Multiparametric MRI Improves Accuracy of Clinical Nomograms for Predicting Extracapsular Extension of Prostate Cancer

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.

Target Volume Optimization for Localized Prostate Cancer

PURPOSE

To provide a comprehensive review of the means by which to optimize target volume definition for the purposes of treatment planning for patients with intact prostate cancer with a specific emphasis on focal boost volume definition.

METHODS

Here we conduct a narrative review of the available literature summarizing the current state of knowledge on optimizing target volume definition for the treatment of localized prostate cancer.

RESULTS

Historically, the treatment of prostate cancer included a uniform prescription dose administered to the entire prostate with or without coverage of all or part of the seminal vesicles. The development of prostate magnetic resonance imaging (MRI) and positron emission tomography (PET) using prostate-specific radiotracers has ushered in an era in which radiation oncologists are able to localize and focally dose-escalate high-risk volumes in the prostate gland. Recent phase 3 data has demonstrated that incorporating focal dose escalation to high-risk subvolumes of the prostate improves biochemical control without significantly increasing toxicity. Still, several fundamental questions remain regarding the optimal target volume definition and prescription strategy to implement this technique. Given the remaining uncertainty, a knowledge of the pathological correlates of radiographic findings and the anatomic patterns of tumor spread may help inform clinical judgement for the definition of clinical target volumes.

CONCLUSION

Advanced imaging has the ability to improve outcomes for patients with prostate cancer in multiple ways, including by enabling focal dose escalation to high-risk subvolumes. However, many questions remain regarding the optimal target volume definition and prescription strategy to implement this practice, and key knowledge gaps remain. A detailed understanding of the pathological correlates of radiographic findings and the patterns of local tumor spread may help inform clinical judgement for target volume definition given the current state of uncertainty.

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.

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI

OBJECTIVE

To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.

MATERIALS AND METHODS

This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.

RESULTS

A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).

CONCLUSION

Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

Ultrahypofractionated Radiation Therapy for Prostate Cancer Including Seminal Vesicles in the Target Volume: A Treatment-planning Study Based on the HYPO-RT-PC Fractionation Schedule

Purpose

Ultrahypofractionated (UHF) radiation therapy (RT) has become a treatment alternative for patients with localized prostate cancer. In more advanced cases, seminal vesicles (SVs) are routinely included in the target volume. The Scandinavian HYPO-RT-PC trial, which compared 42.7 Gy in 7 fractions (fr) to conventional fractionation (CF), did not include SVs in the clinical target volume. The primary objective of the present work was to implement a ultrahypofractionated-simultaneous integrated boost (UHF-SIB) for prostate cancer RT, incorporating SVs into the target volume based on this fractionation schedule. A secondary objective was to analyze the unintentional dose coverage of SVs from state-of-the-art volumetric modulated arc therapy treatments to the prostate gland only.

Methods and Materials

Two different equieffective UHF-SIB treatment schedules to SVs were derived based on the CF clinical schedule (50.0 Gy/25 fr to elective SVs and 70.0 Gy/35 fr to verified SV-invasion (SVI)) using the linear quadric model with α/β = 2 Gy and 3 Gy. The dose to the prostate was 42.7 Gy/7 fr in both schedules, with 31.2 Gy/37.8 Gy (α/β = 2 Gy) and 32.7 Gy/40.1 Gy (α/β = 3 Gy) to elective SV/verified SVI. Volumetric modulated arc therapy plans to the proximal 10 mm and 20 mm were optimized, and dose-volume metrics for target volumes and organs at risk were evaluated.

Results

Dose metrics were overall lower for UHF-SIB compared with CF. QUANTEC-based volume criteria were 2% to 7% lower for the rectum and 2% to 4% lower for the bladder in the UHF-SIB. The D98% to elective SV was 7 to 12 Gy3 lower with UHF-SIB, and the corresponding data for verified SVI were approximately 2 to 3 Gy3. The SV(10 mm) V90%/(29.5 Gy) for prostate-only treatments (42.7 Gy) were as follows: median (IQR), 99% (87-100) and 78% (58-99) for the clinical target volume and planning target volume, respectively.

Conclusions

UHF RT based on the HYPO-RT-PC fractionation schedule, with a SIB technique, to the prostate and the base of the SV can be planned with lower doses (EQD2) to organs at risk, compared with CF. The unintentional dose to the proximal parts of SVs in prostate-only treatment can be substantial.

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

Purpose

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

Methods and Materials

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

Results

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

Conclusions

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

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.

The Impact of Omitting Contralateral Systematic Biopsy on the Surgical Planning of Patients with a Unilateral Suspicious Lesion on Magnetic Resonance Imaging Undergoing Robot-assisted Radical Prostatectomy for Prostate Cancer

Background and objective

A combined approach of magnetic resonance imaging (MRI)-targeted biopsy (TBx) and bilateral systematic biopsy (SBx) is advised in patients who have an increased risk of prostate cancer (PCa). The diagnostic gain of SBx in detecting PCa for treatment planning of patients undergoing robot-assisted radical prostatectomy (RARP) is unknown. This study aims to determine the impact of omitting contralateral SBx on the surgical planning of patients undergoing RARP in terms of nerve-sparing surgery (NSS) and extended pelvic lymph node dissection (ePLND).

Methods

Case files from 80 men with biopsy-proven PCa were studied. All men had a unilateral suspicious lesion on MRI, and underwent TBx and bilateral SBx. Case files were presented to five urologists for the surgical planning of RARP. Each case file was presented randomly using two different sets of information: (1) results of TBx + bilateral SBx, and (2) results of TBx + ipsilateral SBx. The urologists assessed whether they would perform NSS and/or ePLND.

Key findings and limitations

A change in the surgical plan concerning NSS on the contralateral side was observed in 9.0% (95% confidence interval [CI] 6.4-12.2) of cases. Additionally, the indication for ePLND changed in 5.3% (95% CI 3.3-7.9) of cases. Interobserver agreement based on Fleiss' kappa changed from 0.44 to 0.15 for the indication of NSS and from 0.84 to 0.83 for the indication of ePLND.

Conclusions and clinical implications

In our series, the diagnostic information obtained from contralateral SBx has limited impact on the surgical planning of patients with a unilateral suspicious lesion on MRI scheduled to undergo RARP.

Patient summary

In patients with one-sided prostate cancer on magnetic resonance imaging, omitting biopsies on the other side rarely changed the surgical plan with respect to nerve-sparing surgery and the indication to perform extended lymph node dissection.

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.

Quantitative MRI in the Local Staging of Prostate Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

Local staging of prostate cancer (PCa) is important for treatment planning. Radiologist interpretation using qualitative criteria is variable with high specificity but low sensitivity. Quantitative methods may be useful in the diagnosis of extracapsular extension (ECE).

PURPOSE

To assess the performance of quantitative MRI markers for detecting ECE.

STUDY TYPE

Systematic review and meta-analysis.

SUBJECTS

4800 patients from 28 studies with histopathologically confirmed PCa on radical prostatectomy were pooled for meta-analysis. Patients from 46 studies were included for systematic review.

FIELD STRENGTH/SEQUENCE

Diffusion-weighted, T2-weighted, and dynamic contrast-enhanced MRI at 1.5 T or 3 T.

ASSESSMENT

PubMed, Embase, Web of Science, Scopus, and Cochrane databases were searched to identify studies on diagnostic test accuracy or association of any quantitative MRI markers with ECE. Results extracted by two independent reviewers for tumor contact length (TCL) and mean apparent diffusion coefficient (ADC-mean) were pooled for meta-analysis, but not for other quantitative markers including radiomics due to low number of studies available.

STATISTICAL TESTS

Hierarchical summary receiver operating characteristic (HSROC) curves were computed for both TCL and ADC-mean, but summary operating points were computed for TCL only. Heterogeneity was investigated by meta-regression. Results were significant if P ≤ 0.05.

RESULTS

At the 10 mm threshold for TCL, summary sensitivity and specificity were 0.76 [95% confidence interval (CI) 0.71-0.81] and 0.68 [95% CI 0.63-0.73], respectively. At the 15 mm threshold, summary sensitivity and specificity were 0.70 [95% CI 0.53-0.83] and 0.74 [95% CI 0.60-0.84] respectively. The area under the HSROC curves for TCL and ADC-mean were 0.79 and 0.78, respectively. Significant sources of heterogeneity for TCL included timing of MRI relative to biopsy.

DATA CONCLUSION

Both 10 mm and 15 mm thresholds for TCL may be reasonable for clinical use. From comparison of the HSROC curves, ADC-mean may be superior to TCL at higher sensitivities.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Risk Biomarkers for Biochemical Recurrence after Radical Prostatectomy for Prostate Cancer Using Clinical and MRI-Derived Semantic Features

OBJECTIVES

This study aimed to assess the impact of the covariates derived from a predictive model for detecting extracapsular extension on pathology (pECE+) on biochemical recurrence-free survival (BCRFS) within 4 years after robotic-assisted radical prostatectomy (RARP).

METHODS

Retrospective data analysis was conducted from a single center between 2015 and 2022. Variables under consideration included prostate-specific antigen (PSA) levels, patient age, prostate volume, MRI semantic features, and Grade Group (GG). We also assessed the influence of pECE+ and positive surgical margins on BCRFS. To attain these goals, we used the Kaplan-Meier survival function and the multivariable Cox regression model. Additionally, we analyzed the MRI features on BCR (biochemical recurrence) in low/intermediate risk patients.

RESULTS

A total of 177 participants with a follow-up exceeding 6 months post-RARP were included. The 1-year, 2-year, and 4-year risks of BCR after radical prostatectomy were 5%, 13%, and 21%, respectively. The non-parametric approach for the survival analysis showed that adverse MRI features such as macroscopic ECE on MRI (mECE+), capsular disruption, high tumor capsular contact length (TCCL), GG ≥ 4, positive surgical margins (PSM), and pECE+ on pathology were risk factors for BCR. In low/intermediate-risk patients (pECE- and GG < 4), the presence of adverse MRI features has been shown to increase the risk of BCR.

CONCLUSIONS

The study highlights the importance of incorporating predictive MRI features for detecting extracapsular extension pre-surgery in influencing early outcomes and clinical decision making; mECE+, TCCL, capsular disruption, and GG ≥ 4 based on pre-surgical biopsy were independent prognostic factors for early BCR. The presence of adverse features on MRI can assist in identifying low/intermediate-risk patients who will benefit from closer monitoring.

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.

Predictors of Extraprostatic Extension in Patients with Prostate Cancer

PURPOSE

To identify effective factors predicting extraprostatic extension (EPE) in patients with prostate cancer (PCa).

METHODS

This retrospective cohort study recruited 898 consecutive patients with PCa treated with robot-assisted laparoscopic radical prostatectomy. The patients were divided into EPE and non-EPE groups based on the analysis of whole-mount histopathologic sections. Histopathological analysis (ISUP biopsy grade group) and magnetic resonance imaging (MRI) (PI-RADS v2.1 scores [1-5] and the Mehralivand EPE grade [0-3]) were used to assess the prediction of EPE. We also assessed the clinical usefulness of the prediction model based on decision-curve analysis.

RESULTS

Of 800 included patients, 235 (29.3%) had EPE, and 565 patients (70.7%) did not (non-EPE). Multivariable logistic regression analysis showed that the biopsy ISUP grade, PI-RADS v2.1 score, and Mehralivand EPE grade were independent risk factors for EPE. In the regression assessment of the models, the best discrimination (area under the curve of 0.879) was obtained using the basic model (age, serum PSA, prostate volume at MRI, positive biopsy core, clinical T stage, and D'Amico risk group) and Mehralivand EPE grade 3. Decision-curve analysis showed that combining Mehralivand EPE grade 3 with the basic model resulted in superior net benefits for predicting EPE.

CONCLUSION

Mehralivand EPE grades and PI-RADS v2.1 scores, in addition to basic clinical and demographic information, are potentially useful for predicting EPE in patients with PCa.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Prostate-specific antigen level, biopsy grade group, and tumor-capsular contact length on magnetic resonance imaging are independently associated with an extraprostatic extension

OBJECTIVE

To define the clinicopathological and radiological factors independently associated with the existence of an extraprostatic extension in radical prostatectomy specimens.

METHODS

A total of 202 patients who underwent robotic prostatectomy following biparametric magnetic resonance imaging were assessed. We evaluated the clinicopathological and magnetic resonance imaging variables. We performed receiver-operating characteristic curve analyses to identify factors associated with extraprostatic extension. We engaged in multivariate analysis to identify factors independently associated with such extension.

RESULTS

Extraprostatic extensions were apparent in the final prostatectomy specimens of 62 patients (31%). The areas under the curves of the prostate-specific antigen level, the biopsy grade group, and the tumor-capsular contact length on magnetic resonance imaging were 0.76, 0.71, and 0.70, respectively, in receiver-operating characteristic analysis when used to predict extraprostatic extension; thus, higher than the areas under the curves of the other variables (0.61-0.68). The prostate-specific antigen level (odds ratio 1.090, p = 0.004), the biopsy grade group (odds ratios 2.678 and 6.358, p = 0.017 and p < 0.001 for grade group 3-4 and 5), and the tumor-capsular contact length (odds ratio 1.079, p = 0.001) were independently associated with extraprostatic extension. When the three factors were combined, the area under the receiver-operator characteristic curve increased to 0.79.

CONCLUSIONS

The prostate-specific antigen level, the biopsy grade group, and the tumor-capsular contact length on magnetic resonance imaging were independently associated with extracapsular extension.

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.

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

Objective

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

Materials and Methods

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

Results

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

Conclusion

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

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

Simple Summary

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

Abstract

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

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.

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

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

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

Introduction

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

Materials and Methods

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

Results

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

Conclusion

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Preliminary Results of an Ongoing Prospective Clinical Trial on the Use of 68Ga-PSMA and 68Ga-DOTA-RM2 PET/MRI in Staging of High-Risk Prostate Cancer Patients

The aim of the present study is to investigate the synergic role of ⁶⁸Ga-PSMA PET/MRI and ⁶⁸Ga-DOTA-RM2 PET/MRI in prostate cancer (PCa) staging. We present pilot data on twenty-two patients with biopsy-proven PCa that underwent ⁶⁸Ga-PSMA PET/MRI for staging purposes, with 19/22 also undergoing ⁶⁸Gaa-DOTA-RM2 PET/MRI. TNM classification based on image findings was performed and quantitative imaging parameters were collected for each scan. Furthermore, twelve patients underwent radical prostatectomy with the availability of histological data that were used as the gold standard to validate intraprostatic findings. A DICE score between regions of interest manually segmented on the primary tumour on ⁶⁸Ga-PSMA PET, ⁶⁸Ga-DOTA-RM2 PET and on T2 MRI was computed. All imaging modalities detected the primary PCa in 18/19 patients, with ⁶⁸Ga-DOTA-RM2 PET not detecting any lesion in 1/19 patients. In the remaining patients, ⁶⁸Ga-PSMA and MRI were concordant. Seven patients presented seminal vesicles involvement on MRI, with two of these being also detected by ⁶⁸Ga-PSMA, and ⁶⁸Ga-DOTA-RM2 PET being negative. Regarding extraprostatic disease, ⁶⁸Ga-PSMA PET, ⁶⁸Ga-DOTA-RM2 PET and MRI resulted positive in seven, four and five patients at lymph-nodal level, respectively, and at a bone level in three, zero and one patients, respectively. These preliminary results suggest the potential complementary role of ⁶⁸Ga-PSMA PET, ⁶⁸Ga-DOTA-RM2 PET and MRI in PCa characterization during the staging phase.

External validation of a magnetic resonance imaging-based algorithm for prediction of side-specific extracapsular extension in prostate cancer

Introduction

Recently developed algorithm for prediction of side-specific extracapsular extension (ECE) of prostate cancer required validation before being recommended to use. The algorithm assumed that ECE on a particular side was not likely with same side maximum tumor diameter (MTD) <15 mm AND cancerous tissue in ipsilateral biopsy <15% AND PSA <20 ng/mL (both sides condition). The aim of the study was to validate this predictive tool in patients from another department.

Material and methods

Data of 154 consecutive patients (308 prostatic lateral lobes) were used for validation. Predictive factors chosen in the development set of patients were assessed together with other preoperative parameters using logistic regression to check for their significance. Sensitivity, specificity, negative and positive predictive values were calculated for bootstrapped risk-stratified validation dataset.

Results

Validation cohort did not differ significantly from development cohort regarding PSA, PSA density, Gleason score (GS), MTD, age, ECE and seminal vesicle invasion rate. In bootstrapped data set (n = 200 random sampling) algorithm revealed 70.2% sensitivity (95% confidence interval (CI) 58.8–83.0%), 49.9% specificity (95%CI: 42.0–57.7%), 83.9% negative predictive value (NPV; 95%CI: 76.1–91.4%) and 31.1% positive predictive value (PPV; 95%CI: 19.6–39.7%). When limiting analysis to high-risk patients (Gleason score >7) the algorithm improved its performance: sensitivity 91%, specificity 47%, PPV 53%, NPV 89%.

Conclusions

Analyzed algorithm is useful for identifying prostate lobes without ECE and deciding on ipsilateral nerve-sparing technique during radical prostatectomy, especially in patients with GS >7. Due to significant number of false positives in case of: MTD ≥15 mm OR cancer in biopsy ≥15% OR PSA ≥20 ng/mL additional evaluation is necessary to aid decision-making.

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

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

Risk Stratification of Patients Candidate to Radical Prostatectomy Based on Clinical and Multiparametric Magnetic Resonance Imaging Parameters: Development and External Validation of Novel Risk Groups

BACKGROUND

Despite the key importance of magnetic resonance imaging (MRI) parameters, risk classification systems for biochemical recurrence (BCR) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) are still based on clinical variables alone.

OBJECTIVE

We aimed at developing and validating a novel classification integrating clinical and radiological parameters.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective multicenter cohort study was conducted between 2014 and 2020 across seven academic international referral centers. A total of 2565 patients treated with RP for PCa were identified.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Early BCR was defined as two prostate-specific antigen (PSA) values of ≥0.2 ng/ml within 3 yr after RP. Kaplan-Meier and Cox regressions tested time and predictors of BCR. Development and validation cohorts were generated from the overall patient sample. A model predicting early BCR based on Cox-derived coefficients represented the basis for a nomogram that was validated externally. Predictors consisted of PSA, biopsy grade group, MRI stage, and the maximum diameter of lesion at MRI. Novel risk categories were then identified. The Harrel's concordance index (c-index) compared the accuracy of our risk stratification with the European Association of Urology (EAU), Cancer of the Prostate Risk Assessment (CAPRA), and International Staging Collaboration for Cancer of the Prostate (STAR-CAP) risk groups in predicting early BCR.

RESULTS AND LIMITATIONS

Overall, 200 (8%), 1834 (71%), and 531 (21%) had low-, intermediate-, and high-risk disease according to the EAU risk groups. The 3-yr overall BCR-free survival rate was 84%. No differences were observed in the 3-yr BCR-free survival between EAU low- and intermediate-risk groups (88% vs 87%; p = 0.1). The novel nomogram depicted optimal discrimination at external validation (c-index 78%). Four new risk categories were identified based on the predictors included in the Cox-based nomogram. This new risk classification had higher accuracy in predicting early BCR (c-index 70%) than the EAU, CAPRA, and STAR-CAP risk classifications (c-index 64%, 63%, and 67%, respectively).

CONCLUSIONS

We developed and externally validated four novel categories based on clinical and radiological parameters to predict early BCR. This novel classification exhibited higher accuracy than the available tools.

PATIENT SUMMARY

Our novel and straightforward risk classification outperformed currently available preoperative risk tools and should, therefore, assist physicians in preoperative counseling of men candidate to radical treatment for prostate cancer.

Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer

PURPOSE

To investigate and validate the potential role of a radiomics signature in predicting the side-specific probability of extracapsular extension (ECE) of prostate cancer (PCa).

PROCEDURES

The preoperative magnetic resonance imaging data of 238 prostatic samples from 119 enrolled PCa patients were retrospectively assessed. The samples with were randomized in a two-to-one ratio into training (n = 74) and validation (n = 45) datasets. The radiomics features were derived from T2-weighted images (T2WIs). The optimal radiomics features were identified from the least absolute shrinkage and selection operator (LASSO) logistic regression model and were used to construct a predictive radiomics signature via dimension reduction and selection approaches. The association between the radiomics signatures and pathological ECE status was explored. Receiver operating characteristic (ROC) analysis was used to assess the discriminatory ability of the signature. The calibration performance and clinical usefulness of the radiomics signature were subsequently assessed by calibration curve and decision curve analyses.

RESULTS

The proposed radiomics signature that incorporated 17 selected radiomics features was significantly associated with pathological ECE outcomes (P < 0.001) in both the training and validation datasets. The constructed model displayed good discrimination, with areas under the curve (AUC) of 0.906 (95 % confidence interval (CI), 0.847, 0.948) and 0.821 (95 % CI, 0.726, 0.894) for the training and validation datasets, respectively, and had a good calibration performance. The clinical utility of this model was confirmed through decision curve analysis.

CONCLUSIONS

The radiomics signature based on T2WIs showed the potential to predict the side-specific probability of pathological ECE status and can facilitate the preoperative individualized predictions for PCa patients.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Objectives

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

Patients and Methods

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

Results

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

Conclusion

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

Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective

Purpose

This study aimed to develop a model for predicting pathologic extracapsular extension (ECE) and seminal vesicle invasion (SVI) while integrating magnetic resonance imaging-based T-staging (cTMRI, cT1c-cT3b).

Materials and Methods

A total of 1,915 who underwent radical prostatectomy between 2006-2016 met the inclusion/exclusion criteria. We performed a multivariate logistic regression analysis as well as Bayesian network (BN) modeling based on possible confounding factors. The BN model was internally validated using 5-fold validation.

Results

According to the multivariate logistic regression analysis, initial prostate-specific antigen (iPSA) (β=0.050, p<0.001), percentage of positive biopsy cores (PPC) (β=0.033, p<0.001), both lobe involvement on biopsy (β=0.359, p=0.009), Gleason score (β=0.358, p<0.001), and cTMRI (β=0.259, p<0.001) were significant factors for ECE. For SVI, iPSA (β=0.037, p<0.001), PPC (β=0.024, p<0.001), GS (β=0.753, p<0.001), and cTMRI (β=0.507, p<0.001) showed statistical significance. BN models to predict ECE and SVI were also successfully established. The overall AUC/accuracy of the BN models were 0.76/73.0% and 0.88/89.6% for ECE and SVI, respectively. According to internal comparison between the BN model and Roach formula, BN model had improved AUC values for predicting ECE (0.76 vs. 0.74; p=0.060) and SVI (0.88 vs. 0.84, p<0.001).

Conclusion

wo models to predict pathologic ECE and SVI integrating cTMRI were established and installed on a separate website for public access to guide radiation oncologists.

Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer

BACKGROUND

Preoperative prediction of extracapsular extension (ECE) of prostate cancer (PCa) is important to guide clinical decision-making and improve patient prognosis.

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (mpMRI)-based peritumoral radiomics for preoperative prediction of the presence of ECE.

STUDY TYPE

Retrospective.

POPULATION

Two hundred eighty-four patients with PCa from two centers (center 1: 226 patients; center 2: 58 patients). Cases from center 1 were randomly divided into training (158 patients) and internal validation (68 patients) sets. Cases from center 2 were assigned to the external validation set.

FIELD STRENGTH/SEQUENCE

A 3.0 T MRI scanners (three vendors). Sequence: Pelvic T2-weighted turbo/fast spin echo sequence and diffusion weighted echo planar imaging sequence.

ASSESSMENT

The peritumoral region (PTR) was obtained by 3-12 mm (half of the tumor length) 3D dilatation of the intratumoral region (ITR). Single-MRI radiomics signatures, mpMRI radiomics signatures, and integrated models, which combined clinical characteristics with the radiomics signatures were built. The discrimination ability was assessed by area under the receiver operating characteristic curve (AUC) in the internal and external validation sets.

STATISTICAL TESTS

Fisher's exact test, Mann-Whitney U-test, DeLong test.

RESULTS

The PTR radiomics signatures demonstrated significantly better performance than the corresponding ITR radiomics signatures (AUC: 0.674 vs. 0.554, P < 0.05 on T2-weighted, 0.652 vs. 0.546, P < 0.05 on apparent diffusion coefficient, 0.682 vs. 0.556 on mpMRI in the external validation set). The integrated models combining the PTR radiomics signature with clinical characteristics performed better than corresponding radiomics signatures in the internal validation set (eg. AUC: 0.718 vs. 0.671, P < 0.05 on mpMRI) but performed similar in the external validation set (eg. AUC: 0.684, vs. 0.682, P = 0.45 on mpMRI).

DATA CONCLUSION

The peritumoral radiomics can better predict the presence of ECE preoperatively compared with the intratumoral radiomics and may have better generalization than clinical characteristics. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.

Clinical outcomes associated with prostate cancer conspicuity on biparametric and multiparametric MRI: a protocol for a systematic review and meta-analysis of biochemical recurrence following radical prostatectomy

INTRODUCTION

There is an increasing body of evidence to suggest that visibility of prostate cancer on magnetic resonance (MRI) may be related to likelihood of adverse pathological outcomes. Biochemical recurrence (BCR) after radical prostatectomy remains a significant clinical challenge and a means of predicting likelihood of this prior to surgery could inform treatment choice. It appears that MRI could be a potential candidate strategy for BCR prediction, and as such, there is a need to review extant literature on the prognostic capability of MRI. Here, we describe a protocol for a systematic review and meta-analysis of the utility of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) in predicting BCR following radical prostatectomy for prostate cancer treatment.

METHODS AND ANALYSIS

PubMed, MEDLINE, Embase and Cochrane databases will be searched and screening will be guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. In order to meet the inclusion criteria, papers must be English-language articles involving patients who have had bpMRI or mpMRI for suspected prostate cancer and have undergone radical prostatectomy as definitive therapy. Patients must have had prostate-specific antigen monitoring before and after surgery. All relevant papers published from July 1977 to October 2020 will be eligible for inclusion. The Newcastle-Ottawa score will be used to determine the quality and bias of the studies. This protocol is written in-line with the PRISMA protocol 2015 checklist.

ETHICS AND DISSEMINATION

There are no relevant ethical concerns. Dissemination of this protocol will be via peer-reviewed journals as well as national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42020206074.

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

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

Techniques and Outcomes of MRI-TRUS Fusion Prostate Biopsy

PURPOSE OF REVIEW

The goal of this study is to review recent findings and evaluate the utility of MRI transrectal ultrasound fusion biopsy (FBx) techniques and discuss future directions.

RECENT FINDINGS

FBx detects significantly higher rates of clinically significant prostate cancer (csPCa) than ultrasound-guided systematic prostate biopsy (SBx), particularly in repeat biopsy settings. FBx has also been shown to detect significantly lower rates of clinically insignificant prostate cancer. In addition, a dedicated prostate MRI can assist in more accurately predicting the Gleason score and provide further information regarding the index cancer location, prostate volume, and clinical stage. The ability to accurately evaluate specific lesions is vital to both focal therapy and active surveillance, for treatment selection, planning, and adequate follow-up. FBx has been demonstrated in multiple high-quality studies to have improved performance in diagnosis of csPCa compared to SBx. The combination of FBx with novel technologies including radiomics, prostate-specific membrane antigen positron emission tomography (PSMA PET), and high-resolution micro-ultrasound may have the potential to further enhance this performance.

MRI predicts prostatic urethral involvement in men undergoing radical prostatectomy: implications for cryo-ablation of localized prostate cancer

PURPOSE

To determine whether multi-parametric magnetic resonance imaging (mpMRI) can reliably predict proximity of prostate cancer to the prostatic urethra in a contemporary series of men undergoing radical prostatectomy (RP) at two academic centers.

METHODS

Clinical characteristics of consecutive men undergoing pre-operative mpMRI prior to RP and whole-mount axial serial step-sectioned pathology examination at two academic centers between Jun 2016 and Oct 2018 were analyzed retrospectively. Every tumor was characterized by its pathologic minimum distance to the prostatic urethral lumen (pMDUL). Only the cancer closest to the urethra represented the prostatic urethral index lesion. The radiologic minimum distance of the index lesion to the prostatic urethral lumen was measured and noted as ≤ 5 mm versus  > 5 mm. The sensitivity, specificity, positive and negative predicting values (PPV and NPV) and area under the receivers operating characteristics curve (AUC) were calculated for performance of mpMRI for predicting pMDUL ≤ 5 mm.

RESULTS

Of the 163 surgical specimens examined, 112 (69%) exhibited a pMDUL ≤ 5 mm. These men had significantly higher grade group (GG) and advanced pathological and clinical stage. The rates of high PI-RADS score and presence of gross extracapsular extension were also significantly greater for the group with pMDUL ≤ 5 mm. The AUC, sensitivity, specificity, PPV, and NPV were 0.641, 51.8, 76.5, 82.9, and 42.4%, respectively, for mpMRI to predict pMDUL < 5 mm.

CONCLUSIONS

Nearly 70% of men undergoing RP present with tumor within 5 mm of the prostatic urethra. These tumors present higher risk characteristics, and mpMRI exhibited moderate performance and high PPV in their pre-operative detection. Physicians performing partial gland ablation should take these results into consideration during treatment selection and planning.

Multiparametric MRI in the management of prostate cancer: an update-a narrative review

The growing interest in multiparametric MRI is leading to important changes in the diagnostic process of prostate cancer. MRI-targeted biopsy is likely to become a standard for the diagnosis of prostate cancer in the next years. Despite it is well known that MRI has no role as a staging technique, it is clear that multiparametric MRI may be of help in active surveillance protocols. Noteworthy, MRI in active surveillance is not recommended, but a proper understanding of its potential may be of help in achieving the goals of a delayed treatment strategy. Moreover, the development of minimally invasive techniques, like laparoscopic and robotic surgery, has led to greater expectations as regard to the functional outcomes of radical prostatectomy. Multiparametric MRI may play a role in planning surgical strategies, with the aim to provide the highest oncologic outcome with a minimal impact on the quality of life. We maintain that a proper anatomic knowledge of prostate lesions may allow the surgeon to achieve a better result in planning as well as in performing surgery and help the surgeon and the patient engage in a shared decision in planning a more effective strategy for prostate cancer control and treatment. This review highlights the advantages and the limitations of multiparametric MRI in prostate cancer diagnosis, in active surveillance and in planning surgery.

Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients

BACKGROUND

To investigate whether magnetic resonance (MR) radiomic features combined with machine learning may aid in predicting extraprostatic extension (EPE) in high- and non-favorable intermediate-risk patients with prostate cancer.

PURPOSE

To investigate the diagnostic performance of radiomics to detect EPE.

MATERIAL AND METHODS

MR radiomic features were extracted from 228 patients, of whom 86 were diagnosed with EPE, using prostate and lesion segmentations. Prediction models were built using Random Forest. Further, EPE was also predicted using a clinical nomogram and routine radiological interpretation and diagnostic performance was assessed for individual and combined models.

RESULTS

The MR radiomic model with features extracted from the manually delineated lesions performed best among the radiomic models with an area under the curve (AUC) of 0.74. Radiology interpretation yielded an AUC of 0.75 and the clinical nomogram (MSKCC) an AUC of 0.67. A combination of the three prediction models gave the highest AUC of 0.79.

CONCLUSION

Radiomic analysis combined with radiology interpretation aid the MSKCC nomogram in predicting EPE in high- and non-favorable intermediate-risk patients.

External Validation of a Multiparametric Magnetic Resonance Imaging-based Nomogram for the Prediction of Extracapsular Extension and Seminal Vesicle Invasion in Prostate Cancer Patients Undergoing Radical Prostatectomy

The nomogram reported by Gandaglia et al (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. Eur Urol 2020;77:733-41) predicting extracapsular extension (ECE) or seminal vesicle invasion (SVI) has been developed using multiparametric magnetic resonance imaging (MRI) parameters and MRI-targeted biopsy. We aimed to validate this nomogram externally by analyzing 566 patients harboring prostate cancer diagnosed on MRI-targeted biopsy followed by radical prostatectomy. At final pathology, 37% and 12% patients had ECE and SVI, respectively. Performance of the nomogram, in comparison with the Memorial Sloan Kettering Cancer Center (MSKCC) model and Partin tables, was evaluated using discrimination, calibration, and decision curve analysis. Regarding ECE prediction, the nomogram showed higher discrimination (71.8% vs 69.8%, p = 0.3 and 71.8% vs 61.3%, p < 0.001), and similar miscalibration and net benefit for probability threshold above 30% when compared with MSKCC model and Partin tables, respectively. Performance of the nomogram with regard to SVI was comparable in terms of discrimination (68.5% vs 70.4% vs 67.8%, p ≥ 0.6), presenting a slight overestimation on calibration plots and a net benefit for probability threshold above 7.5%. This is the first multicentric study that externally validates a nomogram predicting ECE and SVI in patients diagnosed with MRI-targeted biopsy. Its performance was less optimistic than expected, and implementation of MRI in this setting was not associated with a clear improvement in patient selection and clinical usefulness when compared with available models. We proposed an updated version of the nomogram predicting ECE using the recalibration method, which leads to an improvement in its performance and needs to be validated in another external set. PATIENT SUMMARY: We validate a prediction tool based on multiparametric magnetic resonance imaging (MRI) parameters and MRI-targeted biopsy predicting extracapsular extension and seminal vesicle invasion at radical prostatectomy. An improvement of patient selection was not clearly demonstrated when compared with available models based on clinical parameters, and implementation of MRI in this setting still needs to be clarified.

The role of T2-weighted images in assessing the grade of extraprostatic extension of the prostate carcinoma

PURPOSE

Extraprostatic extension (EPE) is an unfavorable prognostic factor and the grade of EPE is also shown to be correlated with the prognosis of prostate cancer. The current study assessed the value of prostate magnetic resonance imaging (MRI) in measuring the radial distance (RD) of EPE and the role of T2 WI signs in predicting the grade of EPE.

MATERIALS AND METHODS

A total of 110 patients who underwent prostate MRI before radical prostatectomy are enrolled in this retrospective study. Eighty-four patients have organ confined disease and the remaining twenty-six patients have EPE all verified by histopathology. Prostate MRI examinations were conducted with 3T MRI scanner and phased array coil with the following sequences: T2 WI, T1 WI, DCE, DWI with ADC mapping, and high b-value at b = 1500 s/mm². The likelihood of EPE with 5-point Likert scale was assigned, several MRI features were extracted for each dominant tumor identified by using T2 WI. Tumors with Likert scales 4-5 were evaluated further to obtain MRI-based RD. The relationship between pathological and MRI-determined RD was tested. Univariate and multivariate logistic regression models were developed to detect the grade of pathological EPE. The inputs were among the 2 clinical parameters and 4 MRI features.

RESULTS

There is a moderate correlation between pathological RD and MRI-determined RD (ρ = 0.45, P < 0.01). In univariate and multivariate models, MRI features and clinical parameters possess varying significance levels (univariate models; P = 0.048-0.788, multivariate models; P = 0.173-0.769). Multivariate models perform better than the univariate models by offering fair to good performances (AUC = 0.69-0.85). The multivariate model that employs the MRI features offers better performance than the model employs clinical parameters (AUC = 0.81 versus 0.69).

CONCLUSION

Co-existence of T2 WI signs provide higher diagnostic value even than clinical parameters in predicting the grade of EPE. Combined use of clinical parameters and MRI features deliver slightly superior performance than MRI features alone.

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

Objective

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

Patients and Methods

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

Results

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

Conclusion

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

Prognostic Implications of Multiparametric Magnetic Resonance Imaging and Concomitant Systematic Biopsy in Predicting Biochemical Recurrence After Radical Prostatectomy in Prostate Cancer Patients Diagnosed with Magnetic Resonance Imaging-targeted Biopsy

BACKGROUND

The prognostic role of multiparametric magnetic resonance imaging (mpMRI) and systematic biopsy in predicting biochemical recurrence (BCR) after radical prostatectomy (RP) in prostate cancer (PCa) patients has not been addressed yet.

OBJECTIVE

To develop a risk tool predicting BCR after RP in patients diagnosed with magnetic resonance imaging (MRI)-targeted biopsy.

DESIGN, SETTING, AND PARTICIPANTS

A total of 804 patients with a clinical suspicion of PCa and positive mpMRI diagnosed with MRI-targeted plus concomitant systematic biopsy treated with RP were identified.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES

The outcome was represented by BCR defined as two prostate-specific antigen (PSA) values ≥0.2ng/ml after surgery. Multivariable Cox regression analyses assessed the predictors of BCR. A risk tool model based on imaging and biopsy parameters was developed and validated internally. The c-index, calibration plot, and decision curve analyses were used to assess discrimination, calibration, and the net benefit associated with its use in predicting BCR at 36 mo.

RESULTS AND LIMITATIONS

Median (interquartile range) follow-up was 28 (25-29) mo, and 89 patients experienced BCR. The 36-mo BCR-free survival rate was 89%. The maximum diameter of the index lesion and seminal vesicle invasion (SVI) at mpMRI as well as the presence of clinically significant PCa at systematic biopsy (defined as a grade group of >2) were associated with BCR (all p≤0.03). A model based on PSA, Prostate Imaging Reporting and Data System score, SVI at mpMRI, diameter of the index lesion, grade group at MRI-targeted biopsy, and clinically significant PCa at systematic biopsy achieved the highest discrimination (77%) among all clinical models, as well as the European Association of Urology risk groups (62%) and the Cancer of the Prostate Risk Assessment (CAPRA) score (60%). This tool was characterized by excellent calibration at internal validation and the highest net benefit when predicting BCR for the threshold risk between 0% and 30%.

CONCLUSIONS

The adoption of predictive models accounting for mpMRI and MRI-targeted biopsy-derived variables and concomitant systematic biopsy would improve clinicians' ability to identify patients at a higher risk of early recurrence after surgery.

PATIENT SUMMARY

The use of information obtained at multiparametric magnetic resonance imaging (mpMRI), and MRI-targeted and concomitant systematic biopsy would improve clinicians' ability to identify prostate cancer patients at a higher risk of experiencing early biochemical recurrence after surgery.

Confirmation by Early Oncologic Outcomes After Surgery of the Accuracy of Intermediate-risk Prostate Cancer Classification Based on Magnetic Resonance Imaging Staging and Targeted Biopsy

Over the past decade, prostate cancer (PCa) diagnosis drastically evolved from systematic biopsies (SBs) to multiparametric magnetic resonance imaging (mpMRI) and targeted biopsy (TB), which have emerged as powerful imaging tools for diagnosis, staging, and preoperative planning. MRI and TB should now be widely adopted for assessing prognosis and be incorporated into predictive models. To date, the standard intermediate risk classification (IRC) defined unfavourable and favourable disease with clinical information and overall biopsy data. Roumiguie et al have proposed a new model based on mpMRI staging and grade group on TB and validated it using radical prostatectomy (RP) pathology (Urol Oncol 2020;38:386-92). The aim of our study was to validate the accuracy of this new IRC with early oncologic outcomes and biochemical recurrence (BCR) after RP. From a prospective database of RP patients with positive prebiopsy mpMRI (Prostate Imaging-Reporting and Data System score ≥3) followed by SB in combination with TB, 454 patients with intermediate-risk PCa were included. Median follow-up was 31.5 mo. The new IRC outperformed the standard IRC in predicting BCR (p =  0.007). The area under the curve was 0.613 for the new MRI- and TB-based IRC versus 0.575 for the standard IRC. This new IRC could optimise the prediction of recurrence risk before treatment decision-making.

Patient summary

Outcomes after surgery confirm the accuracy of the new classification of intermediate-risk prostate cancer based on magnetic resonance imaging (MRI) staging and targeted biopsy data. We found that this new classification outperformed the standard classification in predicting biochemical recurrence of cancer for men with positive MRI findings undergoing targeted biopsies.

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

Background

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

Methods

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

Results

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

Conclusions

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

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

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

Independent external validation of nomogram to predict extracapsular extension in patients with prostate cancer

INTRODUCTION

The objective of this study was to perform an independent external validation of the Giganti-Coppola nomogram (GCN), which uses clinical and radiological parameters to predict prostate extracapsular extension (ECE) on the final pathology of patients undergoing radical prostatectomy (RP).

MATERIAL AND METHODS

Seventy-two patients diagnosed with prostate cancer (PCa), who were RP candidates from two institutions, were prospectively included. All patients underwent preoperative multi-parametric magnetic resonance imaging (mpMRI) at 1.5 T, without the use of an endorectal coil, with multiplanar images in T1WI, T2WI, DWI, and DCE. The AUC and a calibration graph were used to validate the nomogram, using the regression coefficients of the Giganti-Coppola study.

RESULTS

The original nomogram had an AUC of 0.90 (p = 0.001), with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 100%, 5.1%, 47.1%, 100%, and 48%, respectively. The calibration graph showed an overestimation of the nomogram for ECE.

CONCLUSION

The GCN has an adequate ability in predicting ECE; however, in our sample, it showed limited accuracy and overestimated likelihood of ECE in the final pathology of patients with PCa submitted to RP.

KEY POINTS

• Knowledge of preoperative local staging of prostate cancer is essential for surgical treatment. Extracapsular extension increases the chance of positive surgical margins. • Imaging modalities such as mpMRI alone does not have suitable accuracy in local staging. • Giganti-Coppola's nomogram achieved an adequate ability in predicting ECE.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Predictive model using prostate MRI findings can predict candidates for nerve sparing radical prostatectomy among low-intermediate risk prostate cancer patients

Background

In order to improve postoperative functional outcome, including urinary continence and erectile function, nerve sparing surgery is recommended for patients with clinically localized prostate cancer (PCa). However, due to poor diagnosis accuracy at the preoperative stage, upstaging occurs in a considerable proportion of patients. Multiparametric magnetic resonance imaging (mpMRI) and the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) have recently shown excellent performance in diagnosis and staging of PCa. The aim of this study was to develop a predictive model based on PI-RADS v2 for postoperative upstaging in patients with low-intermediate risk PCa.

Methods

The medical records of 314 patients with low-intermediate risk PCa [prostate-specific antigen (PSA) level ≤20 ng/mL, Gleason score (GS) <8, and clinical stage < T3] who underwent preoperative mpMRI and radical prostatectomy in the Department of Urology, Peking University First Hospital between January 2012 and July 2019 were reviewed retrospectively. Clinicopathological characteristics were collected. All MRI reports were done at our institution as part of routine clinical practice before prostate biopsy and there was no re-reporting occurred. Using PI-RADS v2, the mpMRI results were assigned to three groups: “negative”, “suspicious”, and “positive”. Multivariate logistic regression analysis was used to assess factors associated with postoperative pathological upstaging, defined as the presence of pT3 at final pathology. A regression coefficient based model for predicting postoperative upstaging was constructed and internally validated using 1,000 bootstrap resamples. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). With the optimal cutoff point the performance of the model was assessed through analysis of sensitivity, specificity, positive predictive value, and negative predictive value.

Results

Upstaging was observed in 119 (37.9%) patients. The univariate and multivariate analyses revealed that PSA density, biopsy Gleason grade group (GGG), and mpMRI findings were significantly independent predictors for postoperative upstaging (all P<0.05). A predictive model showing very favorable calibration characteristics and higher accuracy than the single variables was constructed (AUC =0.74; P<0.001). At the optimal cutoff point, the model demonstrated a sensitivity and negative predictive value of 87.4% and 87.0%, respectively.

Conclusions

PI-RADS v2 assessment proved to be one of the most valuable predictors for postoperative upstaging in patients with low-intermediate risk PCa. The predictive model, based on PI-RADS v2 assessment, PSA density, and biopsy GGG, may help to select suitable candidates for nerve sparing radical prostatectomy among patients with low-intermediate risk PCa.

Improvement of the intermediate risk prostate cancer sub-classification by integrating MRI and fusion biopsy features

INTRODUCTION

Treatment decision-making for intermediate-risk prostate cancer (CaP) is mainly based on grade and tumor involvement on systematic biopsy. We aimed to assess the added value of multi-parametric magnetic resonance imaging (mpMRI) and targeted biopsy (TB) features for predicting final pathology and for improving the well-established favourable/unfavourable systematic biopsy-based sub-classification.

MATERIALS AND METHODS

From a prospective database of 377 intermediate risk CaP cases, we evaluated the performance of the standard intermediate risk classification (IRC), and the predictive factors for unfavourable disease on final pathology aiming to build a new model. Overall unfavourable disease (OUD) was defined by any pT3-4 and/or pN1 and/or grade group (GG) ≥ 3.

RESULTS

The standard IRC was found to be predictive for unfavourable disease in this population. However, in multivariable analysis regression, ECE on mpMRI and GG ≥3 on TB remained the 2 independent predictive factors for OUD disease (HR = 2.7, P = 0.032, and HR = 2.41, P = 0.01, respectively). By using the new IRC in which unfavorable risk was defined by ECE on mpMRI and/or GG ≥3 on TB, the proportion of unfavorable cases decreased from 62.3% to 34.1% while better predicting unfavorable disease in RP speciments. The new model displayed a better accuracy than the standard IRC for predicting OUD (AUC: 0.66 vs. 0.55).

CONCLUSIONS

The integration of imaging and TB features drastically improves the intermediate risk sub-classification performance and better discriminates the unfavourable risk group that could benefit from more aggressive therapy such as neo-adjuvant and/or adjuvant treatment, and the favourable group that could avoid over-treatment. External validation in other datasets is needed.