Comparing 3-T multiparametric MRI and the Partin tables to predict organ-confined prostate cancer after radical prostatectomy

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.

Predictive Factors for Extracapsular Extension of Prostate Cancer to Select the Candidates for Nerve-sparing Radical Prostatectomy

Nerve-sparing radical prostatectomy (NSRP) for prostate cancer (PC) enables better postoperative recovery of continence and potency but may increase the risk of positive surgical margins. This study aimed to investigate preoperative predictive factors for extracapsular extension (ECE) of PC to select patients for NSRP. We retrospectively evaluated 288 patients with PC (576 lobes) diagnosed with 12-core transrectal ultrasound-guided biopsy and magnetic resonance imaging (MRI) who underwent laparoscopic or robot-assisted radical prostatectomy at our institution. Surgical specimens and preoperative parameters (prostate-specific antigen, prostate volume, biopsy and MRI findings, preoperative therapy) were analyzed. Of 576 prostate lobes, the incidence Ipsilateral ECE was identified in 97 (16.8%) lobes. The higher number of unilateral positive biopsy cores, the highest Gleason score 8 or more and positive unilateral findings on MRI are significant higher in prostate sides with ECE in univariate analysis. In multivariate analysis, positive unilateral MRI findings (odds ratio [OR], 2.86; p < 0.001) and unilateral biopsy positive core ≥ 3 (OR, 3.73; p < 0.001) were independent predictors of unilateral ECE. The detection rate of unilateral ECE in those cases with two factors (side-specific positive biopsy core 2 or less and side-specific MRI findings negative) was 7.1% (19/269). Patients with fewer unilateral positive biopsy cores and negative unilateral MRI findings might be good candidates for NSRP.

Surgeon seniority and experience have no effect on CaP detection rates using MRI/TRUS fusion-guided targeted biopsies

OBJECTIVE

To determine (i) whether urologist seniority and experience are associated with prostate cancer (CaP) and clinically significant CaP (csCaP) detection rates using magnetic resonance imaging/ultrasound (MRI/US) fusion-guided targeted biopsies, taking multiparametric magnetic resonance imaging (mpMRI) as the reference standard, and (ii) if cancer detection rates (CDR) differ across regions of the prostate using Dickinson's 27-sector map, regardless of seniority.

METHODS

We retrospectively reviewed a consecutive series of patients with suspicion of prostate cancer who underwent targeted and systematic biopsies at 1 center by 1 of 7 urologists (2 seniors and 5 juniors) between January 1, 2016 and December 31, 2021, following positive mpMRI.

RESULTS

The cohort comprised 403 patients (454 lesions) aged 67.7±6.8. The combined (junior and senior) CDR was 57% for CaP and 28% for csCaP. There were no differences in CDR between junior and senior urologists for CaP (58% vs. 55%, P = 0.538) or csCaP (29% vs. 26%, P = 0.58). A general trend was observed for the learning curve, which indicated increasing CDR with urologist experience. Across the 27 sectors, combined CDR ranged between 39% and 99% for CaP and 1% to 67% for csCaP. When grouping anterior vs. posterior sectors, there were no differences in combined CDR of CaP (64% vs. 67%, P = 0.48) and csCaP (31% vs. 38%, P = 0.19) CONCLUSIONS: Urologist seniority is not associated with CDR, urologist experience tends to improve cancer detection, and CDR does not differ between the anterior and posterior regions of the prostate.

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.

Preoperative Multiparametric Prostate Magnetic Resonance Imaging Structured Report Informs Risk for Positive Apical Surgical Margins During Radical Prostatectomy

BACKGROUND

The prostatic apex is the most frequent location of positive surgical margin (PSM) after surgery. Data regarding the ability of multiparametric magnetic resonance imaging (mpMRI) to prospectively identify men at risk for apical PSMs (aPSMs) using a structured report are lacking.

OBJECTIVES

The aims of the study are to determine and to compare the rate of aPSM in men with versus without prospectively flagged at-risk prostate lesions during clinical mpMRI interpretation using whole-mount histopathology as the reference standard.

METHODS

This single-center, retrospective study of prospectively collected data included treatment-naive men with abnormal 3T mpMRI (PI-RADS v2 score ≥3) between January 2016 and December 2018 followed by surgery. During routine clinical interpretation, radiologists flagged prostate lesions abutting the apical most gland and/or encircling the distal most prostatic urethra using standardized language available as a "pick list" option in the structured report. Logistic regression was used to compare the rate of PSM in 2 groups (flagged vs nonflagged men). Propensity score covariate adjustment corrected for potential selection bias according to age, prostate-specific antigen (PSA), PSA density, grade group, and pT stage. The estimate was further adjusted by including surgeon as a covariate.

RESULTS

A total of 428 men were included. A statistically significant higher proportion of aPSMs was noted in flagged (56% [51/91]) compared with nonflagged apical lesions (31% [105/337]; adjusted odds ratio, 2.5; 95% confidence interval, 1.6-4.1; P < 0.01). The difference in aPSM between both groups also varied according to the surgeon performing the RP. Prostate-specific antigen, PSA density, lesion size, apical location, Prostate Imaging Reporting & Data System score, grade group, pT stage, and surgeon's experience were associated with higher PSM rate. Biochemical recurrence, defined as PSA greater than 0.2 ng/mL on 2 measurements after RP, was significantly associated with PSM status (propensity score adjusted odds ratio, 3.1; 95% confidence interval, 1.8-5.3; P < 0.0001); however, patients flagged by radiologists did not have a significant difference in biochemical recurrence rates as compared with nonflagged patients ( P = 0.11).

CONCLUSIONS

Standard language built into structured reports for mpMRI of the prostate helps identify preoperatively patients at risk for aPSM.

CLINICAL IMPACT

Multiparametric MRI is able to identify patients at increased risk for aPSM, and this information can be conveyed in a structured report to urologists, facilitating patient counseling and treatment decisions.

Are staging bone scans necessary in patients with T3a prostate cancer? A multicentre study

Objective:

Studies reporting increased risk of metastases in T3a disease are based on clinical staging (Digital rectal examination) in the pre-multiparametric–magnetic resonance imaging (MRI) (mp-MRI) era. The aim of our study was to assess the rate of positive bone scans in patients ascribed with T3a prostate cancer on a pre-biopsy mp-MRI.

Methods:

We performed a multicentre, retrospective analysis of all patients with T3a prostate cancer staged by mp-MRI who had a bone scan between January 2017 and April 2020.

Results:

A total of 586 patients were diagnosed with T3a prostate cancer on mp-MRI, with a median age of 71 years (range: 47–87). The median presenting PSA was 11 ng/mL (range: 1–537); 125 patients (21.3%) had a PSA ⩽ 20 and either grade group (GG) 1 or 2 in their prostate biopsy; none of these patients had bone metastases. Eighteen patients (3.1%) were found to have bone metastases: 11 patients had GG ⩾ 3 disease on biopsy and nodal disease, 6 had GG ⩾ 3 without evidence of nodal disease and 1 had a PSA of 103.

Conclusion:

The use of bone scans in patients with T3a prostate cancer staged on mp-MRI but without other evidence of high-risk disease (GG ⩾ 3 and PSA > 20 ng/mL) appears to be unnecessary and could be safely avoided.

Level of evidence:

2b

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.

The Quantitative Assessment of Using Multiparametric MRI for Prediction of Extraprostatic Extension in Patients Undergoing Radical Prostatectomy: A Systematic Review and Meta-Analysis

PURPOSE

To investigate the diagnostic performance of using quantitative assessment with multiparametric MRI (mpMRI) for prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa).

METHODS

We performed a computerized search of MEDLINE, Embase, Cochrane Library, Web of Science, and Google Scholar from inception until July 31, 2021. Summary estimates of sensitivity and specificity were pooled with the bivariate model, and quality assessment of included studies was performed with the Quality Assessment of Diagnostic Accuracy Studies-2. We plotted forest plots to graphically present the results. Multiple subgroup analyses and meta-regression were performed to explore the variate clinical settings and heterogeneity.

RESULTS

A total of 23 studies with 3,931 participants were included. The pooled sensitivity and specificity for length of capsular contact (LCC) were 0.79 (95% CI 0.75-0.83) and 0.77 (95% CI 0.73-0.80), for apparent diffusion coefficient (ADC) were 0.71 (95% CI 0.50-0.86) and 0.71 (95% CI 059-0.81), for tumor size were 0.62 (95% CI 0.57-0.67) and 0.75 (95% CI 0.67-0.82), and for tumor volume were 0.77 (95% CI 0.68-0.84) and 0.72 (95% CI 0.56-0.83), respectively. Substantial heterogeneity was presented among included studies, and meta-regression showed that publication year (≤2017 vs. >2017) was the significant factor in studies using LCC as the quantitative assessment (P=0.02).

CONCLUSION

Four quantitative assessments of LCC, ADC, tumor size, and tumor volume showed moderate to high diagnostic performance of predicting EPE. However, the optimal cutoff threshold varied widely among studies and needs further investigation to establish.

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

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

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.

Micro-Ultrasound: a way to bring imaging for prostate cancer back to urology

Only a decade ago, there were insufficient imaging options for the detection and local staging of prostate cancer. However, the introduction of multiparametric magnetic resonance imaging (mpMRI) has advanced a much-needed tool for this scope of application. The possibilities and limitations of mpMRI have been well studied. Imaging must be acquired and evaluated using a standardized protocol (the latest version of Prostate Imaging-Reporting and Data System). Sensitivity has been shown to increase with higher grades and larger tumors, and while the detection rate on a per patient basis is relatively high, the per-lesion detection rate is far inferior. Various specialists have attempted to elevate the use of transrectal ultrasound, a tool frequently used by all urologists. Encouragement for this idea comes from a recently introduced system of high frequency transrectal ultrasound. The level of evidence supporting its use in the detection and staging of prostate cancer is not comparable with mpMRI yet, but initial prospective studies indicate good potential. The sensitivity of micro-ultrasound and mpMRI for clinically significant prostate cancer ranges from 94% to 100% and from 88% to 90%, respectively. Further areas of application, such as local staging for prostate and bladder cancer, are currently being evaluated. In summary, microultrasound presents a promising technology for further improving urological imaging and allows for the possibility of returning prostate cancer imaging to urologists. This review will summarize the current scientific basis for the use of micro-ultrasound in the detection of prostate cancer.

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.

Extracapsular extension on multiparametric magnetic resonance imaging better predicts pT3 disease at radical prostatectomy compared to perineural invasion on biopsy

INTRODUCTION

Risk assessment for non-organ-confined prostate cancer (PCa) is important in the surgical planning for radical prostatectomy (RP). Perineural invasion (PNI) on prostate biopsy has been associated with adverse pathological outcomes at prostatectomy. Similarly, the identification of suspected extracapsular extension (ECE) on multiparametric magnetic resonance imaging (mpMRI) has been shown to predict non-organ-confined disease. However, no prior study has compared these factors in predicting adverse pathology at prostatectomy. We evaluated mpMRI ECE and prostate biopsy PNI on multivariable analysis to determine their ability to predict pathological stage at time of RP.

METHODS

We retrospectively investigated the prostatectomy database at our institution to identify men who underwent prostate biopsy with pre-biopsy mpMRI and subsequent RP from 2013-2017. Multivariable regression analysis was performed to compare the association of mpMRI ECE (mECE) and PNI on prostate biopsy on the likelihood of finding pT3 disease on pathology post-prostatectomy.

RESULTS

Of a total 454 RP between 2013 and 2017, 191 patients met our inclusion criteria. Stage pT2 and pT3+ were found in 120 (62.8%) and 71 (37.2%) patients, respectively. Patients with mECE had 4.84 cumulative odds of worse pathological stage on RP (p=0.045) compared to PNI on biopsy, which showed cumulative odds of 2.25 (p=0.048). When controlling only for those patients without PNI, mECE was still found to be a significant predictor of pT3 disease at RP (p=0.030); however, in patients without mECE, PNI was not significant (p=0.062).

CONCLUSIONS

While mECE and biopsy PNI were both associated with worse pathological stage on RP, mECE had significantly higher cumulative odds compared to PNI. The significant predictive ability of mECE adds further clinical value to the use of mpMRI in PCa management. While validation in a larger cohort is required, these factors have important clinical implications with regards to early diagnosis of advanced disease and surgical planning.

Development of an Indian nomogram for predicting extracapsular extension in prostate cancer

Introduction

The aim of our study was to develop a new Indian nomogram to estimate pathologic extracapsular extension (ECE) risk in prostate cancer, by including PI-RADS v1-based magnetic resonance imaging (MRI) ECE risk score to the clinical variables used in the Partin nomogram (PN).

Materials and Methods

We analyzed 273 patients who underwent MRI of prostate and radical prostatectomy (RP). Univariate and multivariate logistic regression analyses were performed to identify predictors of ECE. We calculated the area under the receiver operating characteristic curve (AUC) for three variables used in PN and MRI ECE risk score, and a new nomogram was designed using binary logistic regression. Calibration curves assessed the agreement between the actual ECE risk and the predicted probability of the new nomogram.

Results

Out of 273 patients, 123 patients (45.1) had ECE on MRI, whereas 136 patients (49.8) had ECE on final pathology. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI for predicting ECE were 76.6, 66.9, 70.0, 73.9, and 71.7 (confidence interval 95), respectively. Multivariate logistic regression analyses showed that clinical T-stage (cT), Gleason score (GS), and MRI ECE risk score remained significant. The highest and the lowest values of the AUC for single variables were 0.748 (MRI ECE risk score) and 0.636 (cT stage), respectively, and AUC for PN was 0.67. New nomogram designed using R statistical package has higher predictive accuracy (0.826) compared to PN (0.67) and good calibration.

Conclusions

MRI adds incremental value to PN. A new Indian nomogram can help in the decision-making process of nerve-sparing RP. This nomogram should be used with caution as validation is pending and will require further studies.

Extraprostatic extension in prostate cancer: primer for radiologists

The presence of extraprostatic extension (EPE) on multiparametric MRI (mpMRI) is an important factor in determining the management of prostate cancer. EPE is an established risk factor for biochemical recurrence of prostate cancer after radical prostatectomy (RP) and patients with EPE may be considered for wider resection margins, non-nerve-sparing surgery, adjuvant radiation therapy (RT), or androgen deprivation therapy (ADT). Several statistical nomograms and scoring systems have been developed to predict pathological stage at time of RP but with varying accuracies. Using the current PI-RADS v2 mpMRI staging guidelines results in high specificity but lacks in sensitivity. These findings reveal the need for more standardization and further refinement of existing MRI protocols and prostate cancer prediction tools. Current studies have looked into indirect additional imaging criteria such as index tumor volume, length of capsular contact, and apparent diffusion coefficient. Measuring for these features can improve the robustness of mpMRI in staging prostate cancer, as they have been shown to be independent predictors of EPE. MRI/ultrasound fusion-guided targeted biopsy can detect EPE not found on standard biopsy. Collectively, these measurements and imaging techniques can augment the detection of EPE and subsequent risk stratification.

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.

Value of adding the apparent diffusion coefficient to capsular contact for the prediction of extracapsular extension in prostate cancer

Objective

To determine whether evaluating the mean apparent diffusion coefficient (ADC) together with capsular contact (CC) adds value in the prediction of microscopic extracapsular extension (ECE) of prostate cancer.

Materials and Methods

Between January 2012 and December 2016, 383 patients underwent multiparametric magnetic resonance imaging (mpMRI) of the prostate. A total of 67 patients were selected for inclusion. Two radiologists (observers 1 and 2), working independently, performed qualitative and quantitative analyses of ECE, macroscopic ECE, and microscopic ECE. A third radiologist assessed the correlation with the clinical data, and two experienced pathologists reviewed all histopathological findings.

Results

Among the 67 patients, mpMRI showed lesions that were confined to the capsule in 44 (66.7%), had microscopic ECE in 12 (17.9%), and had macroscopic ECE in 11 (16.4%). There were no significant differences, in terms of the diagnostic accuracy, as measured by determining the area under the curve (AUC), of CC on T2-weighted images (CCT2), CC on diffusion-weighted imaging (CCDWI), and the mean ADC for the prediction of microscopic ECE, between observer 1 (AUC of 0.728, 0.691, and 0.675, respectively) and observer 2 (AUC of 0.782, 0.821, and 0.799, respectively). Combining the mean ADC with the CCT2 or CCDWI did not improve the diagnostic accuracy for either observer. There was substantial interobserver agreement for the qualitative evaluation of ECE, as demonstrated by the kappa statistic, which was 0.77 (0.66-0.87). The diagnostic accuracy (AUC) of the qualitative assessment for predicting microscopic ECE was 0.745 for observer 1 and 0.804 for observer 2, and the difference was less than significant. In a multivariate analysis, none of clinical or imaging parameters were found to be associated with ECE.

Conclusion

For the detection of microscopic ECE on mpMRI, CC appears to have good diagnostic accuracy, especially if the observer has considerable experience. Adding the mean ADC to the CCT2 or CCDWI does not seem to provide any significant improvement in that diagnostic accuracy.

Prostate cancer local staging using biparametric MRI: assessment and comparison with multiparametric MRI

PURPOSE

The value of adding dynamic contrast-enhanced (DCE) imaging to T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) to improve the detection and staging of prostate cancer (PCa) is unclear. The aim of this retrospective study was to compare the diagnostic performance of non-contrast biparametric MRI (bpMRI) with multiparametric MRI (mpMRI), for local staging of PCa.

METHODS

Ninety-two patients who underwent prostate MRI on a 3-Tesla MRI system before radical prostatectomy for PCa were included retrospectively. Four readers independently assigned a Likert score (ranging from 1 to 5) for predicting extra-prostatic extension (EPE) on T2W + DWI (bpMRI) and then on T2W + DWI + DCE imaging (mpMRI). MRI-based staging results were compared with radical prostatectomy histology. A prediction of EPE generalized linear mixed model was used to assess the added-value of DCE and discriminative power of staging accuracy by area under the receiver-operating curve (AUC ROC).

RESULTS

AUC was not significantly improved by DCE (mpMRI, AUC = 0.73 [95%CI: 0.655‒0.827] vs. bpMRI, AUC = 0.76 [95%CI: 0.681‒0.846]). After applying a selection procedure, only MRI criteria were retained in a multivariate model. The following criteria were significantly associated with local extension: localization in the peripheral zone (p < 0.001), maximal diameter of the lesion (<0.0001), curvilinear capsular contact on T2W (p < 0.0001), capsular irregularity on T2W (p < 0.0001), bulging on T2W (p < 0.001) and seminal vesicle hypo-signal (p < 0.001).

CONCLUSION

Use of bpMRI did not result in a decrease in local staging accuracy.

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.

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.

Prostate MRI: staging and decision-making

Multi-parametric prostate MRI (mpMRI) plays a critical role in the diagnosis, staging, and evaluation of treatment response in patients with prostate cancer. Radiologists, through an accurate and standardized interpretation of mpMRI, can clinically stage prostate cancer and help to risk stratify patients who may benefit from more invasive treatment or exclude patients who may be harmed by overtreatment. The purpose of this article is to describe key findings to accurately stage prostate cancer with mpMRI and to describe the contexts in which mpMRI is best applied.

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.

Predicting side-specific prostate cancer extracapsular extension: a simple decision rule of PSA, biopsy, and MRI parameters

Objective

To develop an easy-to-use side-specific tool for the prediction of prostate cancer extracapsular extension (ECE) using clinical, biopsy, and MRI parameters.

Materials and methods

Retrospective analysis of patients who underwent radical prostatectomy preceded by staging multiparametric MRI of the prostate was performed. Multivariate logistic regression analysis was used to choose independent predictors of ECE. Continuous variables were transformed to categorical ones by choosing threshold values using spline knots or testing thresholds used in previously described models. Internal validation of the rule was carried out as well as validation of other algorithms on our group was performed.

Results

In the analyzed period of time, 88 out of 164 patients who underwent radical prostatectomy met inclusion criteria. ECE was evidenced at radical prostatectomy in 41 patients (46.6%) and in 53 lobes (30.1%). In the multivariate analysis PSA, total percentage of cancerous tissue in cores (%PCa) and maximum tumour diameter (MTD) of Likert 3–5 lesions on MRI were independent predictors of ECE. The following rule for predicting side-specific ECE was proposed: %PCa ≥ 15% OR MTD ≥ 15 mm OR PSA ≥ 20 ng/mL. Internal validation of the algorithm revealed safe lower confidence limits for sensitivity and NPV, proving that model offers accurate risk grouping that can be safely used in decision-making.

Conclusion

The rule developed in this study makes ECE prediction fast, intuitive, and side-specific. However, until validated externally it should be used with caution.

Multiparametric MRI - local staging of prostate cancer and beyond

Background Accurate local staging is critical for treatment planning and prognosis in patients with prostate cancer (PCa). The primary aim is to differentiate between organ-confined and locally advanced disease with the latter carrying a worse clinical prognosis. Multiparametric MRI (mpMRI) is the imaging modality of choice for the local staging of PCa and has an incremental value in assessing pelvic nodal disease and bone involvement. It has shown superior performance compared to traditional staging based on clinical nomograms, and provides additional information on the site and extent of disease. MRI has a high specificity for diagnosing extracapsular extension (ECE), seminal vesicle invasion (SVI) and lymph node (LN) metastases, however, sensitivity remains poor. As a result, extended pelvic LN dissection remains the gold standard for assessing pelvic nodal involvement, and there has been recent progress in developing advanced imaging techniques for more distal staging. Conclusions T2W-weighted imaging is the cornerstone for local staging of PCa. Imaging at 3T and incorporating both diffusion weighted and dynamic contrast enhanced imaging can further increase accuracy. "Next generation" imaging including whole body MRI and PET-MRI imaging using prostate specific membrane antigen (68Ga-PSMA), has shown promising for assessment of LN and bone involvement as compared to the traditional work-up using bone scintigraphy and body CT.

Apparent diffusion coefficient in extraprostatic extension of prostate cancer: a systematic review and diagnostic meta-analysis

Objective: To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) for local staging of prostate cancer. Methods: Databases of Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar were searched up to May 31, 2018, with language restricted to English. All studies concerning multiparametric magnet resonance imaging (mpMRI) with ADC for detection of extracapsular extension (ECE, T3a) and/or extraprostatic extension (EPE, overall stage of T3) were identified by two reviewers independently, and quality of included studies was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 tool. True positive, false positive, false negative and true negative of each study were extracted to reconstruct the 2×2 tables for evaluating diagnostic accuracy. Summary estimates of sensitivity, specificity, and corresponding 95% CIs were calculated with bivariate model and hierarchical summary receiver operating characteristic model, then presented in forest plots. Multiple subgroup analyses and meta-regression were performed, and publication bias was evaluated with Deeks funnel. Results: A total of 18 studies were included, with 6 involved ECE and 12 for EPE. Pooled sensitivity was 80.5% (95% CI 76.5-83.9%) with specificity of 69.1% (95% CI 62.3-75.2%). Multiple subgroup analyses showed that if ADC and length of capsular contact are regarded as independent predictors, pooled sensitivity was 85% (95% CI 77-90%) and 81.1% (95% CI 76.0-85.3%), with specificity of 70.8% (95% CI 56.3-82.0%) and 66.6% (95% CI 57.6-74.5%), respectively. Meta-regression demonstrated that there was no substantially significant difference in types of coil, magnet field strength (1.5T versus 3.0T), and analysis method (per-lesion versus per-patient). Conclusion: By introducing ADC to MRI, we could obtain favorable sensitivity for diagnostic performance of EPE, but with a little decreased specificity.

Head-to-head comparison between multiparametric MRI, the partin tables, memorial sloan kettering cancer center nomogram, and CAPRA score in predicting extraprostatic cancer in patients undergoing radical prostatectomy

BACKGROUND

It is unclear whether clinical models including the Partin tables (PT), the Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and the cancer of the prostate risk assessment (CAPRA) can benefit from incorporating multiparametric magnetic resonance imaging (mpMRI) when staging prostate cancer (PCa).

PURPOSE

To compare the accuracy of clinical models, mpMRI, and mpMRI plus clinical models in predicting stage ≥pT3 of PCa.

STUDY TYPE

Prospective monocentric cohort study.

POPULATION

Seventy-three patients who underwent radical prostatectomy between 2016-2018.

FIELD STRENGTH/SEQUENCE

3.0T using turbo spin echo (TSE) imaging, single-shot echoplanar diffusion-weighted imaging, and T₁ -weighted high-resolution-isotropic-volume-examination (THRIVE) contrast-enhanced imaging.

ASSESSMENT

We calculated the probability of extraprostatic extension (EPE) using the PT and MSKCC, as well as the CAPRA score. Three readers with 2-8 years of experience in mpMRI independently staged PCa on imaging.

STATISTICAL TESTS

Receiver operating characteristics analysis and logistic regression analysis to investigate the per-patient accuracy of mpMRI vs. clinical models vs. mpMRI plus clinical models in predicting stage ≥pT3. The alpha level was 0.05.

RESULTS

Median probability for EPE and MSKCCn was 27.3% and 47.0%, respectively. Median CAPRA score was 3. Stage ≥pT3 occurred in 32.9% of patients. Areas under the curve (AUCs) were 0.62 for PT, 0.62 for MSKCCn, 0.64 for CAPRA, and 0.73-0.75 for mpMRI (readers 1-3) (P > 0.05 for all comparisons). Compared with mpMRI, the combination of mpMRI with PT or MSKCCn provided lower AUCs (P > 0.05 for all the readers), while the combination with CAPRA provided significantly higher (P < 0.05) AUCs in the case of readers 1 and 3. On multivariable analysis, mpMRI by reader 1 was the only independent predictor of stage ≥pT3 (odds ratio 7.40). DATA CONCLUSION: mpMRI was more accurate than clinical models and mpMRI plus clinical models in predicting stage ≥pT3, except for the combination of mpMRI and CAPRA in two out of three readers.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1604-1613.

A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI

Purpose To evaluate MRI features associated with pathologically defined extraprostatic extension (EPE) of prostate cancer and to propose an MRI grading system for pathologic EPE. Materials and Methods In this prospective study, consecutive male study participants underwent preoperative 3.0-T MRI from June 2007 to March 2017 followed by robotic-assisted laparoscopic radical prostatectomy. An MRI-based EPE grading system was defined as follows: curvilinear contact length of 1.5 cm or capsular bulge and irregularity were grade 1, both features were grade 2, and frank capsular breach were grade 3. Multivariable logistic regression and decision curve analyses were performed to compare the MRI grade model and clinical parameters (prostate-specific antigen, Gleason score) for pathologic EPE prediction by using the area under the receiver operating characteristic curve (AUC) value. Results Among 553 study participants, the mean age was 60 years ± 8 (standard deviation); the median prostate-specific antigen value was 6.3 ng/mL. A total of 125 of 553 (22%) participants had pathologic EPE at radical prostatectomy. Detection of pathologic EPE, defined as number of pathologic EPEs divided by number of participants with individual MRI features, was as follows: curvilinear contact length, 88 of 208 (42%); capsular bulge and irregularity, 78 of 175 (45%); and EPE visible at MRI, 37 of 56 (66%). For MRI, grades 1, 2, and 3 for detection of pathologic EPE were 18 of 74 (24%), 39 of 102 (38%), and 37 of 56 (66%), respectively. Clinical features plus the MRI-based EPE grading system (prostate-specific antigen, International Society of Urological Pathology stage, MRI grade) predicted pathologic EPE better than did MRI grade alone (AUC, 0.81 vs 0.77, respectively; P < .001). Conclusion Higher MRI-based extraprostatic extension (EPE) grading categories were associated with a greater risk of pathologic EPE. Clinical features plus MRI grading had the highest diagnostic performance for prediction of pathologic EPE. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Eberhardt in this issue.

Adding multiparametric MRI to the MSKCC and Partin nomograms for primary prostate cancer: Improving local tumor staging?

INTRODUCTION AND OBJECTIVES

As a single diagnostic modality, multiparametric MRI (mpMRI) has imperfect accuracy to detect locally advanced prostate cancer (T-stages 3-4). In this study we evaluate if combining mpMRI with preoperative nomograms (Memorial Sloan Kettering Cancer Center [MSKCC] and Partin) improves the prediction of locally advanced tumors.

MATERIALS AND METHODS

Preoperative mpMRI results of 430 robot-assisted radical prostatectomy patients were analyzed. MSKCC and Partin nomogram scores predicting extraprostatic growth were calculated. Logistic regression analysis was performed, combining the nomogram prediction scores with mpMRI results. The diagnostic value of the combined models was evaluated by creating receiver operator characteristics curves and comparing the area under the curve (AUC).

RESULTS

mpMRI was a significant predictor of locally advanced disease in addition to both the MSKCC and Partin nomogram, despite its low sensitivity (45.3%). However, overall predictive accuracy increased by only 1% when mpMRI was added to the MSKCC nomogram (AUC MSKCC 0.73 vs MSKCC + mpMRI 0.74). Predictive accuracy for the Partin Tables increased 4% (AUC Partin 0.62 vs Partin + mpMRI 0.66).

CONCLUSION

The addition of mpMRI to the preoperative MSKCC and Partin nomograms did not increase diagnostic accuracy for the prediction of locally advanced prostate cancer.

The current role of prostate multiparametric magnetic resonance imaging

Prostate multi-parametric magnetic resonance imaging (mpMRI) has shown excellent sensitivity for Gleason ≥7 cancers, especially when their volume is ≥0.5 mL. As a result, performing an mpMRI before prostate biopsy could improve the detection of clinically significant prostate cancer (csPCa) by adding targeted biopsies to systematic biopsies. Currently, there is a consensus that targeted biopsies improve the detection of csPCa in the repeat biopsy setting and at confirmatory biopsy in patients considering active surveillance. Several prospective multicentric controlled trials recently showed that targeted biopsy also improved csPCa detection in biopsy-naïve patients. The role of mpMRI and targeted biopsy during the follow-up of active surveillance remains unclear. Whether systematic biopsy could be omitted in case of negative mpMRI is also a matter of controversy. mpMRI did show excellent negative predictive values (NPV) in the literature, however, since NPV depends on the prevalence of the disease, negative mpMRI findings should be interpreted in the light of a priori risk for csPCa of the patient. Nomograms combining mpMRI findings and classical risk predictors (age, prostate-specific antigen density, digital rectal examination, etc.) will probably be developed in the future to decide whether a prostate biopsy should be obtained. mpMRI has a good specificity for detecting T3 stage cancers, but its sensitivity is low. It should therefore not be used routinely for staging purposes in low-risk patients. Nomograms combining mpMRI findings and other clinical and biochemical data will also probably be used in the future to better assess the risk of T3 stage disease.

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

PURPOSE

We examined the additional value of preoperative prostate multiparametric magnetic resonance imaging and transrectal ultrasound/multiparametric magnetic resonance imaging fusion guided targeted biopsy when performed in combination with clinical nomograms to predict adverse pathology at radical prostatectomy.

MATERIALS AND METHODS

We identified all patients who underwent 3 Tesla multiparametric magnetic resonance imaging prior to fusion biopsy and radical prostatectomy. The Partin and the MSKCC (Memorial Sloan Kettering Cancer Center) preradical prostatectomy nomograms were applied to estimate the probability of organ confined disease, extraprostatic extension, seminal vesicle invasion and lymph node involvement using transrectal ultrasound guided systematic biopsy and transrectal ultrasound/multiparametric magnetic resonance imaging fusion guided targeted biopsy Gleason scores. With radical prostatectomy pathology as the gold standard we developed multivariable logistic regression models based on these nomograms before and after adding multiparametric magnetic resonance imaging to assess any additional predictive ability.

RESULTS

A total of 532 patients were included in study. When multiparametric magnetic resonance imaging findings were added to the systematic biopsy based MSKCC nomogram, the AUC increased by 0.10 for organ confined disease (p <0.001), 0.10 for extraprostatic extension (p = 0.003), 0.09 for seminal vesicle invasion (p = 0.011) and 0.06 for lymph node involvement (p = 0.120). Using Gleason scores derived from targeted biopsy compared to systematic biopsy provided an additional predictive value of organ confined disease (Δ AUC 0.07, p = 0.003) and extraprostatic extension (Δ AUC 0.07, p = 0.048) at radical prostatectomy with the MSKCC nomogram. Similar results were obtained using the Partin nomogram.

CONCLUSIONS

Magnetic resonance imaging alone or in addition to standard clinical nomograms provides significant additional predictive ability of adverse pathology at the time of radical prostatectomy. This information can be greatly beneficial to urologists for preoperative planning and for counseling patients regarding the risks of future therapy.

Prostate Biopsy in Active Surveillance Protocols: Immediate Re-biopsy and Timing of Subsequent Biopsies

PURPOSE OF REVIEW

This manuscript reviews contemporary literature regarding prostate cancer active surveillance (AS) protocols as well as other tools that may guide the management of biopsy frequency and assess the possibility of progression in low-risk prostate cancer.

RECENT FINDINGS

There is no consensus regarding the timing of surveillance biopsies; however, an immediate repeat biopsy within 12 months of diagnosis for patients considering AS confirms patients who have favorable risk disease yet also identifies patients who were undersampled initially. Studies regarding multiparametric MRI, nomograms, and biomarkers show promise in risk stratifying and counseling patients during AS. Further studies are needed to determine if these supplemental tests can decrease the frequency of surveillance biopsies. An immediate re-biopsy can help to reduce the risk of missing clinically significant disease. Other clinical tools, including mpMRI, exist that can be used as an adjunct to counsel patients and guide a personalized discussion regarding the frequency of surveillance biopsies.

MR Imaging for Prostate Cancer Screening and Active Surveillance

The current prostate cancer management paradigm has been criticized in recent years for contributing to the overdiagnosis and overtreatment of the disease. Active surveillance is an avenue by which to reduce overtreatment, but patient selection and monitoring remain a challenge. The use of prostate MR imaging has been growing in recent years and has been incorporated into prostate cancer screening and patient selection and monitoring for active surveillance. This review article discusses the current evidence for the use of MR imaging in each of those settings.

Prostate Magnetic Resonance Imaging Provides Limited Incremental Value Over the Memorial Sloan Kettering Cancer Center Preradical Prostatectomy Nomogram

OBJECTIVE

To examine the incremental value of prostate magnetic resonance imaging (MRI) when used in combination with the currently available preoperative risk stratification tool, the Memorial Sloan Kettering Cancer Center (MSKCC) preradical prostatectomy nomogram.

MATERIALS AND METHODS

We reviewed our institutional database of prostate MRI performed before radical prostatectomy between December 2014 and March 2016 (n = 236). We generated a logistic regression model based on observed final pathology results and the MSKCC nomogram predictions for organ-confined disease, extracapsular extension (ECE), seminal vesicle invasion, and lymph node involvement (LNI) ("MSKCC only"). We then generated a combined regression model incorporating both the MSKCC nomogram prediction with the degree of prostate MRI suspicion ("MSKCC + MRI"). Receiver operating characteristic curves were generated, and the area under the curves (AUCs) were compared.

RESULTS

When independently examining the MSKCC nomogram predicted risk and the degree of prostate MRI suspicion, MRI was a predictor for ECE (odds ratio 2.8, P <.01) and LNI (odds ratio 5.6, P = .01). When examining the "MSKCC + MRI" and "MSKCC only" models, the incremental benefit in risk discrimination from the combined model ("MSKCC + MRI") was not significant for organ-confined disease, ECE, seminal vesicle invasion, or LNI (ΔAUC +0.03, P = .10; ΔAUC +0.03, P = .08; ΔAUC 0.63, P = .63; ΔAUC +0.04, P = .42; respectively).

CONCLUSION

A combined model with prostate MRI and the MSKCC nomogram provides no additional risk discrimination over the MSKCC nomogram-based model alone. Evaluation of prostate MRI as a predictive tool should be performed in combination with, not independent of, these clinical risk stratification models.

Seminal vesicle invasion on multi-parametric magnetic resonance imaging: Correlation with histopathology

OBJECTIVES

The pre-treatment risk of seminal vesicle (SV) invasion (SVI) from prostate cancer is currently based on nomograms which include clinical stage (cT), Gleason score (GS) and prostate-specific antigen (PSA). The aim of our study was to evaluate the staging accuracy of 3T (3T) multi-parametric (mp) Magnetic Resonance Imaging (MRI) by comparing the imaging report of SVI with the tissue histopathology. The additional value in the existing prediction models and the role of radiologists' experience were also examined.

METHODS

After obtaining institutional review board approval, we retrospectively reviewed clinico-pathological data from 527 patients who underwent a robot-assisted radical prostatectomy (RARP) between January 2012 and March 2015. Preoperative prostate imaging with an endorectal 3T-mp-MRI was performed in all patients. Sequences consisted of an axial pre-contrast T1 sequence, three orthogonally-oriented T2 sequences, axial diffusion weighted and dynamic contrast-enhanced sequences. We considered SVI in case of low-signal intensity in the SV on T2-weighted sequences or apparent mass while diffusion-weighted and DCE sequences were used to confirm findings on T2. Whole-mount section pathology was performed in all patients. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of MRI (index test) for the prediction of histological SVI (reference standard) were calculated. We developed logistic multivariable regression models including: clinical variables (PSA, cT, percentage of involved cores/total cores, primary GS 4-5) and Partin table estimates. MRI results (negative/positive exam) were then added in the models and the multivariate modeling was reassessed. In order to assess the extent of SVI and the reason for mismatch with pathology an MRI-review from an expert genitourinary radiologist was performed in a subgroup of 379 patients.

RESULTS

A total of 54 patients (10%) were found to have SVI on RARP-histopathology. In the overall cohort sensitivity, specificity, PPV and NPV for SVI detection on MRI were 75.9%, 94.7%, 62% and 97% respectively. Based on our sub-analysis, the radiologist's expertise improved the accuracy demonstrating a sensitivity, specificity, PPV and NPV of 85.4%, 95.6%, 70.0% and 98.2%, respectively. In the multivariate analysis PSA (odds ratio [OR] 1.07, p=0.008), primary GS 4 or 5 (OR 3.671, p=0.007) and Partin estimates (OR 1.07, p=0.023) were significant predictors of SVI. When MRI results were added to the analysis, a highly significant prediction of SVI was observed (OR 45.9, p<0.0001). Comparing Partin, MRI and Partin with MRI predictive models, the areas under the curve were 0.837, 0.884 and 0.929, respectively.

CONCLUSIONS

MRI had high diagnostic accuracy for SVI on histopathology. It provided added diagnostic value to clinical/Partin based SVI-prediction models alone. A key factor is radiologist's experience, though no inter-observer variability could be examined due to the availability of a single expert radiologist.

A novel tool for predicting extracapsular extension during graded partial nerve sparing in radical prostatectomy

OBJECTIVES

To create a statistical tool for the estimation of extracapsular extension (ECE) level of prostate cancer and determine the nerve-sparing (NS) approach that can be safely performed during radical prostatectomy (RP).

PATIENTS AND METHODS

A total of 11 794 lobes, from 6 360 patients who underwent robot-assisted RP between 2008 and 2016 were evaluated. Clinicopathological features were included in a statistical algorithm for the prediction of the maximum ECE width. Five multivariable logistic models were estimated for: presence of ECE and ECE width of >1, >2, >3, and >4 mm. A five-zone decision rule based on a lower and upper threshold is proposed. Using a graphical interface, surgeons can view patient's pre-treatment characteristics and a curve showing the estimated probabilities for ECE amount together with the areas identified by the decision rule.

RESULTS

Of the 6 360 patients, 1 803 (28.4%) were affected by non-organ-confined disease. ECE was present in 1 351 lobes (11.4%) and extended beyond the capsule for >1, >2, >3, and >4 mm in 498 (4.2%), 261 (2.2%), 148 (1.3%), 99 (0.8%) cases, respectively. ECE width was up to 15 mm (interquartile range 1.00-2.00). The five logistic models showed good predictive performance, the area under the receiver operating characteristic curve was: 0.81 for ECE, and 0.84, 0.85, 0.88, and 0.90 for ECE width of >1, >2, >3, and >4 mm, respectively.

CONCLUSION

This novel tool predicts with good accuracy the presence and amount of ECE. Furthermore, the graphical interface available at www.prece.it can support surgeons in patient counselling and preoperative planning.

Development and comparison of a Chinese nomogram adding multi-parametric MRI information for predicting extracapsular extension of prostate cancer

PURPOSE

To improve the performation of a nomogram for predicting side-specific extracapsular extension (SS-ECE).

RESULTS

One hundred and ninety-six patients (55.5%) had ECE on final pathology. Bilateral and unilateral ECE rate was 13.9% (49/353) and 41.6% (147/353), respectively. The mean age was 65.9 years and the mean serum prostate specific antigen (PSA) was 15.0 ng/ml. Based on multivariate logistic regression analysis, clinical stage (cStage), PSA, Gleason sum, percentage of positive cores, and ECE risk score were significant predictors of ECE. The current nomogram had higher predictive accuracy (0.851) and superior calibration. According to the decision curve analysis (DCA) results, the updated nomogram demonstrated a high net benefit across a wide range of threshold probabilities.

MATERIALS AND METHODS

We studied 353 patients with cStage T1c-T3 prostate cancer underwent radical prostatectomy. The candidate predictors associated with ECE were cStage, PSA, Gleason sum, percentage of positive cores, maximum cancer percentage and ECE risk score from multi-parametric magnetic resonance imaging (MP-MRI). The receiver operating characteristic (ROC) analysis was performed and an updated nomogram was constructed. The DCA was performed to test the predictive ability of the nomogram. In addition, the validation and calibration of the Memorial Sloan-Kettering cancer center (MSKCC) nomograms were performed in the current subjects.

CONCLUSIONS

Predictors, including cStage, PSA, Gleason sum, percentage of positive cores, maximum cancer percentage, and ECE risk score, were combined to construct a SS-ECE prediction nomogram. And the current nomogram might help urologists in decision-making process of preserving or resecting neurovascular bundles preoperatively.

Association between a 17-gene genomic prostate score and multi-parametric prostate MRI in men with low and intermediate risk prostate cancer (PCa)

Background

We aimed to directly compare results from multi-parametric prostate MRI (mpMRI) and a biopsy-based 17-gene RT-PCR assay providing a Genomic Prostate Score (GPS) among individuals who were candidates for active surveillance with low and intermediate risk prostate cancer (PCa).

Patients and methods

We evaluated the association between GPS results (scale 0–100) and endorectal mpMRI findings in men with clinically localized PCa. MR studies were reviewed to a five-tier scale of increasing suspicion of malignancy. Mean apparent diffusion coefficient (ADC) was calculated from a single dominant lesion. Mean rank of the GPS (0–100) among MRI strata was compared with the Kruskal-Wallis test and Dunn's multiple comparison test. Spearman's correlation was performed to examine the association between mean ADC and scaled GPS.

Results

Of 186 patients who received GPS testing, 100 were identified who received mpMRI. Mean GPS results differed between mpMRI categories (p = 0.001); however a broad range was observed in all mpMRI categories. Among men with biopsy Gleason pattern 3+3, mean GPS results were not significantly different among MRI groups (p = 0.179), but GPS differences were seen among MRI categories for patients with pattern 3+4 (p = 0.010). Mean ADC was weakly associated with GPS (σ = -0.151). Stromal response (p = 0.015) and cellular organization (p = 0.045) gene group scores differed significantly by MRI findings, but no differences were seen among androgen signaling or proliferation genes.

Conclusions

Although a statistically significant association was observed between GPS results and MRI scores, a wide range of GPS values were observed across imaging categories suggesting that mpMRI and genomic profiling may offer non- overlapping clinical insights.

Role of prostate magnetic resonance imaging in active surveillance

Active surveillance (AS) has emerged as a beneficial strategy for management of low risk prostate cancer (PCa) and prevention of overtreatment of indolent disease. However, selection of patients for AS using traditional 12-core transrectal prostate biopsy is prone to sampling error and presents a challenge for accurate risk stratification. In fact, around a third of men are upgraded on repeat biopsy which disqualifies them as appropriate AS candidates. This uncertainty affects adoption of AS among patients and physicians, leading to current AS protocols involving repetitive prostate biopsies and unclear triggers for progression to definitive treatment. Prostate magnetic resonance imaging (MRI) has the potential to overcome some of these limitations through localization of significant tumors in the prostate. In conjunction with MRI-targeted prostate biopsy, improved sampling and detection of clinically significant PCa can help streamline the process of selecting suitable men for AS and early exclusion of men who require definitive treatment. MRI can also help minimize the invasive nature of monitoring for disease progression while on AS. Men with stable MRI findings have high negative predictive value for Gleason upgrade on subsequently biopsy, suggesting that men may potentially be monitored by serial MRI examinations with biopsy reserved for significant changes on imaging. Targeted biopsy on AS also allows for specific sampling of concerning lesions, although further data is necessary to evaluate the relative contribution of systematic and targeted biopsy in detecting the 25-30% of men who progress on AS. Further research is also warranted to better understand the nature of clinically significant cancers that are missed on MRI and why certain men have progression of disease that is not visible on prostate MRI. Consensus is also needed over what constitutes progression on MRI, when prostate biopsy can be safely avoided, and how to best utilize this additional information in current AS protocols. Despite these challenges, prostate MRI, either alone or in conjunction with MRI-targeted prostate biopsy, has the potential to significantly improve our current AS paradigm and rates of AS adoption among patients moving forward.

Multi-parametric magnetic resonance imaging as a management decision tool

The ability to image the prostate accurately and better characterize cancerous lesions makes multiparametric magnetic resonance imaging (mpMRI) an invaluable tool to improve management of localized prostate cancer (PCa). Improved risk stratification is warranted given the evidence of significant overtreatment of indolent PCa. mpMRI can more accurately rule out clinically significant PCa in men deciding between surveillance and definitive treatment to reduce overtreatment. mpMRI improves detection of clinically significant PCa, which helps minimize sampling error, a major limitation of the traditional diagnostic paradigm. Aside from helping determine candidacy for initial surveillance vs. treatment, mpMRI is a useful tool for following men on active surveillance (AS) with the potential to reduce the need for serial biopsies. When definitive treatment is warranted, mpMRI can be used to determine the local extent of disease. This provides information that is useful in the treatment decision, counseling about outcomes, and surgical planning. While mpMRI is a significant step forward in PCa management, it is necessary to understand its limitations. mpMRI and MRI-guided fusion biopsy techniques still do not detect all clinically significant tumors. The utility of current mpMRI techniques is limited by the multifocal nature of PCa with poor detection of non-index lesions, inaccurate estimation of tumor size and geometry, and the need for interpretation by specialized radiologists. The role of mpMRI will continue to expand as improvements in technology and experience help overcome these limitations.