based on preoperative mpMRI characteristics. Receiver operating characteristic (ROC)-derived area under the curve (AUC) estimates were used to test the accuracy of the regression tree-derived risk stratification tool.

RESULTS

BCR was detected in 47 patients (16.2 %) at a median follow-up of 24 months. In mpMRI based multivariate analyses, the maximum diameter of the index lesion (HR 1.081, 95%Cl 1.015-1.151, p = 0.015) the presence of PI-RADS 5 lesions (HR 2.604, 95%Cl 1.043-6.493, p = 0.04), ≥iT3a stage (HR 2.403, 95%Cl 1.013-5.714, p = 0.046) and ISUP grade ≥4 on biopsy (HR 2.440, 95%Cl 1.123-5.301, p = 0.024) were independent predictors of BCR. In regression tree analysis, patients were stratified into three risk groups: maximum diameter of index lesion, biopsy ISUP grade, and clinical stage on mpMRI. The regression tree-derived risk stratification model had moderate-good accuracy in predicting early BCR (AUC 77 %) CONCLUSION: Straightforward mpMRI and mpMRI-derived biopsy-based risk stratification for BCR prediction provide an additional clinical predictive model to the currently available pathological risk tools.

Role of [18F]-PSMA-1007 PET radiomics for seminal vesicle invasion prediction in primary prostate cancer

PURPOSE

The purpose of this study is to investigate the diagnostic utility of [18F]-PSMA-1007 PET radiomics combined with machine learning methods to predict seminal vesicle invasion (SVI) after radical prostatectomy (RP) in prostate cancer (PCa) patients.

METHODS

This is a post hoc retrospective analysis for a prospective clinical trial that included a consecutive sample of PCa patients (n = 140) who had [18F]-PSMA-1007 PET/CT prior to RP. The intraprostatic lesion's volume of interest (VOI) was semi-automatically sketched using a threshold of 40 % maximum standardized uptake value (SUVmax), namely 40%SUVmax-VOI, and seminal vesicle glands were manually contoured, namely SV-VOI. Models were built using a variety of machine learning methods such as logistic regression, random forest, and support vector machine. The area under the receiver operating characteristic curve (AUC) was calculated for different models, and the prediction performances of radiomics models were compared against the radiologists' assessment. Kaplan-Meier analysis was utilized to assess the effectiveness of selected radiomics features to determine the progression-free survival (PFS) probability.

RESULTS

The training set had 112 patients and the test set had 28 patients. The highest AUC for the PET radiomics model of 40%SUVmax-VOI and the PET radiomics model of SV-VOI were 0.85 and 0.96 in the test set, respectively. The PET radiomics model of SV-VOI had a significantly higher AUC compared to the radiologists' assessment (P < 0.05). The Kaplan-Meier analysis showed that PET radiomics features were associated with PFS in patients with PCa.

CONCLUSION

Radiomics models developed by preoperative [18F]-PSMA-1007 PET were proven useful in predicting SVI, and PSMA PET radiomics features were correlated with PFS, suggesting that the PSMA PET radiomics might be an accurate tool for PCa characterization.

Clinical value of contralateral biopsies in men with unilateral MRI foci undergoing targeted biopsy

OBJECTIVES

To evaluate the additional prostate cancer detection yield and clinical implications of performing contralateral systematic biopsies in men with unilateral suspicious magnetic resonance imaging (MRI) findings undergoing MRI-guided transperineal (TP) biopsies in an outpatient clinic.

PATIENTS AND METHODS

A prospective study of 655 consecutive men with unilateral MRI suspicious findings undergoing office-based MRI-guided TP biopsies between May 2022 and December 2023. All men had pre-biopsy MRI followed by MRI-guided TP fusion biopsies with at least four targeted cores per lesion plus five contralateral systematic biopsies. The primary objective was the clinically significant prostate cancer (csPCa) detection rate of contralateral systematic biopsies in men with no or insignificant PCa on targeted biopsies. Secondary objectives included the impact of contralateral biopsies on PCa grade upgrading, additional insignificant PCa diagnoses, and the clinical implications of multifocal csPCa detected on both targeted and contralateral cores.

RESULTS

Any and csPCa (Gleason Grade Group [GG] ≥2) was detected in 564/655 (85%) and 471/655 (71%) men with a median age of 66 years and PSA level of 7.6 ng/mL. Overall, seven of 655 (1%) men had csPCa detected by contralateral systematic biopsies missed on MRI-targeted biopsy, all of whom had low-volume Gleason GG 2 PCa eligible for active surveillance. Furthermore, 70/464 (15%) men with csPCa on MRI-targeted biopsy had matching Gleason GG 2-5 PCa on contralateral biopsy, and another seven had higher Gleason GG. However, the additional information from contralateral biopsies did not seem to influence whole-gland treatment allocation and nerve-sparing during surgery.

CONCLUSION

Contralateral systematic biopsies in men with unilateral MRI findings undergoing MRI-guided TP targeted biopsies have limited value for csPCa detection and risk assessment for whole-gland treatment but may be important for determining PCa multifocality considering focal therapy eligibility.

Accuracy of MRI in detecting seminal vesicle invasion in prostate cancer: a systematic review and meta-analysis

OBJECTIVE

To determine the diagnostic test accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting seminal vesicle invasion (SVI).

METHODS

The Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed, the Excerpta Medica dataBASE (EMBASE) and Cochrane databases were search up to May 2023. We included studies that investigated the accuracy of mpMRI in detecting SVI when compared to radical prostatectomy specimens as the reference standard. Data extraction was performed by two independent reviewers to construct 2 × 2 tables, as well as patient and study characteristics. The methodological quality of the included studies was assessed with the Quality of Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled and presented graphically with summary receiver operator characteristic (SROC) plots.

RESULTS

A total of 27 articles with 4862 patients were included for analysis. The summary sensitivity and specificity were 0.57 (95% confidence interval [CI] 0.45-0.68) and 0.95 (95% CI 0.92-0.99), respectively. Meta-regression indicated that there was no evidence that coil strength (P = 0.079), coil type (P = 0.589), year of publication (P = 0.503) or use of the Prostate Imaging-Reporting and Data System (P = 0.873) significantly influenced these results. The summary diagnostic odds ratio was 28.3 (95% CI 15.0-48.8) and the area under the curve for the SROC curve was 0.87. The I2 statistic was a modest 11.9%. In general, methodological quality was good.

CONCLUSION

The use of mpMRI in detecting SVI has excellent specificity but poor sensitivity. Both endorectal coils and magnetic field strength do not significantly impact the accuracy of MRI. These findings suggest that mpMRI cannot reliably rule out SVI in patients with prostate cancer.

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.

Utility of positive core number on MRI-ultrasound fusion targeted biopsy in combination with PI-RADS scores for predicting unexpected extracapsular extension of clinically localized prostate cancer

OBJECTIVES

To evaluate the utility of magnetic resonance imaging (MRI) and MRI-ultrasound fusion targeted biopsy (TB) for predicting unexpected extracapsular extension (ECE) in clinically localized prostate cancer (CLPC).

METHODS

This study enrolled 89 prostate cancer patients with one or more lesions showing a Prostate Imaging-Reporting and Data System (PI-RADS) score ≥3 but without morphological abnormality in the prostatic capsule on pre-biopsy MRI. All patients underwent TB and systematic biopsy followed by radical prostatectomy (RP). Each lesion was examined by 3-core TB, taking cores from each third of the lesion. The preoperative variables predictive of ECE were explored by referring to RP specimens in the lesion-based analysis.

RESULTS

Overall, 186 lesions, including 81 (43.5%), 73 (39.2%), and 32 (17.2%) with PI-RADS 3, 4, and 5, respectively, were analyzed. One hundred and twenty-two lesions (65.6%) were diagnosed as cancer on TB, and ECE was identified in 33 (17.7%) on the RP specimens. The positive TB core number was ≤2 in 129 lesions (69.4%) and three in 57 lesions (30.6%). On the multivariate analysis, PI-RADS ≥4 (p = 0.049, odds ratio [OR] = 2.39) and three positive cores on TB (p = 0.005, OR = 3.07) were independent predictors of ECE. Lesions with PI-RADS ≥4 and a positive TB core number of 3 had a significantly higher rate of ECE than those with PI-RADS 3 and a positive TB core number ≤2 (37.5% vs. 7.8%, p < 0.001).

CONCLUSIONS

Positive TB core number in combination with PI-RADS scores is helpful to predict unexpected ECE in CLPC.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study

BACKGROUND

Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence.

PURPOSE

To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI).

STUDY TYPE

Retrospective.

POPULATION

Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution).

FIELD STRENGTH/SEQUENCES

T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T.

ASSESSMENT

The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter.

STATISTICAL TESTS

Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant.

RESULTS

The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]).

DATA CONCLUSION

TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 4.

Development of a microultrasound-based nomogram to predict extra-prostatic extension in patients with prostate cancer undergoing robot-assisted radical prostatectomy

OBJECTIVES

To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence of extra-prostatic extension and guide the grade of nerve-sparing.

MATERIAL AND METHODS

All patients underwent microultrasound the day before robot-assisted radical prostatectomy. Variables significantly associated with extra-prostatic extension at univariable analysis were used to build the multivariable logistic model, and the regression coefficients were used to develop the nomogram. The model was subjected to 1000 bootstrap resamples for internal validation. The performance of the microultrasound-based model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).

RESULTS

Overall, 122/295 (41.4%) patients had a diagnosis of extra-prostatic extension on definitive pathology. Microultrasound correctly identify extra-prostatic extension in 84/122 (68.9%) cases showing a sensitivity and a specificity of 68.9% and 84.4%, with an AUC of 76.6%. After 1000 bootstrap resamples, the predictive accuracy of the microultrasound-based model was 85.9%. The calibration plot showed a satisfactory concordance between predicted probabilities and observed frequencies of extra-prostatic extension. The DCA showed a higher clinical net-benefit compared to the model including only clinical parameters. Considering a 4% cut-off, nerve-sparing was recommended in 173 (58.6%) patients and extra-prostatic extension was detected in 32 (18.5%) of them.

CONCLUSION

We developed a microultrasound-based nomogram for the prediction of extra-prostatic extension that could aid in the decision whether to preserve or not neurovascular bundles. External validation and a direct comparison with mpMRI-based nomogram is crucial to corroborate our results.

Gleason score 3+3=6 prostatic adenocarcinoma is not benign and the current debate is unhelpful to clinicians and patients

Prostate adenocarcinoma is a common malignancy associated with a significant morbidity and mortality. In both prostate biopsies and radical prostatectomy specimens Gleason scoring informs both treatment and outcome prediction. The current convention is that in needle biopsies, Gleason patterns 3, 4 and 5 are considered to be malignant. Despite this there is debate as to whether or not Gleason score (GS) 3+3=6 should be diagnosed as cancer due to potential over-treatment and the psychological impact on patients. It is apparent that GS 3+3=6 is indolent disease with a low risk of metastasis. However, it does have the histological features of malignancy and is capable of infiltrating the prostate gland, extraprostatic extension, and metastatic spread. Furthermore GS 3+3=6 carcinoma has immunohistochemical and molecular genetic features similar to those of higher grade prostatic carcinoma. If GS 3+3=6 tumour is considered benign, the question arises should a benign label be given to the Gleason pattern 3 component of tumour that includes Gleason patterns of higher grade? This would seem a logical step as GS 3+3=6 cancers and the pattern 3 component in cancers with multiple patterns are morphologically identical. If pattern 3 is considered to be benign, then Gleason scoring would be limited to 4+4=8, 4+5=9, 5+4=9 and 5+5=10 which is clearly inappropriate. The correct strategy to address potential over-treatment of patients with low-grade cancer is clinician and patient education, not the recalibration of Gleason grading to reclassify malignant tumours as benign.

The Role of Multiparametric MRI (mpMRI) in the Prediction of Adverse Prostate Cancer Pathology in Radical Prostatectomy Specimen

INTRODUCTION

Prostate cancer (PCa) risk stratification is essential in guiding therapeutic decision. Multiparametric magnetic resonance tomography (mpMRI) holds promise in the prediction of adverse pathologies (AP) after prostatectomy (RP). This study aims to identify clinical and imaging markers in the prediction of adverse pathology.

METHODS

Patients with PCa, diagnosed by targeted biopsy after mpMRI and undergoing RP, were included. The predictive accuracy of mpMRI for extraprostatic extension (ECE), seminal vesicle infiltration (SVI), and lymph node positivity was calculated from the final histopathology.

RESULTS

846 patients were involved. Independent risk parameters include imaging findings such as ECE (OR 3.12), SVI (OR 2.55), and PI-RADS scoring (4: OR 2.01 and 5: OR 4.34). mpMRI parameters such as ECE, SVI, and lymph node metastases showed a high prognostic accuracy (73.28% vs. 95.35% vs. 93.38%) with moderate sensitivity compared to the final histopathology. The ROC analysis of our combined scoring system (D'Amico classification, PSA density, and MRI risk factors) improves the prediction of adverse pathology (AUC: 0.73 vs. 0.69).

CONCLUSION

Our study supports the use of mpMRI for comprehensive pretreatment risk assessment in PCa. Due to the high accuracy of factors like ECE, SVI, and PI-RADS scoring, utilizing mpMRI data enabled accurate prediction of unfavorable pathology after RP.

Defining the role of multiparametric MRI in predicting prostate cancer extracapsular extension

OBJECTIVES

To identify the predictive factors of prostate cancer extracapsular extension (ECE) in an institutional cohort of patients who underwent multiparametric MRI of the prostate prior to radical prostatectomy (RP).

PATIENTS AND METHODS

Overall, 126 patients met the selection criteria, and their medical records were retrospectively collected and analysed; 2 experienced radiologists reviewed the imaging studies. Logistic regression analysis was conducted to identify the variables associated to ECE at whole-mount histology of RP specimens; according to the statistically significant variables associated, a predictive model was developed and calibrated with the Hosmer-Lomeshow test.

RESULTS

The predictive ability to detect ECE with the generated model was 81.4% by including the length of capsular involvement (LCI) and intraprostatic perineural invasion (IPNI). The predictive accuracy of the model at the ROC curve analysis showed an area under the curve (AUC) of 0.83 [95% CI (0.76-0.90)], p < 0.001. Concordance between radiologists was substantial in all parameters examined (p < 0.001). Limitations include the retrospective design, limited number of cases, and MRI images reassessment according to PI-RADS v2.0.

CONCLUSION

The LCI is the most robust MRI factor associated to ECE; in our series, we found a strong predictive accuracy when combined in a model with the IPNI presence. This outcome may prompt a change in the definition of PI-RADS score 5.

MRI characteristics and oncological follow-up of patients with ISUP grade group 4 or 5 prostate cancer

OBJECTIVES

To analyze multiparametric MRI (mpMRI) characteristics of patients with International Society of Urological Pathology (ISUP) grade group (GG) 4 or 5 prostate cancer (PC) and to correlate MRI parameters with the occurrence of biochemical recurrence (BCR) after radical prostatectomy (RPE).

METHODS

In this single-center cohort study consecutive patients with mpMRI and ISUP GG 4 or 5 PC were retrospectively analyzed. Clinical, MR-guided biopsy, and diagnostic mpMRI parameter were assessed. A subcohort of patients with RPE and follow-up was analyzed separately. A univariate and multivariate analyses were performed to determine parameters that are associated to patients with BCR after RPE.

RESULTS

145 patients (mean age 70y, median PSA 10.9 ng/ml) were analyzed. 99% had a PI-RADS classification of 4 or 5, 48% revealed MRI T3 stage, and median diameter of the MRI index lesion (IL) was 15 mm. IL showed a median ADC value of 668 ×10-6 mm2/s and exhibited contrast enhancement in 94% of the cases. For patients with follow-up after RPE (n = 82; mean follow-up time 68 ± 27 m), MRI parameters were significantly different for contact length of the IL to the pseudocapsule (LCC), MRI T3 stage, and IL localization (p < 0.05). Higher PSAD and MRI T3 stage were independent parameters for the risk of BCR when incorporating clinical, biopsy, and MRI parameters.

CONCLUSION

ISUP GG 4 or 5 PC has distinctive characteristics on mpMRI and were detected on MRI in all cases. In addition, higher PSAD and MRI T3 stage were significant predictors for BCR after RPE.

Prostate Cancer Local Staging with Magnetic Resonance Imaging

Accurate determination of the local stage of prostate cancer is crucial for treatment planning and prognosis. The primary objective of local staging is to distinguish between organ-confined and locally advanced disease, with the latter carrying a worse clinical prognosis. The presence of locally advanced disease features of prostate cancer, such as extra-prostatic extension, seminal vesicle invasion, and positive surgical margin, can impact the choice of treatment. Over the past decade, multiparametric MRI (mpMRI) has become the preferred imaging modality for the local staging of prostate cancer and has been shown to provide accurate information on the location and extent of disease. It has demonstrated superior performance compared to staging based on traditional clinical nomograms. Despite being a relatively new technique, mpMRI has garnered considerable attention and ongoing investigations. Therefore, in this review, we will discuss the current use of mpMRI on prostate cancer local staging.

Use of PIRADS 2.1 to predict capsular invasion in patients with radiologic T3a prostate cancer

Objective

Using multi-parametric magnetic resonance imaging (mpMRI) to identify patients with clinical T3a (cT3a) who were overestimated on mpMRI with final pathological T2 (pT2). To suggest that the neurovascular bundle (NVB) can be preserved by evaluating the characteristics of patients according to their pathological grade among cT3a prostate cancer (PCa) patients using mpMRI.

Materials and methods

Patients who underwent robot-assisted laparoscopic radical prostatectomy (RALP) were retrospectively analyzed and those patients with clinical T3aN0M0 were enrolled. These enrolled patients were divided into a localized cancer group with pT2 PCa and a locally advanced group with pT3a or higher. Factors affecting the diagnosis of localized PCa after RALP in patients with cT3a PCa were evaluated.

Results

Among the preoperative parameters of patients with cT3a PCa, the prostate specific antigen density (PSAD) (OR: 3.76, 95% CI: 1.85-7.64, p<0.001), international society of urological pathology (ISUP) grade (p<0.05), and index lesion size (OR: 1.44, 95% CI: 1.85-7.64, p<0.001) were significantly associated with pathological locally advanced PCa. Optimal cut-off values for prediction of pT3a or higher were 0.36 ng/mL2 for PSAD (sensitivity: 55.7%, specificity: 70.8%), 1.77 cm for index lesion size (sensitivity: 54.3%, specificity: 66.0%), and 2.5 for ISUP grading (sensitivity: 67.6%, specificity: 53.2%). For prediction of pT3a or higher among patients with cT3a PCa, a nomogram was developed using ISUP grade, index lesion size, and PSAD on prostate biopsy (area under the curve: 0.71, 95% CI: 0.670-0.754, p<0.001). PSAD less than 0.36 index lesion size less than 1.77 cm, and biopsy ISUP grade 1-2 are highly likely to indicate that there is no actual extracapsular extension in cT3a PCa patients.

Conclusions

PSAD, ISUP, and index lesion size showed significant associations with the classification of pathologic localized and locally advanced PCa in patients with cT3a PCa. A nomogram including these features can predict the diagnosis of locally advanced PCa in patients with cT3a PCa.

Development and Validation of Nomograms Based on Nutritional Risk Index for Predicting Extracapsular Extension and Seminal Vesicle Invasion in Patients Undergoing Radical Prostatectomy

Background

The aim of the study was to investigate the predictive value of the nutritional risk index (NRI) for extracapsular extension (ECE) and seminal vesicle invasion (SVI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP), and further develop and validate predictive nomograms for ECE and SVI based on the NRI.

Methods

We retrospectively analyzed 734 PCa patients who underwent RP between 2010 and 2020 in the Department of Urology at Peking University Third Hospital. The enrolled patients were randomly divided into a primary cohort (n = 489) and a validation cohort (n = 245) in a 2:1 manner. The baseline NRI of patients was calculated using serum albumin level and body mass index, and a malnutrition status was defined as NRI ≤ 98. Univariate and multivariate logistic regression analyses were conducted to identify predictors for ECE and SVI. Nomograms for predicting ECE and SVI were established based on the results of the multivariate logistic regression analysis. The performance of the nomograms was estimated using Harrell's concordance index (C-index), the area under curve (AUC) of receiver operating characteristic (ROC) curves and the calibration curves.

Results

In the primary cohort, 70 (14.3%) patients with NRI ≤ 98 were classified as malnutrition, while the remaining 419 (85.7%) patients with NRI > 98 were considered to have normal nutrition. The nomograms for predicting ECE and SVI shared common factors including NRI, percentage of positive biopsy cores (PPC) and biopsy Gleason score, while prostate-specific antigen (PSA) levels and PSA density (PSAD) were only incorporated in ECE nomogram. The C-indexes of the nomograms for predicting ECE and SVI were 0.785 (95% confidence interval (CI): 0.745 - 0.826) and 0.852 (95% CI: 0.806 - 0.898), respectively. The calibration curves demonstrated excellent agreement between the predictions by the nomograms and the actual observations. The results remained reproducible when the nomograms were applied to the validation cohort.

Conclusions

The NRI is significantly associated with ECE and SVI in PCa patients. The nomogram established based on the NRI in our study can provide individualized risk estimation for ECE and SVI in PCa patients, and may be valuable for clinicians in making well-informed decisions regarding treatment strategies and patient management.

A Novel Nomogram to Identify Candidates for Focal Therapy Among Patients with Localized Prostate Cancer Diagnosed via Magnetic Resonance Imaging-Targeted and Systematic Biopsies: A European Multicenter Study

BACKGROUND

Suitable selection criteria for focal therapy (FT) are crucial to achieve success in localized prostate cancer (PCa).

OBJECTIVE

To develop a multivariable model that better delineates eligibility for FT and reduces undertreatment by predicting unfavorable disease at radical prostatectomy (RP).

DESIGN, SETTING, AND PARTICIPANTS

Data were retrospectively collected from a prospective European multicenter cohort of 767 patients who underwent magnetic resonance imaging (MRI)-targeted and systematic biopsies followed by RP in eight referral centers between 2016 and 2021. The Imperial College of London eligibility criteria for FT were applied: (1) unifocal MRI lesion with Prostate Imaging-Reporting and Data System score of 3-5; (2) prostate-specific antigen (PSA) ≤20 ng/ml; (3) cT2-3a stage on MRI; and (4) International Society of Urological Pathology grade group (GG) 1 and ≥6 mm or GG 2-3. A total of 334 patients were included in the final analysis.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome was unfavorable disease at RP, defined as GG ≥4, and/or lymph node invasion, and/or seminal vesicle invasion, and/or contralateral clinically significant PCa. Logistic regression was used to assess predictors of unfavorable disease. The performance of the models including clinical, MRI, and biopsy information was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis. A coefficient-based nomogram was developed and internally validated.

RESULTS AND LIMITATIONS

Overall, 43 patients (13%) had unfavorable disease on RP pathology. The model including PSA, clinical stage on digital rectal examination, and maximum lesion diameter on MRI had an AUC of 73% on internal validation and formed the basis of the nomogram. Addition of other MRI or biopsy information did not significantly improve the model performance. Using a cutoff of 25%, the proportion of patients eligible for FT was 89% at the cost of missing 30 patients (10%) with unfavorable disease. External validation is required before the nomogram can be used in clinical practice.

CONCLUSIONS

We report the first nomogram that improves selection criteria for FT and limits the risk of undertreatment.

PATIENT SUMMARY

We conducted a study to develop a better way of selecting patients for focal therapy for localized prostate cancer. A novel predictive tool was developed using the prostate-specific antigen (PSA) level measured before biopsy, tumor stage assessed via digital rectal examination, and lesion size on magnetic resonance imaging (MRI) scans. This tool improves the prediction of unfavorable disease and may reduce the risk of undertreatment of localized prostate cancer when using focal therapy.

The role of nuclear medicine tracers for prostate cancer surgery: from preoperative to intraoperative setting

PURPOSE OF REVIEW

There has been a growing interest in the use of novel molecular imaging modalities for the management of prostate cancer (PCa), spanning from diagnostic to therapeutic settings. The aim of this review is to provide a comprehensive overview of recently published studies investigating the use of novel nuclear medicine tracers across different stages of PCa management.

RECENT FINDINGS

Emerging evidence supports the use of molecular imaging for preoperative staging of PCa, where prostate-specific membrane antigen (PSMA) PET has shown superior accuracy compared to conventional imaging for the detection of nodal and distant metastases, which needs to be translated to new risk stratification. A role for PSMA PET has been proposed for PCa diagnosis, with local activity associated with histology. Surgical guidance, using either visual feedback or gamma-ray detectors to identify tissues with accumulated radio-labeled tracers, may improve the ability to resect locoregional diseases and thus maximize oncological control. PSMA targeted therapy (Lu-PSMA) has been mainly investigated in the castration-resistant setting, but might have a role in earlier settings such as neoadjuvant treatment.

SUMMARY

Novel molecular imaging using PSMA-based tracers could significantly improve PCa management in the diagnosis, staging, and intraoperative guidance settings, potentially leading to personalized and effective treatment decisions.

Multiparametric MRI-based 5-year Risk Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy

Background Current predictive tools to estimate the risk of biochemical recurrence (BCR) after treatment of prostate cancer do not consider multiparametric MRI (mpMRI) information. Purpose To develop a risk prediction tool that considers mpMRI findings to assess the risk of 5-year BCR after radical prostatectomy. Materials and Methods In this retrospective single-center analysis in 1459 patients with prostate cancer who underwent mpMRI before radical prostatectomy (in 2012-2015), the outcome of interest was 5-year BCR (two consecutive prostate-specific antigen [PSA] levels > 0.2 ng/mL [0.2 µg/L]). Patients were randomly divided into training (70%) and test (30%) sets. Kaplan-Meier plots were applied to the training set to estimate survival probabilities. Multivariable Cox regression models were used to test the relationship between BCR and different sets of exploratory variables. The C-index of the final model was calculated for the training and test sets and was compared with European Association of Urology, University of California San Francisco Cancer of the Prostate Risk Assessment, Memorial Sloan-Kettering Cancer Center, and Partin risk tools using the partial likelihood ratio test. Five risk categories were created. Results The median duration of follow-up in the whole cohort was 59 months (IQR, 32-81 months); 376 of 1459 (25.8%) patients had BCR. A multivariable Cox regression model (referred to as PIPEN, and composed of PSA density, International Society of Urological Pathology grade group, Prostate Imaging Reporting and Data System category, European Society of Urogenital Radiology extraprostatic extension score, nodes) fitted to the training data yielded a C-index of 0.74, superior to that of other predictive tools (C-index 0.70 for all models; P ≤ .01) and a median higher C-index on 500 test set replications (C-index, 0.73). Five PIPEN risk categories were identified with 5-year BCR-free survival rates of 92%, 84%, 71%, 56%, and 26% in very low-, low-, intermediate-, high-, and very high-risk patients, respectively (all P < .001). Conclusion A five-item model for predicting the risk of 5-year BCR after radical prostatectomy for prostate cancer was developed and internally verified, and five risk categories were identified. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Aguirre and Ortegón in this issue.

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.

Advances in optical molecular imaging for neural visualization

Iatrogenic nerve injury is a significant complication in surgery, which can negatively impact patients' quality of life. Currently, the main clinical neuroimaging methods, such as computed tomography, magnetic resonance imaging, and high-resolution ultrasonography, do not offer precise real-time positioning images for doctors during surgery. The clinical application of optical molecular imaging technology has led to the emergence of new concepts such as optical molecular imaging surgery, targeted surgery, and molecular-guided surgery. These advancements have made it possible to directly visualize surgical target areas, thereby providing a novel method for real-time identification of nerves during surgery planning. Unlike traditional white light imaging, optical molecular imaging technology enables precise positioning and identifies the cation of intraoperative nerves through the presentation of color images. Although a large number of experiments and data support its development, there are few reports on its actual clinical application. This paper summarizes the research results of optical molecular imaging technology and its ability to realize neural visualization. Additionally, it discusses the challenges neural visualization recognition faces and future development opportunities.

Advances in high-risk localized prostate cancer: Staging and management

Nearly 15% of individuals with localized prostate cancer are identified as high risk for recurrence and progression of the disease, which is why the correct staging is vital for the definition of correct treatment-also developing novel therapeutic strategies to find a balance between getting better outcomes without sacrificing the quality of life (QoL). In this narrative review, we introduced the current standards of staging and primary treatment of high-risk localized prostate cancer (PCa), based on international guidelines and arguments in the debate, under the light of the most recent literature. It brings essential tools such as PSMA PET/CT and different nomograms (Briganti. MSKCC, Gandaglia) for accurate staging and selecting wisely the definitive therapy. Even though there is a broad discussion over the best local treatment in curative-intent treatment, it looks more important to define which patient profile would adapt correctly to every different treatment, highlighting the benefits and superior outcomes with multimodal treatment.

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.

Impact of Magnetic Resonance Imaging Targeting on Pathologic Upgrading and Downgrading at Prostatectomy: A Systematic Review and Meta-analysis

CONTEXT

The evidence supporting multiparametric magnetic resonance imaging (MRI) targeting for biopsy is nearly exclusively based on biopsy pathologic outcomes. This is problematic, as targeting likely allows preferential identification of small high-grade areas of questionable oncologic significance, raising the likelihood of overdiagnosis and overtreatment.

OBJECTIVE

To estimate the impact of MRI-targeted, systematic, and combined biopsies on radical prostatectomy (RP) grade group concordance.

EVIDENCE ACQUISITION

PubMed MEDLINE and Cochrane Library were searched from July 2018 to January 2022. Studies that conducted systematic and MRI-targeted prostate biopsies and compared biopsy results with pathology after RP were included. We performed a meta-analysis to assess whether pathologic upgrading and downgrading were influenced by biopsy type and a net-benefit analysis using pooled risk difference estimates.

EVIDENCE SYNTHESIS

Both targeted only and combined biopsies were less likely to result in upgrading (odds ratio [OR] vs systematic of 0.70, 95% confidence interval [CI] 0.63-0.77, p < 0.001, and 0.50, 95% CI 0.45-0.55, p < 0.001), respectively). Targeted only and combined biopsies increased the odds of downgrading (1.24 (95% CI 1.05-1.46), p = 0.012, and 1.96 (95% CI 1.68-2.27, p < 0.001) compared with systematic biopsies, respectively. The net benefit of targeted and combined biopsies is 8 and 7 per 100 if harms of up- and downgrading are considered equal, but 7 and -1 per 100 if the harm of downgrading is considered twice that of upgrading.

CONCLUSIONS

The addition of MRI-targeting results in lower rates of upgrading as compared to systematic biopsy at RP (27% vs 42%). However, combined MRI-targeted and systematic biopsies are associated with more downgrading at RP (19% v 11% for combined vs systematic). Strong heterogeneity suggests further research into factors that influence the rates of up- and downgrading and that distinguishes clinically relevant from irrelevant grade changes is needed. Until then, the benefits and harms of combined MRI-targeted and systematic biopsies cannot be fully assessed.

PATIENT SUMMARY

We reviewed the ability of magnetic resonance imaging (MRI)-targeted biopsies to predict cancer grade at prostatectomy. We found that combined MRI-targeted and systematic biopsies result in more cancers being downgraded than systematic biopsies.

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade

BACKGROUND

To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa).

METHODS

This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms.

RESULTS

PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction.

CONCLUSIONS

PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.

Added value of shear-wave elastography in the prediction of extracapsular extension and seminal vesicle invasion before radical prostatectomy

The purpose of this study was to analyze the value of transrectal shear-wave elastography (SWE) in combination with multivariable tools for predicting adverse pathological features before radical prostatectomy (RP). Preoperative clinicopathological variables, multiparametric magnetic resonance imaging (mp-MRI) manifestations, and the maximum elastic value of the prostate (Emax) on SWE were retrospectively collected. The accuracy of SWE for predicting adverse pathological features was evaluated based on postoperative pathology, and parameters with statistical significance were selected. The diagnostic performance of various models, including preoperative clinicopathological variables (model 1), preoperative clinicopathological variables + mp-MRI (model 2), and preoperative clinicopathological variables + mp-MRI + SWE (model 3), was evaluated with area under the receiver operator characteristic curve (AUC) analysis. Emax was significantly higher in prostate cancer with extracapsular extension (ECE) or seminal vesicle invasion (SVI) with both P < 0.001. The optimal cutoff Emax values for ECE and SVI were 60.45 kPa and 81.55 kPa, respectively. Inclusion of mp-MRI and SWE improved discrimination by clinical models for ECE (model 2 vs model 1, P = 0.031; model 3 vs model 1, P = 0.002; model 3 vs model 2, P = 0.018) and SVI (model 2 vs model 1, P = 0.147; model 3 vs model 1, P = 0.037; model 3 vs model 2, P = 0.134). SWE is valuable for identifying patients at high risk of adverse pathology.

Micro-ultrasound-guided biopsies versus systematic biopsies in the detection of prostate cancer: a systematic review and meta-analysis

PURPOSE

The diagnosis of prostate cancer (PCa) still relies on the performance of both targeted (TB) and systematic biopsies (SB). Micro-ultrasound (mUS)-guided biopsies demonstrated a high sensitivity in detecting clinically significant prostate cancer (csPCa), which could be comparable to that of magnetic resonance imaging (MRI)-TB, but their added value has not been compared to SB yet.

METHODS

We conducted a systematic review and meta-analysis, based on Medline, EMBASE, Scopus, and Web of Science, in accordance with PRISMA guidelines, to compare mUS-guided biopsies to SB.

RESULTS

Based on the literature search of 2957 articles, 15 met the inclusion criteria (2967 patients). Most patients underwent mUS-guided biopsies, followed by MRI-TB and SB. Respectively 5 (n = 670) and 4 (n = 467) studies, providing raw data on SB, were included in a random-effect meta-analysis of the detection rate of csPCa, i.e. Gleason Grade Group (GGG) ≥ 2 or non-csPCa (GGG = 1). Overall, PCa was detected in 56-71% of men, with 31.3-49% having csPCa and 17-25.4% having non-csPCa. Regarding csPCa, mUS-guided biopsies identified 196 and SB 169 cases (Detection Ratio (DR): 1.18, 95% CI 0.83-1.68, I2 = 69%), favoring mUS-guided biopsies; regarding non-csPCa, mUS-guided biopsies identified 62 and SB 115 cases (DR: 0.55, 95% CI 0.41-0.73, I2 = 0%), also favoring mUS-guided biopsies by decreasing unnecessary diagnosis.

CONCLUSION

Micro-ultrasound-guided biopsies compared favorably with SB for the detection of csPCa and detected fewer non-csPCa than SB. Prospective trials are awaited to confirm the interest of adding mUS-guided biopsies to MRI-TB to optimize csPCa detection without increasing overdiagnosis of non-csPCa.

MRI versus non-MRI diagnostic pathways before radical prostatectomy: Impact on nerve-sparing, positive surgical margins, and biochemical recurrence

PURPOSE

Magnetic resonance imaging (MRI) prior to biopsy has improved detection of clinically significant prostate cancer (CaP), but its impact on surgical outcomes is less well established. We compared MRI vs. non-MRI diagnostic pathways among patients receiving radical prostatectomy (RP) for impact on surgical outcomes.

MATERIALS AND METHODS

Men diagnosed with CaP and receiving RP at Loyola University Medical Center (2014-2021) were categorized into MRI or non-MRI diagnostic pathways based on receipt of MRI before prostate biopsy. Primary outcomes of interest included positive surgical margin (PSM) rates, the performance of bilateral nerve-sparing, and biochemical recurrence (BCR). Multivariable logistic regression models, Kaplan-Meier curves, and Cox proportional hazards regression were employed.

RESULTS

Of 609 patients, 281 (46.1%) were in the MRI and 328 (53.9%) in the non-MRI groups. MRI patients had similar PSA, biopsy grade group (GG) distribution, RP GG, pT stage, and RP CaP volume compared to non-MRI patients. PSM rates were not statistically different for the MRI vs. non-MRI groups (22.8% vs. 26.8%, P = 0.25). Bilateral nerve-sparing rates were higher for the MRI vs. non-MRI groups (OR 1.95 (95%CI 1.32-2.88), P = 0.001). The MRI group demonstrated improved BCR (HR 0.64 (95%CI 0.41-0.99), P = 0.04) after adjustment for age, PSA, RP GG, pT, pN, and PSM status. On meta-analysis, a 5.2% PSM reduction was observed but high heterogeneity for use of nerve-sparing.

CONCLUSIONS

An MRI-based diagnostic approach selected patients for RP with a small reduction in PSM rates, greater utilization of bilateral nerve-sparing, and improved cancer control by BCR compared to a non-MRI approach even after adjustment for known prognostic factors.

Site-specific concordance of targeted and systematic biopsy cores at the index lesion on multiparametric magnetic resonance: can we spare the double-tap?

PURPOSE

To define the impact of systematic biopsy (SB) cores directed in the same area of index lesion in patients undergoing targeted biopsy (TB) and SB for prostate cancer (PCa) suspicion.

METHODS

We retrospectively analyzed data of biopsy-naïve patients with one single suspicious lesion at mpMRI who underwent TB plus SB at our institution between January 2015 and September 2021. A convenient sample of 336 patients was available for our analyses. The primary outcome was to evaluate the impact of overlapping SB cores directed to the index lesion at mpMRI. The secondary outcome was to evaluate the SB cores concordance in terms of highest Gleason Score Detection with TB cores.

RESULTS

56% of patients were found to have site-specific concordance. SB cores determined disease upgrade in 22.1% patients. Thirty-one (16.4%) site-concordant patients experienced upgrade through overlapping SB cores, while 149 (79.3%) had no benefit by SB cores, and 8 (4.3%) patients had the worst ISUP at TB cores. 50% of the patients with negative-TB were upgraded to insignificant PCa, and 17.5% was upgraded from negative to unfavorable-intermediate- or high-risk PCa. Overall, 14 (19.4%) patients were also upgraded from ISUP 1 on TB to csPCa, with 28.5% of these harboring high-risk PCa. In csPCas at TB, 9 (12.5%) patients were upgraded from intermediate- to high-risk disease by SB.

CONCLUSIONS

TB alone consents to identify worst ISUP PCa in vast majority of patients scheduled for biopsy. A non-negligible number of patients are upgraded via-SB cores, including also index lesion overlapping cores. Omitting these cores might lead to a suboptimal patient management.

Predicting the probability of pT3 or higher pathological stage at radical prostatectomy: COVID19-specific considerations

Background

We tested whether a model identifying prostate cancer (PCa) patients at risk of pT3-4/pN1 can be developed for use during COVID19 pandemic, in order to guarantee appropriate treatment to patients harboring advanced disease patients without compromising sustainability of care delivery.

Methods

Within the Surveillance, Epidemiology and End Results database 2010-2016, we identified 27,529 patients with localized PCa and treated with radical prostatectomy. A multivariable logistic regression model predicting presence of pT3-4/pN1 disease was fitted within a development cohort (n=13,977, 50.8%). Subsequently, external validation (n=13,552, 49.2%) and head-to-head comparison with NCCN risk group stratification was performed.

Results

In model development, age, PSA, biopsy Gleason Grade Group (GGG) and percentage of positive biopsy cores were independent predictors of pT3-4/pN1 stage. In external validation, prediction of pT3-4/pN1 with novel nomogram was 74% accurate versus 68% for NCCN risk group stratification. Nomogram achieved better calibration and showed net-benefit over NCCN risk group stratification in decision curve analyses. The use of nomogram cut-off of 49% resulted in pT3-4/pN1 rate of 65%, instead of the average 35%.

Conclusion

The newly developed, externally validated nomogram predicts presence of pT3-4/pN1 better than NCCN risk group stratification and allows to focus radical prostatectomy treatment on individuals at highest risk of pT3-4/pN1.

Diagnosis and Pathologic Reporting of Prostate Cancer in the Era of MRI-Targeted Prostate Biopsy

Historically, the detection of prostate cancer relied upon a systematic yet random sampling of the prostate by transrectal ultrasound guided biopsy. This approach was a nontargeted technique that led to the under detection of cancers at biopsy and the upgrading of cancers at radical prostatectomy. Multiparametric MRI-targeted prostate biopsy allows for an image-directed approach to the identification of prostate cancer. MRI-targeted biopsy of the prostate is superior for the detection of clinically significant prostate cancer. As this technique has become more prevalent among urologists, pathologists need to recognize how this development impacts cancer diagnosis and reporting.

Prostate biopsy in the era of MRI-targeting: towards a judicious use of additional systematic biopsy

OBJECTIVES

We aimed to develop and compare strategies that help optimize current prostate biopsy practice by identifying patients who may forgo concurrent systematic biopsy (SBx) in favor of MRI-targeted (TBx) alone.

METHODS

Retrospective study on 745 patients who underwent combined MRI-TBx plus SBx. Primary outcome was the upgrade to clinically significant prostate cancer (csPCa; grade group ≥ 2) on SBx versus MRI-TBx. Variables (age, previous biopsy status, Prostate Imaging Reporting and Data System (PI-RADS) score, index lesion size/location, number of lesions, PSA, PSA density, prostate volume) associated with the primary outcome were identified by logistic regression and used for biopsy strategies. Clinical utility was assessed by decision curve analysis (DCA).

RESULTS

SBx detected 47 (6%) additional men with csPCa. The risk of detecting csPCa uniquely on SBx was significantly lower in men with PI-RADS 5 (versus PI-RADS 3: OR 0.30, p = 0.03; versus PI-RADS 4: OR 0.33, p = 0.01), and previous negative biopsy (versus previous positive biopsy: OR 0.40, p = 0.007), and increased with age (per 10 years: OR 1.64, p = 0.016). No significant association was observed for other variables. DCA identified the following strategies as most useful: (a) avoid SBx in men with PI-RADS 5 and (b) additionally in those with previous negative biopsy, resulting in avoiding SBx in 201 (27%) and 429 (58%), while missing csPCa in 5 (1%) and 15 (2%) patients, respectively.

CONCLUSION

Not all men benefit equally from the combination of SBx and MRI-TBx. SBx avoidance in men with PI-RADS 5 and/or previous negative biopsy may reduce the risk of excess biopsies with a low risk of missing csPCa.

KEY POINTS

• In men undergoing MRI-targeted biopsy, the risk of detecting clinically significant prostate cancer (csPCa) only on additional systematic biopsy (SBx) decreased in men with PI-RADS 5, previous negative biopsy, and younger age. • Using these variables may help select men who could avoid the risk of excess SBx. • If missing csPCa in 5% was acceptable, forgoing SBx in men with PI-RADS 5 and/or previous negative biopsy enabled the highest net reduction in SBx.

External Validation of Models for Prediction of Side-specific Extracapsular Extension in Prostate Cancer Patients Undergoing Radical Prostatectomy

BACKGROUND

Predicting the risk of side-specific extracapsular extension (ECE) is essential for planning nerve-sparing radical prostatectomy (RP) in patients with prostate cancer (PCa).

OBJECTIVE

To externally validate available models for prediction of ECE.

DESIGN, SETTING, AND PARTICIPANTS

Sixteen models were assessed in a cohort of 737 consecutive PCa patients diagnosed via multiparametric magnetic resonance imaging (MRI)-targeted and systematic biopsies and treated with RP between January 2016 and November 2021 at eight referral centers.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Model performance was evaluated in terms of discrimination using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS AND LIMITATIONS

Overall, ECE was identified in 308/1474 (21%) prostatic lobes. Prostatic lobes with ECE had higher side-specific clinical stage on digital rectal examination and MRI, number of positive biopsy cores, and International Society of Urological Pathology grade group in comparison to those without ECE (all p < 0.0001). Less optimistic performance was observed in comparison to previous published studies, although the models described by Pak, Patel, Martini, and Soeterik achieved the highest accuracy (AUC ranging from 0.73 to 0.77), adequate calibration for a probability threshold <40%, and the highest net benefit for a probability threshold >8% on DCA. Inclusion of MRI-targeted biopsy data and MRI information in models improved patient selection and clinical usefulness. Using model-derived cutoffs suggested by their authors, approximately 15% of positive surgical margins could have been avoided. Some available models were not included because of missing data, which constitutes a limitation of the study.

CONCLUSIONS

We report an external validation of models predicting ECE and identified the four with the best performance. These models should be applied for preoperative planning and patient counseling.

PATIENT SUMMARY

We validated several tools for predicting extension of prostate cancer outside the prostate gland. These tools can improve patient selection for surgery that spares nerves affecting recovery of sexual potency after removal of the prostate. They could potentially reduce the risk of finding cancer cells at the edge of specimens taken for pathology, a finding that suggests that not all of the cancer has been removed.

Multiparametric MRI-based nomograms in predicting positive surgical margins of prostate cancer after laparoscopic radical prostatectomy

Background

Positive surgical margins (PSMs) are an independent risk factor of biochemical recurrence in patients with prostate cancer (PCa) after laparoscopic radical prostatectomy; however, limited MRI-based predictive tools are available. This study aimed to develop a novel nomogram combining clinical and multiparametric MRI (mpMRI) parameters to reduce PSMs by improving surgical planning.

Methods

One hundred and three patients with PCa (55 patients with negative surgical margins [NSMs] and 48 patients with PSMs) were included in this retrospective study. The following parameters were obtained using GE Functool post-processing software: diffusion-weighted imaging (DWI); intravoxel incoherent motion model (IVIM); and diffusion kurtosis imaging (DKI). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to analyze the data set to select the optimal MRI predictors. Preoperatively clinical parameters used to build a clinical nomogram (C-nomogram). Multivariable logistic regression analysis was used to build an MRI nomogram (M-nomogram) by introducing the MRI parameters. Based on the MRI and clinical parameters, build an MRI combined with clinical parameters nomogram (MC-nomogram). Comparisons with the M-nomogram and MC-nomogram were based on discrimination, calibration, and decision curve analysis (DCA). A 3-fold cross-validation method was used to assess the stability of the nomogram.

Results

There was no statistical difference in AUC between the C-nomogram (sensitivity=64%, specificity=65% and AUC=0.683), the M-nomogram (sensitivity=57%, specificity=88% and AUC=0.735) and the MC-nomogram (sensitivity= 64%, specificity=82% and AUC=0.756). The calibration curves of the three nomograms used to predict the risk of PSMs in patients with PCa showed good agreement. The net benefit of the MC-nomogram was higher than the others (range, 0.2-0.7).

Conclusions

The mpMRI-based nomogram can predict PSMs in PCa patients. Although its AUC (0.735) is not statistically different from that of the clinical-based nomogram AUC (0.683). However, mpMRI-based nomogram has higher specificity (88% VS. 63%), model stability, and clinical benefit than clinical-based nomogram. And the predictive ability of mpMRI plus clinical parameters for PSMs is further improved.

Radiation Protection and Occupational Exposure on 68Ga-PSMA-11-Based Cerenkov Luminescence Imaging Procedures in Robot-Assisted Prostatectomy

Cerenkov luminescence imaging (CLI) was successfully implemented in the intraoperative context as a form of radioguided cancer surgery, showing promise in the detection of surgical margins during robot-assisted radical prostatectomy. The present study was designed to provide a quantitative description of the occupational radiation exposure of surgery and histopathology personnel from CLI-guided robot-assisted radical prostatectomy after the injection of ⁶⁸Ga-PSMA-11 in a single-injection PET/CT CLI protocol. Methods: Ten patients with preoperative ⁶⁸Ga-PSMA-11 administration and intraoperative CLI were included. Patient dose rate was measured before PET/CT (n = 10) and after PET/CT (n = 5) at a 1-m distance for 4 patient regions (head [A], right side [B], left side [C], and feet [D]). Electronic personal dosimetry (EPD) was used for intraoperative occupational exposure (n = 10). Measurements included the first surgical assistant and scrub nurse at the operating table and the CLI imager/surgeon at the robotic console and encompassed the whole duration of surgery and CLI image acquisition. An estimation of the exposure of histopathology personnel was performed by measuring prostate specimens (n = 8) with a germanium detector. Results: The measured dose rate value before PET/CT was 5.3 ± 0.9 (average ± SD) μSv/h. This value corresponds to a patient-specific dose rate constant for positions B and C of 0.047 μSv/h⋅MBq. The average dose rate value after PET/CT was 1.04 ± 1.00 μSv/h. The patient-specific dose rate constant values corresponding to regions A to D were 0.011, 0.026, 0.024, and 0.003 μSv/h⋅MBq, respectively. EPD readings revealed average personal equivalent doses of 9.0 ± 7.1, 3.3 ± 3.9, and 0.7 ± 0.7 μSv for the first surgical assistant, scrub nurse, and CLI imager/surgeon, respectively. The median germanium detector-measured activity of the prostate specimen was 2.96 kBq (interquartile range, 2.23-7.65 kBq). Conclusion: Single-injection ⁶⁸Ga-PSMA-11 PET/CT CLI procedures are associated with a reasonable occupational exposure level, if kept under 110 procedures per year. Excised prostate specimen radionuclide content was below the exemption level for ⁶⁸Ga. Dose rate-based calculations provide a robust estimation for EPD measurements.

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.

Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning

The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of 0.72 at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning.

Grade and stage misclassification in intermediate unfavorable-risk prostate cancer radiotherapy candidates

BACKGROUND

We tested for upgrading (Gleason grade group [GGG] ≥ 4) and/or upstaging to non-organ-confined stage ([NOC] ≥ pT3/pN1) in intermediate unfavorable-risk (IU) prostate cancer (PCa) patients treated with radical prostatectomy, since both change the considerations for dose and/or type of radiotherapy (RT) and duration of androgen deprivation therapy (ADT).

METHODS

We relied on Surveillance, Epidemiology, and End Results (2010-2015). Proportions of (a) upgrading, (b) upstaging, or (c) upgrading and/or upstaging were tabulated and tested in multivariable logistic regression models.

RESULTS

We identified 7269 IU PCa patients. Upgrading was recorded in 479 (6.6%) and upstaging in 2398 (33.0%), for a total of 2616 (36.0%) upgraded and/or upstaged patients, who no longer fulfilled the IU grade and stage definition. Prostate-specific antigen, clinical stage, biopsy GGG, and percentage of positive cores, neither individually nor in multivariable logistic regression models, discriminated between upgraded and/or upstaged patients versus others.

CONCLUSIONS

IU PCa patients showed very high (36%) upgrading and/or upstaging proportion. Interestingly, the overwhelming majority of those were upstaged to NOC. Conversely, very few were upgraded to GGG ≥ 4. In consequence, more than one-third of IU PCa patients treated with RT may be exposed to suboptimal dose and/or type of RT and to insufficient duration of ADT, since their true grade and stage corresponded to high-risk PCa definition, instead of IU PCa. Data about magnetic resonance imaging were not available but may potentially help with better stage discrimination.

The benefit of adopting Microultrasound in the prostate cancer imaging pathway: A lesion-by-lesion analysis: Biopsies prostatiques guidée par micro-échographie, quel bénéfice ? Une analyse lésion par lésion

INTRODUCTION

- Microultrasound (MicroUS) is a novel imaging modality relying on a high-frequency transducer which confers a three-fold improvement in spatial resolution as compared with conventional transrectal ultrasound. We evaluated the diagnostic value of MRI-MicroUS fusion biopsy and determined the additional benefit of employing MicroUS.

METHODS

- Retrospective analysis of consecutive treatment-naïve men undergoing MRI-MicroUS fusion biopsy between May 2018 and March 2019. Pre-biopsy MRI was systematically reviewed in a dedicated meeting where suspicious lesions PIRADS ≥ 3 were registered and uploaded in the ExactVu MicroUS device. MRI and MicroUS lesions were individually marked in a PIRADS v2 scheme. The biopsy protocol included MRI-MicroUS fusion and MicroUS targeted biopsies; systematic biopsies were performed at clinician's discretion. The diagnostic value was evaluated in terms of detection rate of clinically significant prostate cancer, defined as Gleason pattern ≥ 4 at histology.

RESULTS

- In all, 148 patients with a median age of 69 years (IQR 63-74) and median PSA density of 0.16 ng/ml/cc (0.10-0.23) were included. Clinically significant cancer was detected in 42.5% (63/148) patients. MRI detected 89 lesions in the peripheral zone; 73% (65/89) were visible on MicroUS. Clinically significant cancer was detected in 46.1% (30/65) MRI and MicroUS visible lesions, and in 4.2% (1/24) lesions only visible on mpMRI. MicroUS additionally identified 35 suspicious lesions non-visible on MRI of which clinically significant cancer was present in 25.7% (9/35).

CONCLUSION

- Adding MicroUS to the conventional pathway seems to increase the detection rate of clinically significant disease in unselected men undergoing biopsy. © 2022 Elsevier Masson SAS. All rights reserved.

Contemporary seminal vesicle invasion rates in NCCN high-risk prostate cancer patients

BACKGROUND

Contemporary seminal vesicle invasion (SVI) rates in National Cancer Comprehensive Network (NCCN) high-risk prostate cancer (PCa) patients are not well known but essential for treatment planning. We examined SVI rates according to individual patient characteristics for purpose of treatment planning.

MATERIALS AND METHODS

Within Surveillance, Epidemiology, and End Results (SEER) database (2010-2015), 4975 NCCN high-risk patients were identified. In the development cohort (SEER geographic region of residence: South, North-East, Mid-West, n = 2456), we fitted a multivariable logistic regression model predicting SVI. Its accuracy, calibration, and decision curve analyses (DCAs) were then tested versus previous models within the external validation cohort (SEER geographic region of residence: West, n = 2519).

RESULTS

Out of 4975 patients, 28% had SVI. SVI rate ranged from 8% to 89% according to clinical T stage, prostate-specific antigen (PSA), biopsy Gleason Grade Group and percentage of positive biopsy cores. In the development cohort, these variables were independent predictors of SVI. In the external validation cohort, the current model achieved 77.6% accuracy vs 73.7% for Memorial Sloan Kettering Cancer Centre (MSKCC) vs 68.6% for Gallina et al. Calibration was better than for the two alternatives: departures from ideal predictions were 6.0% for the current model vs 9.8% for MSKCC vs 38.5% for Gallina et al. In DCAs, the current model outperformed both alternatives. Finally, different nomogram cutoffs allowed to discriminate between low versus high SVI risk patients.

CONCLUSIONS

More than a quarter of NCCN high-risk PCa patients harbored SVI. Since SVI positivity rate varies from 8% to 89%, the currently developed model offers a valuable approach to distinguish between low and high SVI risk patients.

Machine Learning-Based Prediction of Pathological Upgrade From Combined Transperineal Systematic and MRI-Targeted Prostate Biopsy to Final Pathology: A Multicenter Retrospective Study

Objective

This study aimed to evaluate the pathological concordance from combined systematic and MRI-targeted prostate biopsy to final pathology and to verify the effectiveness of a machine learning-based model with targeted biopsy (TB) features in predicting pathological upgrade.

Materials and Methods

All patients in this study underwent prostate multiparametric MRI (mpMRI), transperineal systematic plus transperineal targeted prostate biopsy under local anesthesia, and robot-assisted laparoscopic radical prostatectomy (RARP) for prostate cancer (PCa) sequentially from October 2016 to February 2020 in two referral centers. For cores with cancer, grade group (GG) and Gleason score were determined by using the 2014 International Society of Urological Pathology (ISUP) guidelines. Four supervised machine learning methods were employed, including two base classifiers and two ensemble learning-based classifiers. In all classifiers, the training set was 395 of 565 (70%) patients, and the test set was the remaining 170 patients. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The Gini index was used to evaluate the importance of all features and to figure out the most contributed features. A nomogram was established to visually predict the risk of upgrading. Predicted probability was a prevalence rate calculated by a proposed nomogram.

Results

A total of 515 patients were included in our cohort. The combined biopsy had a better concordance of postoperative histopathology than a systematic biopsy (SB) only (48.15% vs. 40.19%, p = 0.012). The combined biopsy could significantly reduce the upgrading rate of postoperative pathology, in comparison to SB only (23.30% vs. 39.61%, p < 0.0001) or TB only (23.30% vs. 40.19%, p < 0.0001). The most common pathological upgrade occurred in ISUP GG1 and GG2, accounting for 53.28% and 20.42%, respectively. All machine learning methods had satisfactory predictive efficacy. The overall accuracy was 0.703, 0.768, 0.794, and 0.761 for logistic regression, random forest, eXtreme Gradient Boosting, and support vector machine, respectively. TB-related features were among the most contributed features of a prediction model for upgrade prediction.

Conclusion

The combined effect of SB plus TB led to a better pathological concordance rate and less upgrading from biopsy to RP. Machine learning models with features of TB to predict PCa GG upgrading have a satisfactory predictive efficacy.

Negative mpMRI Rules Out Extra-Prostatic Extension in Prostate Cancer before Robot-Assisted Radical Prostatectomy

Background: The accuracy of multi-parametric MRI (mpMRI) in the pre-operative staging of prostate cancer (PCa) remains controversial. Objective: The purpose of this study was to evaluate the ability of mpMRI to accurately predict PCa extra-prostatic extension (EPE) on a side-specific basis using a risk-stratified 5-point Likert scale. This study also aimed to assess the influence of mpMRI scan quality on diagnostic accuracy. Patients and Methods: We included 124 men who underwent robot-assisted RP (RARP) as part of the NeuroSAFE PROOF study at our centre. Three radiologists retrospectively reviewed mpMRI blinded to RP pathology and assigned a Likert score (1-5) for EPE on each side of the prostate. Each scan was also ascribed a Prostate Imaging Quality (PI-QUAL) score for assessing the quality of the mpMRI scan, where 1 represents the poorest and 5 represents the best diagnostic quality. Outcome measurements and statistical analyses: Diagnostic performance is presented for the binary classification of EPE, including 95% confidence intervals and the area under the receiver operating characteristic curve (AUC). Results: A total of 231 lobes from 121 men (mean age 56.9 years) were evaluated. Of these, 39 men (32.2%), or 43 lobes (18.6%), had EPE. A Likert score ≥3 had a sensitivity (SE), specificity (SP), NPV, and PPV of 90.4%, 52.3%, 96%, and 29.9%, respectively, and the AUC was 0.82 (95% CI: 0.77-0.86). The AUC was 0.76 (95% CI: 0.64-0.88), 0.78 (0.72-0.84), and 0.92 (0.88-0.96) for biparametric scans, PI-QUAL 1-3, and PI-QUAL 4-5 scans, respectively. Conclusions: MRI can be used effectively by genitourinary radiologists to rule out EPE and help inform surgical planning for men undergoing RARP. EPE prediction was more reliable when the MRI scan was (a) multi-parametric and (b) of a higher image quality according to the PI-QUAL scoring system.

Risk model based on MRI fusion biopsy characteristics predicts biochemical recurrence after radical prostatectomy

BACKGROUND

To determine the prostate cancer biochemical recurrence-related fusion biopsy characteristics before radical surgery and to establish the risk prediction model of biochemical recurrence of prostate cancer.

METHODS

Three hundred and four patients undergoing radical surgery for prostate cancer at Huadong Hospital affiliated to Fudan University between 2009 and 2020 for preoperative magnetic resonance imaging (MRI) before biopsy with suspicious prostate cancer lesions. Each case was followed by a 10 + x needle combination of targeted biopsy (intentional or robotic fusion) with systematic biopsy. Prostate-specific antigen levels were measured at 1, 3, and 6 months postoperatively, followed by reexamination every 6 months. Survival analysis was performed by the Kaplan-Meier method, univariate and multivariate analysis by Cox, and Logistic risk regression models.

RESULTS

Higher Prostate Imaging Reporting And Data System (PI-RADS) scores (p < 0.001), suspicious extracapsular invasion (p < 0.001), and seminal vesicle invasion (p < 0.001) on MRI, the largest lesion diameter on MRI (p = 0.006), higher biopsy International Society of Urological Pathology (ISUP) grade group (p < 0.001) related to higher biochemical recurrence rates, higher pathological staging (p < 0.001), and a greater probability of local lymph node metastasis (p < 0.001). We accurately predicted the biochemical recurrence of prostate cancer after radical surgery based on preoperative features including the long diameter of the largest MRI lesion more than 23 mm, seminal vesicle invasion on MRI, and targeted fusion biopsy ISUP grade >3 Risk stratified classification (AUC = 0.729, p < 0.001). In our cohort, this risk stratification had a larger area under the curve than predictive models based only on magnetic resonance parameters and traditional risk scores.

CONCLUSIONS

In this cohort, seminal vesicle invasion on MRI, the long diameter of the largest MRI lesion, and targeted fusion biopsy ISUP grade grope are significantly predictive of pathologic features and biochemical recurrence after prostate surgery. The risk stratification integrating the three parameters could better predict the biochemical recurrence than the traditional model.

Transrectal Ultrasound in Prostate Cancer: Current Utilization, Integration with mpMRI, HIFU and Other Emerging Applications

Transrectal ultrasound (TRUS) has been an invaluable tool in the assessment of prostate size, anatomy and aiding in prostate cancer (PCa) diagnosis for decades. Emerging techniques warrant an investigation into the efficacy of TRUS, how it compares to new techniques, and options to increase the accuracy of prostate cancer diagnosis. Currently, TRUS is used to guide both transrectal and transperineal biopsy approaches with similar cancer detection rates, but lower rates of infection have been reported with the transperineal approach, while lower rates of urinary retention are often reported with the transrectal approach. Multiparametric MRI has substantial benefits for prostate cancer diagnosis and triage such as lesion location, grading, and can be combined with TRUS to perform fusion biopsies targeting specific lesions. Micro-ultrasound generates higher resolution images that traditional ultrasound and has been shown effective at diagnosing PCa, giving it the potential to become a future standard of care. Finally, high-intensity focused ultrasound focal therapy administered via TRUS has been shown to offer safe and effective short-term oncological control for localized disease with low morbidity, and the precise nature makes it a viable option for salvage and repeat therapy.

WITHDRAWN: The benefit of adopting Microultrasound in the prostate cancer imaging pathway : A lesion-by-lesion analysis

INTRODUCTION

Microultrasound (MicroUS) is a novel imaging modality relying on a high-frequency transducer which confers a three-fold improvement in spatial resolution as compared with conventional transrectal ultrasound. We evaluated the diagnostic value of MRI-MicroUS fusion biopsy and determined the additional benefit of employing MicroUS.

METHODS

Retrospective analysis of consecutive treatment-naïve men undergoing MRI-MicroUS fusion biopsy between May 2018 and March 2019. Pre-biopsy MRI was systematically reviewed in a dedicated meeting where suspicious lesions PIRADS≥3 were registered and uploaded in the ExactVu MicroUS device. MRI and MicroUS lesions were individually marked in a PIRADS v2 scheme. The biopsy protocol included MRI-MicroUS fusion and MicroUS targeted biopsies; systematic biopsies were performed at clinician's discretion. The diagnostic value was evaluated in terms of detection rate of clinically significant prostate cancer, defined as Gleason pattern≥4 at histology.

RESULTS

In all, 148 patients with a median age of 69 years (IQR 63-74) and median PSA density of 0.16ng/ml/cc (0.10-0.23) were included. Clinically significant cancer was detected in 42.5% (63/148) patients. MRI detected 89 lesions in the peripheral zone; 73% (65/89) were visible on MicroUS. Clinically significant cancer was detected in 46.1% (30/65) MRI and MicroUS visible lesions, and in 4.2% (1/24) lesions only visible on mpMRI. MicroUS additionally identified 35 suspicious lesions non-visible on MRI of which clinically significant cancer was present in 25.7% (9/35).

CONCLUSION

Adding MicroUS to the conventional pathway seems to increase the detection rate of clinically significant disease in unselected men undergoing biopsy.

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.