The Impact of Clinical Stage on Prostate Cancer Survival Following Radical Prostatectomy

Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review

The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.

CRITICAL RELEVANT STATEMENT

The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.

PROTOCOL OF SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .

KEY POINTS

Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.

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.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction

OBJECTIVES

To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE.

METHODS

We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix.

RESULTS

AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P < .05).

CONCLUSION

The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions.

ADVANCES IN KNOWLEDGE

This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

The prognostic nomogram for PSA-incongruent low-risk prostate cancer treated by radical prostatectomy

OBJECTIVE

To establish a prognostic nomogram for PSA-incongruent low-risk prostate cancer (PCa) patients (Gleason score 6 and clinical stage T2a) at diagnosis and treated with radical prostatectomy (RP), based on clinical and pathological metrics.

METHODS

In total, 217 patients diagnosed with PCa were included in this study. All patients had a Gleason score of 6 (GS6) in biopsy, had clinical T2a before surgery and were treated with RP. Biochemical progression-free survival (bPFS) was analyzed using the Kaplan-Meier method. Univariate and multivariate analyses were used to determine prognostic factors related to bPFS. A prognostic nomogram was established based on factors that were significant in the multivariate analyses.

RESULTS

The median bPFS had a significant difference in the subgroup of PSA at diagnosis (' < 10 ng/mL': 71.698 [67.549-75.847] vs '10-20 ng/mL': 71.038 [66.220-75.857] vs ' ≥ 20 ng/mL': 26.746 [12.384-41.108] months [Log Rank P < 0.001]), the subgroup of T stage upgrade (Negative: 70.016 [65.846-74.187] vs 'T2b/c': 69.183 [63.544-74.822] vs 'T3/4': 32.235 [11.877-52.593] months [Log Rank P < 0.001]) and the subgroup of Gleason score upgrade (Negative: 72.63 [69.096-76.163] vs '3 + 4': 68.393 [62.243-74.543] vs '4 + 3': 41.427 [27.517-55.336] vs ' ≥ 8': 28.291 [7.527-49.055] [Log Rank P < 0.001]). PSA at diagnoses (Hazard Ratio (HR) 1.027, 95% CI 1.015-1.039, P < 0.001), T stage upgrade (HR 2.116, 95% CI 1.083-4.133, P = 0.028), and Gleason score upgrade (HR 2.831, 95% CI 1.892-4.237, P < 0.001) were identified as independent predictors with significance in multivariable Cox regression analysis. A nomogram was established based on these three factors.

CONCLUSIONS

Our study indicated that PSA-incongruent low-risk PCa patients (PSA with 10-20 ng/mL) had a similar prognosis to those with real low-risk PCa (PSA < 10 ng/mL) in the D' Amico criteria. We also established a nomogram based on three significant prognostic factors, including PSA at diagnosis, T stage upgrade, and Gleason score upgrade, which were associated with clinical outcomes in prostate cancer patients with GS6 and T2a after surgery.

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

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

Machine learning in predicting extracapsular extension (ECE) of prostate cancer with MRI: a protocol for a systematic literature review

INTRODUCTION

In patients with prostate cancer (PCa), the detection of extracapsular extension (ECE) and seminal vesicle invasion is not only important for selecting the appropriate therapy but also for preoperative planning and patient prognosis. It is of paramount importance to stage PCa correctly before surgery, in order to achieve better surgical and outcome results. Over the last years, MRI has been incorporated in the classical prostate staging nomograms with clinical improvement accuracy in detecting ECE, but with variability between studies and radiologist's experience.

METHODS AND ANALYSIS

The research question, based on patient, index test, comparator, outcome and study design criteria, was the following: what is the diagnostic performance of artificial intelligence algorithms for predicting ECE in PCa patients, when compared with that of histopathological results after radical prostatectomy. To answer this question, we will use databases (EMBASE, PUBMED, Web of Science and CENTRAL) to search for the different studies published in the literature and we use the QUADA tool to evaluate the quality of the research selection.

ETHICS AND DISSEMINATION

This systematic review does not require ethical approval. The results will be disseminated through publication in a peer-review journal, as a chapter of a doctoral thesis and through presentations at national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42020215671.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

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.

Diagnostic performance of ESUR scoring system for extraprostatic prostate cancer extension: A meta-analysis

PURPOSE

We aimed to evaluate the diagnostic performance of the European Society of Urogenital Radiology (ESUR) scoring system for detection of extraprostatic extension (EPE) in prostate cancer (PCa) by performing a meta-analysis.

MATERIALS AND METHODS

A literature search of MEDLINE, EMBASE, Cochrane Library, Web of Science, and Google Scholar was performed to identify relevant studies from January 2012 to December 2020. We included diagnostic accuracy studies using ESUR scoring system for detection of EPE, and with prostatectomy histopathological results as the reference standard. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The summary estimates of sensitivity and specificity were pooled using bivariate random-effects modeling. We conducted multiple subgroup analyses and meta-regression to explore varied clinical settings.

RESULTS

10 studies with a total of 1698 participants were included in this meta-analysis. Pooled sensitivity and specificity were 0.71 (95% CI 0.61-0.80) and 0.76 (95% CI 0.67-0.84), respectively, with the area under ROC of 0.80 (95% CI 0.77-0.84). The Higgins I² statistics demonstrated substantial heterogeneity in both sensitivity (I² = 86.5%) and specificity (I² = 91.6%), meta-regression revealed that the cutoff values (ESUR score ≥ 3 vs. ESUR score ≥ 4, P = 0.02) and malignancy rate (<40% vs. ≥40%, P = 0.04) were significant factors responsible for heterogeneity. Using endorectal coil and higher field strength (3.0 T) showed no additional benefit for EPE detection.

CONCLUSION

The evidence available for ESUR scoring system tends to show moderate diagnostic performance for detection of EPE, and the cutoff values (P = 0.02) and malignancy rate (P = 0.04) were significant factors contributed to the heterogeneity.

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Background

Radical prostatectomy is the main treatment for prostate cancer (PCa), a common cancer type among men. Recurrence frequently occurs in a proportion of patients. Therefore, there is a great need to early screen those patients to specifically schedule adjuvant therapy to improve the recurrence-free survival (RFS) rate. This study aims to develop a biomarker to predict RFS for patients with PCa based on the data of methylation, an important heritable contributor to carcinogenesis.

Methods

Methylation expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus database (GSE26126), and the European Bioinformatics Institute (E-MTAB-6131). The stable co-methylation modules were identified by weighted gene co-expression network analysis. The genes in modules were overlapped with differentially methylated RNAs (DMRs) screened by MetaDE package in three datasets, which were used to screen the prognostic genes using least absolute shrinkage and selection operator analyses. The prognostic performance of the prognostic signature was assessed by survival curve analysis.

Results

Five co-methylation modules were considered preserved in three datasets. A total of 192 genes in these 5 modules were overlapped with 985 DMRs, from which a signature panel of 11 methylated messenger RNAs and 3 methylated long non-coding RNAs was identified. This signature panel could independently predict the 5-year RFS of PCa patients, with an area under the receiver operating characteristic curve (AUC) of 0.969 for the training TCGA dataset and 0.811 for the testing E-MTAB-6131 dataset, both of which were higher than the predictive accuracy of Gleason score (AUC = 0.689). Also, the patients with the same Gleason score (6–7 or 8–10) could be further divided into the high-risk group and the low-risk group.

Conclusion

These results suggest that our prognostic model may be a promising biomarker for clinical prediction of RFS in PCa patients.

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

Purpose

To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction.

Methods and materials

In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets.

Results

The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively.

Conclusions

The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-021-00414-6.

Accuracy of abbreviated multiparametric MRI-derived protocols in predicting local staging of prostate cancer in men undergoing radical prostatectomy

BACKGROUND

Abbreviated magnetic resonance imaging (aMRI) protocols have emerged as an alternative to multiparametric MRI (mpMRI) to reduce examination time and costs.

PURPOSE

To compare multiple aMRI protocols for predicting pathological stage ≥T3 (≥pT3) prostate cancer (PCa).

MATERIAL AND METHODS

One hundred and eight men undergoing staging mpMRI before radical prostatectomy (RP) were retrospectively evaluated. 3.0-T imaging was performed with a 32-channel surface coil and a protocol including diffusion-weighted imaging (DWI), transverse T2-weighted (tT2W) imaging, coronal T2W (cT2W) imaging, sagittal T2W (sT2) imaging, and dynamic contrast-enhanced (DCE) imaging. Two readers independently assessed whether any MRI observation showed stage ≥T3 on each sequence (reading order: DWI, cT2W, tT2W, sT2W, DCE). Final stage was assessed by matching readers' assignments to pathology, and combining them into eight protocols: DWI + tT2W, DWI + cT2W + tT2W, DWI + tT2W + sT2W, DWI + cT2W + tT2W + sT2W, DWI + tT2W + DCE, DWI + cT2W + tT2W + DCE, DWI + tT2W + sT2W + DCE, and mpMRI. Diagnostic accuracy and inter-reader agreement for aMRI protocols were calculated.

RESULTS

Prevalence of ≥pT3 PCa was 31.5%. Sensitivity, specificity, positive (PPV) and negative predictive value (NPV) of aMRI protocols were comparable to mpMRI for R1. Sensitivity was 74.3% (95% confidence interval [CI] 64.8-72.0) to 77.1% (95% CI 67.9-84.4), and NPV 86.8% (95% CI 78.6-92.3) to 88.1% (95% CI 80.1-93.3). All accuracy measures of the various aMRI protocols were similar to mpMRI also for R2, albeit all slightly lower compared to R1. On a per-protocol basis, there was substantial inter-reader agreement in predicting stage ≥pT3 (k 0.63-0.67).

CONCLUSION

When comparing the diagnostic accuracy of multiple aMRI protocols against mpMRI for predicting stage ≥pT3 PCa, the protocol with the fewest sequences (DWI + tT2W) is apparently equivalent to standard mpMRI.

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.

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.

Coexpression Network Analysis Identifies a Novel Nine-RNA Signature to Improve Prognostic Prediction for Prostate Cancer Patients

Background

Prostate cancer (PCa) is the most common malignancy and the leading cause of cancer death in men. Recent studies suggest the molecular signature was more effective than the clinical indicators for the prognostic prediction, but all of the known studies focused on a single RNA type. The present study was to develop a new prognostic signature by integrating long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) and evaluate its prognostic performance.

Methods

The RNA expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) or Gene Expression Omnibus database (GSE17951, GSE7076, and GSE16560). The PCa-driven modules were identified by constructing a weighted gene coexpression network, the corresponding genes of which were overlapped with differentially expressed RNAs (DERs) screened by the MetaDE package. The optimal prognostic signature was screened using the least absolute shrinkage and selection operator analysis. The prognostic performance and functions of the combined prognostic signature was then assessed.

Results

Twelve PCa-driven modules were identified using TCGA dataset and validated in the GSE17951 and GSE7076 datasets, and six of them were considered to be preserved. A total of 217 genes in these 6 modules were overlapped with 699 DERs, from which a nine-gene prognostic signature was identified (including 3 lncRNAs and 6 mRNAs), and the risk score of each patient was calculated. The overall survival was significantly shortened in patients having the risk score higher than the cut-off, which was demonstrated in TCGA (p = 5.063E − 03) dataset and validated in the GSE16560 (p = 3.268E − 02) dataset. The prediction accuracy of this risk score was higher than that of clinical indicators (the Gleason score and prostate-specific antigen) or the single RNA type, with the area under the receiver operator characteristic curve of 0.945. Besides, some new therapeutic targets and mechanisms (MAGI2-AS3-SPARC/GJA1/CYSLTR1, DLG5-AS1-DEFB1, and RHPN1-AS1-CDC45/ORC) were also revealed.

Conclusion

The risk score system established in this study may provide a novel reliable method to identify PCa patients at a high risk of death.

Clinical stage provides useful prognostic information even after pathological stage is known for prostate cancer in the PSA era

BACKGROUND

Pathological and clinical stage are associated with prostate cancer-specific survival after prostatectomy. With PSA screening, the post-surgery prognostic utility of clinical stage is debatable in studies seeking to identify new biomarkers. Few studies have investigated clinical stage and lethal prostate cancer association after accounting for pathological stage. We hypothesize that clinical stage provides prognostic information beyond pathological stage in the PSA era.

METHODS

Cox regression models tested associations between clinical and pathological stage and lethal prostate cancer among 3,064 participants from the Health Professionals Follow-Up Study and Physicians' Health Study (HPFS/PHS) who underwent prostatectomy. Likelihood ratio tests and c-statistics were used to assess the models' prognostic utility. Equivalent analyses were performed in 16,134 men who underwent prostatectomy at Johns Hopkins.

RESULTS

Independently, clinical and pathological stage were associated (p<0.0001 for both) with rate of lethal prostate cancer in HPFS/PHS. The model with clinical and pathological stage fit significantly better than the model with only pathological stage in all men (p = 0.01) and in men diagnosed during the PSA era (p = 0.04). The mutually adjusted model also improved discriminatory ability. In the Johns Hopkins cohort, the model with clinical and pathological stage improved discriminatory ability and fit significantly better overall (p<0.0001) and in the PSA era (p<0.0001).

CONCLUSIONS

Despite stage migration resulting from widespread PSA screening, clinical stage remains associated with progression to lethal prostate cancer independent of pathological stage. Future studies evaluating associations between new factors and poor outcome following prostatectomy should consider including both clinical and pathological stages since the data is already available.

Applied anatomy of pelvic lymph nodes and its clinical significance for prostate cancer:a single-center cadaveric study

Background

Pelvic lymph node dissection (PLND) is one of the most important steps in radical prostatectomy (RP). Not only can PLND provide accurate clinical staging to guide treatment after prostatectomy but PLND can also improve the prognosis of patients by eradicating micro-metastases. However, reports of the number of pelvic lymph nodes have generally come from incomplete dissection during surgery, there is no anatomic study that assesses the number and variability of lymph nodes. Our objective is to assess the utility of adopting the lymph node count as a metric of surgical quality for the extent of lymph node dissection during RP for prostate cancer by conducting a dissection study of pelvic lymph nodes in adult male cadavers.

Methods

All 30 adult male cadavers underwent pelvic lymph node dissection (PLND), and the lymph nodes in each of the 9 dissection zones were enumerated and analyzed.

Results

A total of 1267 lymph nodes were obtained. The number of lymph nodes obtained by limited PLND was 4–22 (14.1 ± 4.5), the number obtained by standard PLND was 16–35 (25.9 ± 5.6), the number obtained by extended PLND was 17–44 (30.0 ± 7.0), and the number obtained by super-extended PLDN was 24–60 (42.2 ± 9.7).

Conclusions

There are substantial inter-individual differences in the number of lymph nodes in the pelvic cavity. These results have demonstrated the rationality and feasibility of adopting lymph node count as a surrogate for evaluating the utility of PLND in radical prostatectomy, but these results need to be further explored.

Accuracy of multiparametric magnetic resonance imaging for detecting extracapsular extension in prostate cancer: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic accuracy of multiparametric MRI (mpMRI) for detecting extracapsular extension (ECE) in patients with prostate cancer (PCa).

METHODS AND MATERIALS

We searched MEDLINE, PubMed, Embase and the Cochrane library up to December 2018. We included studies that used mpMRI to differentiate ECE from organ-confined PCa with a combination of T₂ weighted imaging (T₂WI), diffusion-weighted imaging, and dynamic contrast-enhanced MRI. All studies included had pathological diagnosis with radical prostatectomy. Two reviewers independently assessed the methodological quality of included studies by using Quality Assessment of Diagnostic Accuracy Studies 2 tool. We calculated pooled sensitivity, specificity, positive and negative predictive values, diagnostic odds ratios and receiver operating characteristic curve for mpMRI from 2 × 2 tables.

RESULTS

A total of 17 studies that comprised 3374 participants were included. The pooled data showed a sensitivity of 0.55 (95% confidence interval 0.43, 0.66]) and specificity of 0.87 (95% confidence interval 0.82, 0.91) for extracapsular extension detection in PCa.

CONCLUSION

First, our meta-analysis shows moderate sensitivity and high specificity for mpMRI to differentiate ECE from organ-confined prostate cancer before surgery. Second, our meta-analysis shows that mpMRI had no significant differences in performance compared with the former meta-analysis with use of T₂WI alone or with additional functional MRI.

ADVANCES IN KNOWLEDGE

It is the first meta-analysis to evaluate the accuracy of mpMRI in combination of TWI, diffusion-weightedimaging and dynamiccontrast-enhanced-MRI for extracapsular extension detection.

Consensus statements on the management of clinically localized prostate cancer from the Hong Kong Urological Association and the Hong Kong Society of Uro-Oncology

OBJECTIVE

To formulate consensus statements to facilitate physician management strategies for patients with clinically localized prostate cancer (PCa) in Hong Kong by jointly convening a panel of 12 experts from the two local professional organizations representing PCa specialists, who had previously established consensus statements on the management of metastatic PCa for the locality.

METHODS

Through a series of meetings, the panellists discussed their clinical experience and the published evidence regarding various areas of the management of localized PCa, then drafted consensus statements. At the final meeting, each drafted statement was voted on by every panellist based on its practicability of recommendation in the locality.

RESULTS

A total of 76 consensus statements were ultimately accepted and established by panel voting.

CONCLUSION

Derived from the recent evidence and major overseas guidelines, along with local clinical experience and practicability, the consensus statements were aimed to serve as a practical reference for physicians in Hong Kong for the management of localized PCa.

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.

MRI-Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

BACKGROUND

Radiomics approaches based on multiparametric MRI (mp-MRI) have shown high accuracy in prostate cancer (PCa) management. However, there is a need to apply radiomics to the preoperative prediction of extracapsular extension (ECE).

PURPOSE

To develop and validate a radiomics signature to preoperatively predict the probability of ECE for patients with PCa, compared with the radiologists' interpretations.

STUDY TYPE

Retrospective.

POPULATION

In total, 210 patients with pathology-confirmed ECE status (101 positive, 109 negative) were enrolled.

FIELD STRENGTH/SEQUENCE

T₂ -weighted imaging (T₂ WI), diffusion-weighted imaging, and dynamic contrast-enhanced imaging were performed on two 3.0T MR scanners.

ASSESSMENT

A radiomics signature was constructed to predict the probability of ECE prior to radical prostatectomy (RP). In all, 17 stable radiomics features of 1619 extracted features based on T₂ WI were selected. The same images were also evaluated by three radiologists. The predictive performance of the radiomics signature was validated and compared with radiologists' interpretations.

STATISTICAL TESTS

A radiomics signature was developed by a least absolute shrinkage and selection operator (LASSO) regression algorithm. Samples enrolled were randomly divided into two groups (143 for training and 67 for validation). Discrimination, calibration, and clinical usefulness were validated by analysis of the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve, respectively. The predictive performance was then compared with visual assessments of three radiologists.

RESULTS

The radiomics signature yielded an AUC of 0.902 and 0.883 in the training and validation cohort, respectively, and outperformed the visual assessment (AUC: 0.600-0.697) in the validation cohort. Pairwise comparisons demonstrated that the radiomics signature was more sensitive than the radiologists (75.00% vs. 46.88%-50.00%, all P < 0.05), but obtained comparable specificities (91.43% vs. (88.57%-94.29%); P ranged from 0.64-1.00).

DATA CONCLUSION

A radiomics signature was developed and validated that outperformed the radiologists' visual assessments in predicting ECE status.

LEVEL OF EVIDENCE

4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1914-1925.

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.

Evaluation of the most optimal multiparametric magnetic resonance imaging sequence for determining pathological length of capsular contact

OBJECTIVES

To assess the most optimal multi-parametric magnetic resonance imaging sequence (Mp-MRI) in determining pathological length of capsular contact (LCC) for the diagnosis of prostate cancer extraprostatic extension (EPE).

METHODS

105 patients with prostate cancer who underwent Mp-MRI of prostate prior to radical prostatectomy were enrolled in this retrospective study. LCC was determined from T2-weighted images (T2WI), Apparent Diffusion Coefficient (ADC) map, dynamic contrast-enhanced MRI (DCE-MRI) separately by two blinded radiologists. The LCCs in patients with and without EPE were compared with Mann Whitney-U test. The relationship between pathological LCC and the LCC that was measured from each Mp-MRI sequences were calculated by using Spearman test. The ability of all individual Mp-MRI sequences in determining pathological LCC was calculated by drawing receiver operator characteristic (ROC) curves. The diagnostic accuracy of LCC based on each MRI sequences for EPE diagnosis was also calculated with ROC curve analysis.

RESULTS

The patients with EPE had longer median LCC than patients without EPE for each Mp-MRI sequences and for both readers. In addition, the LCC showed a broader overlapping between patients with and without EPE on ADC map (reader-1, p = 0.01; reader-2, p = 0.01) when compared with T2WI (reader-1, p = 0.002; reader-2, p = 0.001) and DCE-MRI (reader-1, p = 0.001; reader-2, p = 0.001). LCC based on DCE-MRI showed the strongest correlation with pathological LCC. The area under the curve (AUC) based on LCC was higher when using the DCE-MRI (reader-1: 0.874, p = 0.030; reader-2: 0.862, p = 0.02) than when using T2WI and ADC map in predicting pathological LCC for both readers. While the LCC based on ADC map showed poor diagnostic accuracy, LCC based on T2WI and DCE-MRI had fair diagnostic accuracy for EPE diagnosis.

CONCLUSION

The contact between prostate tumor and capsule seems to be a useful and objective parameter for evaluating the EPE of prostate cancer with Mp-MRI. More specifically, LCC based on DCE-MRI has highest correlation with pathological LCC and has better ability to predict pathological LCC when compared with other Mp-MRI sequences. However, the performance of LCC based on T2WI and DCE-MRI was similar for EPE diagnosis. It seems measurement of LCC from DCE-MRI and measurement of LCC from T2WI does not show any difference in clinical EPE assessment.

Prostate-specific antigen after salvage radiotherapy for postprostatectomy biochemical recurrence predicts long-term outcome including overall survival

BACKGROUND

For patients with recurrent prostate cancer after radical prostatectomy (RP), salvage radiotherapy (SRT) is a second chance of cure. However, depending on risk factors, 40-70% of the patients experience further progression. With a focus on the pre- and post-SRT serum level of the prostate-specific antigen (PSA), we assessed the determinants of the long-term outcome after SRT.

PATIENT AND METHODS

Between 1997 and 2011, 464 patients received 3D-conformal SRT with median 66.6 Gy. The median PSA level before SRT was 0.31 ng/ml. In our retrospective analysis, post-SRT progression was defined as either a rising PSA >0.2 ng/ml above the nadir, or the application of anti-androgens or clinical recurrence. A PSA <0.1 ng/ml was termed undetectable. We analyzed the data with the Kaplan-Meier method (Logrank test) and multivariable Cox regression.

RESULTS

The median follow-up was 5.9 years. Overall, 178 patients had recurrence, 13 developed distant metastases and 30 died. Univariate, a pre-RP PSA <10 ng/ml, pathological stage pT <3, Gleason score <8, positive surgical margins, a pre-SRT PSA <0.2 ng/ml and a post-SRT PSA nadir <0.1 ng/ml correlated with fewer and later second recurrences. In a multivariable Cox model, pT, Gleason score, margin status and pre-SRT PSA were significant covariates of progression. If the post-SRT PSA response was included in the regression analysis, then a nadir ≥0.1 ng/ml was the strongest risk factor. Initiating SRT at a PSA <0.2 ng/ml correlated with a post-SRT PSA <0.1 ng/ml. Men who achieved an undetectable post-SRT PSA nadir also had lower rates of metastases and a better overall survival. However, there were too few events for Cox regression analysis of these two endpoints.

CONCLUSIONS

Early SRT at a PSA <0.2 ng/ml correlates with re-achieving an undetectable PSA, which predicts improved freedom from progression and metastases and better overall survival.

Reduced Connexin 43 expression is associated with tumor malignant behaviors and biochemical recurrence-free survival of prostate cancer

Connexin 43, a gap junction protein, coordinates cell-to-cell communication and adhesion. Altered Connexin 43 expression associated with cancer development and progression. In this study, we assessed Connexin 43 expression for association with clinicopathological features and biochemical recurrence of prostate cancer after radical prostatectomy. Pathological specimens were collected from 243 patients who underwent radical prostatectomy and from 60 benign prostatic hyperplasia (BPH) patients to construct tissue microarrays and immunohistochemical analysis of Connexin 43 expression. Kaplan-Meier curves and multivariable Cox proportion hazard model were performed to associate Connexin 43 expression with postoperative biochemical recurrence-free survival (BFS). Connexin 43 expression was significantly reduced or lost in tumor tissues compared to that of BPHs (39.1% vs. 96.7%, P<0.001). Reduced Connexin 43 expression was associated with high levels of preoperative PSA, high Gleason score, advanced pT stage, positive surgical margin, extracapsular extension, and seminal vesicle invasion (P < 0.05, for all). Kaplan-Meier curves showed that reduced Connexin 43 expression was associated with shortened postoperative BFS (P < 0.001). Multivariate analysis showed that reduced Connexin 43 expression, high Gleason score and advanced pT stage were independent predictors for BFS of patients (P < 0.05). Connexin 43 expression was significantly reduced or lost in prostate cancer tissues, which was associated with advanced clinicopathological features and poor BFS of patients after radical prostatectomy.

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.

The efficacy and utilisation of preoperative multiparametric magnetic resonance imaging in robot-assisted radical prostatectomy: does it change the surgical dissection plan?

Objective

We investigated the effect of the use of multiparametric prostate magnetic resonance imaging (mp-MRI) on the dissection plan of the neurovascular bundle and the oncological results of our patients who underwent robot-assisted radical prostatectomy.

Material and methods

We prospectively evaluated 60 consecutive patients, including 30 patients who had (Group 1), and 30 patients who had not (Group 2) mp-MRI before robot-assisted radical prostatectomy. Based on the findings of mp-MRI, the dissection plan was changed as intrafascial, interfascial, and extrafascial in the mp-MRI group. Two groups were compared in terms of age, prostate-specific antigen (PSA), Gleason sum scores and surgical margin positivity.

Results

There was no statistically significant difference between the two groups in terms of age, PSA, biopsy Gleason score, final pathological Gleason score and surgical margin positivity. mp-MRI changed the initial surgical plan in 18 of 30 patients (60%) in Group 1. In seventeen of these patients (56%) surgical plan was changed from non-nerve sparing to interfascial nerve sparing plan. In one patient dissection plan was changed to non-nerve sparing technique which had extraprostatic extension on final pathology. Surgical margin positivity was similar in Groups 1, and 2 (16% and 13%, respectively) although, Group 1 had higher number of high- risk patients. mp-MRI confirmed the primary tumour localisation in the final pathology in 27 of of 30 patients (90%).

Conclusion

Preoperative mp-MRI effected the decision to perform a nerve-sparing technique in 56% of the patients in our study; moreover, changing the dissection plan from non-nerve-sparing technique to a nerve sparing technique did not increase the rate of surgical margin positivity.

Evaluation of extracapsular extension in prostate cancer using qualitative and quantitative multiparametric MRI

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (mpMRI) for extracapsular extension (ECE) in prostate cancer (PCa).

MATERIALS AND METHODS

In all, 292 patients who received radical prostatectomy and underwent preoperative mpMRI at 3T were enrolled retrospectively. For determining the associations with ECE, the likelihood of ECE was assessed qualitatively on T₂ -weighted imaging (T₂ WI) and combined T₂ WI and diffusion-weighted imaging (DWI) or dynamic contrast-enhanced imaging (DCEI). Quantitative MRI parameters were measured in PCa based on histopathological findings. Two models for detecting ECE including imaging and clinical parameters were developed using multivariate analysis: Model 1 excluding combined T₂ WI and DWI and DCEI and Model 2 excluding combined T₂ WI and DWI, and combined T₂ WI and DCEI. Diagnostic performance of imaging parameters and models was evaluated using the area under the receiver operating characteristics curve (Az).

RESULTS

For detecting ECE, the specificity, accuracy, and Az of combined T₂ WI and DWI or DCEI were statistically better than those of T₂ WI (P < 0.05), and all quantitative MRI parameters showed a statistical difference between the patients with and without ECE (P < 0.05). On multivariate analysis, significant independent markers in Model 1 were combined T₂ WI and DWI, combined T₂ WI and DCEI, and K^trans (P < 0.05). In Model 2, significant markers were combined T₂ WI and DWI and DCEI, K^trans , K_ep , and Ve (P < 0.05). The Az values of models 1 and 2 were 0.944 and 0.957, respectively.

CONCLUSION

mpMRI may be useful to improve diagnostic accuracy of the models for determining the associations with ECE in PCa.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1760-1770.

Prostate cancer risk stratification with magnetic resonance imaging

In recent years, multiparametric magnetic resonance imaging (mpMRI) has shown promise for prostate cancer (PCa) risk stratification. mpMRI, often followed by targeted biopsy, can be used to confirm low-grade disease before enrollment in active surveillance. In patients with intermediate or high-risk PCa, mpMRI can be used to inform surgical management. mpMRI has sensitivity of 44% to 87% for detection of clinically significant PCa and negative predictive value of 63% to 98% for exclusion of significant disease. In addition to tumor identification, mpMRI has also been shown to contribute significant incremental value to currently used clinical nomograms for predicting extraprostatic extension. In combination with conventional clinical criteria, accuracy of mpMRI for prediction of extraprostatic extension ranges from 92% to 94%, significantly higher than that achieved with clinical criteria alone. Supplemental sequences, such as diffusion-weighted imaging and dynamic contrast-enhanced imaging, allow quantitative evaluation of cancer-suspicious regions. Apparent diffusion coefficient appears to be an independent predictor of PCa aggressiveness. Addition of apparent diffusion coefficient to Epstein criteria may improve sensitivity for detection of significant PCa by as much as 16%. Limitations of mpMRI include variability in reporting, underestimation of PCa volume and failure to detect clinically significant disease in a small but significant number of cases.

Multiparametric magnetic resonance imaging localizes established extracapsular extension of prostate cancer

OBJECTIVE

To define the accuracy of multiparametric magnetic resonance imaging (MP-MRI) for identifying focal and established extracapsular extension (ECE) in various zones of the prostate.

METHODS

Between 2010 and 2013, 342 patients underwent MP-MRI of the prostate (3T, no endorectal coil with axial perfusion and diffusion images). The findings of the images were reported as negative, suspicious, or positive for ECE by a single expert radiologist. Radical prostatectomy specimens were reviewed to confirm the size and the location of ECE and further defined as focal or established ECE. Established ECE included extension that was multifocal or involving more than 5 glands. The accuracy of MRI in localizing focal and established ECE to each zone of the prostate was determined. Regression analyses were performed to identify predictors of ECE.

RESULTS

We identified 112 patients who underwent prostate MP-MRI and radical prostatectomy. MRI findings considered suspicious or definite for ECE accurately predicted pathologic ECE (P<0.001). MP-MRI identified established ECE but not focal ECE. Sensitivity, specificity, positive predictive value, and negative predictive value of MP-MRI for established ECE were 70.7%, 90.6%, 57.1%, and 95.1%, respectively. MRI identified ECE to the left vs. right side as well as each zone of the prostate; however, sensitivity was lowest at the apex. On multivariate analysis, MRI was a significant predictor of ECE that was independent of prostate-specific antigen level, Gleason score, and clinical stage.

CONCLUSION

MP-MRI is useful for identifying established but not focal ECE in all zones of the prostate. MRI was a significant independent predictor of established ECE and may be a useful adjunct in staging prostate cancer.

Prognostic value of intraductal carcinoma of the prostate in radical prostatectomy specimens

BACKGROUND

Intraductal carcinoma of the prostate (IDC-P) is an adverse prognostic factor for radical prostatectomy (RP). The endpoint in most IDC-P studies is increased prostate-specific antigen (PSA) levels. The aim of this study was to evaluate whether IDC-P in RP specimens is an adverse prognostic factor for progression-free survival (PFS) and cancer-specific survival (CSS).

METHODS

We retrospectively evaluated 206 high-risk prostate cancer patients treated with RP and analyzed data on age, serum PSA level at diagnosis, biopsy Gleason score (bGS), surgical margin (SM), clinical T stage (cT), extraprostatic extension (EPE), seminal vesicle invasion (SVI), lymph node metastasis (LN), and neoadjuvant therapy.

RESULTS

An IDC-P component was found in 104 cases. Forty-four patients experienced clinical failure, and 20 patients died of the disease. Patients with IDC-P showed a higher bGS and stage (including cT, EPE, SVI, and LN) than those without IDC-P. In univariate analysis, IDC-P, PSA level, bGS, SM, cT, SVI, LN, and EPE (P < 0.0001) were significantly associated with PFS. IDC-P (P = 0.0004), PSA level (P < 0.0001), SM (P = 0.0013), cT (P = 0.0019), SVI (P = 0.0012), and LN (P = 0.0002) were significantly associated with CSS. In multivariate analysis, IDC-P (P = 0.0038), and cT (P = 0.0001) were significantly associated with PFS. IDC-P (P = 0.0238) and PSA level (P = 0.0112) were significantly associated with CSS.

CONCLUSIONS

IDC-P in RP specimens was an independent risk factor for PFS and CSS and could predict clinical outcomes.

Nuclear MTA1 overexpression is associated with aggressive prostate cancer, recurrence and metastasis in African Americans

Metastasis-associated protein 1 (MTA1), a negative epigenetic modifier, plays a critical role in prostate cancer (PCa) progression. We hypothesized that MTA1 overexpression in primary tumor tissues can predict PCa aggressiveness and metastasis. Immunohistochemical staining of MTA1 was done on archival PCa specimens from University of Mississippi Medical Center and University of Iowa. We found that nuclear MTA1 overexpression was positively correlated with the severity of disease progression reaching its highest levels in metastatic PCa. Nuclear MTA1 overexpression was significantly associated with Gleason > 7 tumors in African Americans but not in Caucasians. It was also a predictor of recurrent disease. We concluded that MTA1 nuclear overexpression may be a prognostic indicator and a future therapeutic target for aggressive PCa in African American men. Our findings may be useful for categorizing African American patients with a higher probability of recurrent disease and metastasis from those who are likely to remain metastasis-free.