Prognostic value of preoperative multiparametric magnetic resonance imaging (MRI) for predicting biochemical recurrence after radical prostatectomy

Association between mpMRI detected tumor apparent diffusion coefficient and 5-year biochemical recurrence risk after radical prostatectomy

PURPOSE

To assess the ability of tumor apparent diffusion coefficient (ADC) values obtained from multiparametric magnetic resonance imaging (mpMRI) to predict the risk of 5-year biochemical recurrence (BCR) after radical prostatectomy (RP).

MATERIALS AND METHODS

This retrospective analysis included 1207 peripheral and 232 non-peripheral zone prostate cancer (PCa) patients who underwent mpMRI before RP (2012-2015), with the outcome of interest being 5-year BCR. ADC was evaluated as a continuous variable and as categories: low (< 850 µm2/s), intermediate (850-1100 µm2/s), and high (> 1100 µm2/s). Kaplan-Meier curves with log-rank testing of BCR-free survival, multivariable Cox proportional hazard regression models were formed to estimate the risk of BCR.

RESULTS

Among the 1439 males with median age 63 (± 7) years, the median follow-up was 59 months, and 306 (25%) patients experienced BCR. Peripheral zone PCa patients with BCR had lower tumor ADC values than those without BCR (874 versus 1025 µm2/s, p < 0.001). Five-year BCR-free survival rates were 52.3%, 74.4%, and 87% for patients in the low, intermediate, and high ADC value categories, respectively (p < 0.0001). Lower ADC was associated with BCR, both as continuously coded variable (HR: 5.35; p < 0.001) and as ADC categories (intermediate versus high ADC-HR: 1.56, p = 0.017; low vs. high ADC-HR; 2.36, p < 0.001). In the non-peripheral zone PCa patients, no association between ADC and BCR was observed.

CONCLUSION

Tumor ADC values and categories were found to be predictive of the 5-year BCR risk after RP in patients with peripheral zone PCa and may serve as a prognostic biomarker.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Incremental prognostic value of ADC histogram analysis in patients with high-risk prostate cancer receiving adjuvant hormonal therapy after radical prostatectomy

Purpose

To investigate the incremental prognostic value of preoperative apparent diffusion coefficient (ADC) histogram analysis in patients with high-risk prostate cancer (PCa) who received adjuvant hormonal therapy (AHT) after radical prostatectomy (RP).

Methods

Sixty-two PCa patients in line with the criteria were enrolled in this study. The 10^th, 50^th, and 90^th percentiles of ADC (ADC₁₀, ADC₅₀, ADC₉₀), the mean value of ADC (ADC_mean), kurtosis, and skewness were obtained from the whole-lesion ADC histogram. The Kaplan-Meier method and Cox regression analysis were used to analyze the relationship between biochemical recurrence-free survival (BCR-fs) and ADC parameters and other clinicopathological factors. Prognostic models were constructed with and without ADC parameters.

Results

The median follow-up time was 53.4 months (range, 41.1-79.3 months). BCR was found in 19 (30.6%) patients. Kaplan-Meier curves showed that lower ADC_mean, ADC₁₀, ADC₅₀, and ADC₉₀ and higher kurtosis could predict poorer BCR-fs (all p<0.05). After adjusting for clinical parameters, ADC₅₀ and kurtosis remained independent prognostic factors for BCR-fs (HR: 0.172, 95% CI: 0.055-0.541, p=0.003; HR: 7.058, 95% CI: 2.288-21.773, p=0.001, respectively). By adding ADC parameters to the clinical model, the C index and diagnostic accuracy for the 24- and 36-month BCR-fs were improved.

Conclusion

ADC histogram analysis has incremental prognostic value in patients with high-risk PCa who received AHT after RP. Combining ADC₅₀, kurtosis and clinical parameters can improve the accuracy of BCR-fs prediction.

Synthetic magnetic resonance imaging for primary prostate cancer evaluation: Diagnostic potential of a non-contrast-enhanced bi-parametric approach enhanced with relaxometry measurements

Purpose

Bi-parametric magnetic resonance imaging (bpMRI) with diffusion-weighted images has wide utility in diagnosing clinically significant prostate cancer (csPCa). However, bpMRI yields more false-negatives for PI-RADS category 3 lesions than multiparametric (mp)MRI with dynamic-contrast-enhanced (DCE)-MRI. We investigated the utility of synthetic MRI with relaxometry maps for bpMRI-based diagnosis of csPCa.

Methods

One hundred and five treatment-naïve patients who underwent mpMRI and synthetic MRI before prostate biopsy for suspected PCa between August 2019 and December 2020 were prospectively included. Three experts and three basic prostate radiologists evaluated the diagnostic performance of conventional bpMRI and synthetic bpMRI for csPCa. PI-RADS version 2.1 category 3 lesions were identified by consensus, and relaxometry measurements (T1-value, T2-value, and proton density [PD]) were performed. The diagnostic performance of relaxometry measurements for PI-RADS category 3 lesions in peripheral zone was compared with that of DCE-MRI. Histopathological evaluation results were used as the reference standard. Statistical analysis was performed using the areas under the receiver operating characteristic curve (AUC) and McNemar test.

Results

In 102 patients without significant MRI artefacts, the diagnostic performance of conventional bpMRI was not significantly different from that of synthetic bpMRI for all readers (p = 0.11–0.79). The AUCs of the combination of T1-value, T2-value, and PD (T1 + T2 + PD) for csPCa in peripheral zone for PI-RADS category 3 lesions were 0.85 for expert and 0.86 for basic radiologists, with no significant difference between T1 + T2 + PD and DCE-MRI for both expert and basic radiologists (p = 0.29–0.45).

Conclusion

Synthetic MRI with relaxometry maps shows promise for contrast media-free evaluation of csPCa.

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Diagnostic performances of synthetic bpMRI and conventional bpMRI are comparable for primary PCa

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Diagnostic performance of synthetic MRI variables are similar to that of DCE-MRI for csPCa in PZ

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Synthetic bpMRI shows potential as a contrast agent-free method for primary PCa

Machine learning algorithms can more efficiently predict biochemical recurrence after robot-assisted radical prostatectomy

OBJECTIVES

To develop a model for predicting biochemical recurrence (BCR) in patients with long follow-up periods using clinical parameters and the machine learning (ML) methods.

MATERIALS METHOD

Patients who underwent robot-assisted radical prostatectomy between January 2014 and December 2019 were retrospectively reviewed. Patients who did not have BCR were assigned to Group 1, while those diagnosed with BCR were assigned to Group 2. The patient's demographic data, preoperative and postoperative parameters were all recorded in the database. Three different ML algorithms were employed: random forest, K-nearest neighbour, and logistic regression.

RESULTS

Three hundred and sixty-eight patients were included in this study. Among these patients, 295 (80.1%) did not have BCR (Group 1), while 73 (19.8%) had BCR (Group 2). The mean duration of follow-up and duration until the diagnosis of BCR was calculated as 35.2 ± 16.7 and 11.5 ± 11.3 months, respectively. The multivariate analysis revealed that NLR, PSAd, risk classification, PIRADS score, T stage, presence or absence of positive surgical margin, and seminal vesicle invasion were predictive for BCR. Classic Cox regression analysis had an area under the curve (AUC) of 0.915 with a sensitivity and specificity of 90.6% and 79.8%. The AUCs for receiver-operating characteristic curves for random forest, K nearest neighbour, and logistic regression were 0.95, 0.93, and 0.93, respectively. All ML models outperformed the conventional statistical regression model in the prediction of BCR after prostatectomy.

CONCLUSION

The construction of more reliable and potent models will provide the clinicians and patients with advantages such as more accurate risk classification, prognosis estimation, early intervention, avoidance of unnecessary treatments, relatively lower morbidity and mortality. The ML methods are cheap, and their powers increase with increasing data input; we believe that their clinical use will increase over time.

Association Between Multiparametric Magnetic Resonance Imaging of the Prostate and Oncological Outcomes after Primary Treatment for Prostate Cancer: A Systematic Review and Meta-analysis

CONTEXT

The diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) diagnosis has been extensively explored. Little is known about the prognostic value of mpMRI suspicion scores and other quantitative mpMRI information.

OBJECTIVE

To systematically review the current literature assessing the relationship between pretreatment mpMRI and oncological outcomes after primary treatment for PCa to assess the role of mpMRI as a prognostic tool.

EVIDENCE ACQUISITION

A computerized bibliographic search of MEDLINE/PubMed, EMBASE, Scopus, and the Cochrane Library CENTRAL databases was performed for all studies assessing the relationship between mpMRI and oncological outcomes after primary treatment for PCa. The review protocol is registered in the PROSPERO database (CRD42020209899).

EVIDENCE SYNTHESIS

A total of six studies were included. Reliable evidence is still limited in this field. The Prostate Imaging-Reporting and Data System (PI-RADS) score was an independent predictor of biochemical recurrence (BCR) after radical prostatectomy (RP) in the majority of the studies included. The tumor volume at mpMRI was not significantly associated with BCR after RP for PCa. Data on disease progression and PCa-specific mortality are limited. Heterogeneity among the studies was substantial.

CONCLUSIONS

The review shows that PI-RADS scores provide information on the future likelihood of cancer recurrence or progression, at least for men undergoing RP. We are of the view that this information should be taken into account to identify men at higher risk of unfavorable outcomes.

PATIENT SUMMARY

A higher Prostate Imaging-Reporting and Data System score for magnetic resonance imaging of the prostate seems to be positively associated with oncological failure in prostate cancer and should be incorporated into future risk models.

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

INTRODUCTION

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

METHODS AND ANALYSIS

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

ETHICS AND DISSEMINATION

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

PROSPERO REGISTRATION NUMBER

CRD42020206074.

Conspicuity of prostate cancer on multiparametric magnetic resonance imaging: A cross-disciplinary translational hypothesis

Pre-biopsy multiparametric magnetic resonance imaging (mpMRI) has transformed the risk stratification and diagnostic approach for suspected prostate cancer. The majority of clinically significant prostate cancers are visible on pre-biopsy mpMRI, however, there are a subset of significant tumors that are not detected by mpMRI. The radiobiological mechanisms underpinning mpMRI-visibility and invisibility of these cancers remain uncertain. Emerging evidence suggests that mpMRI-visible tumors are enriched with molecular features associated with increased disease aggressivity and poor clinical prognosis, which is supported by short-term endpoints, such as biochemical recurrence following surgery. Furthermore, at the histopathological level, mpMRI-visible tumors appear to exhibit increased architectural and vascular density compared to mpMRI-invisible disease. It seems probable that the genomic, pathological, radiological, and clinical features of mpMRI-visible and mpMRI-invisible prostate cancers are interrelated. Here, we propose a novel cross-disciplinary theory that links genomic and molecular evidence with cellular and histopathological appearances, elucidating both the mpMRI visibility and clinical status of significant prostate cancer.

Impact of PI-RADS v2 on indication of prostate biopsy

Objectives:

To identify the group of patients who could safely avoid prostate biopsy based on the findings of multiparametric prostate resonance imaging (MRmp), parameterized with PI-RADS v2, using prostate biopsy as reference test and to assess the sensitivity and specificity of mpMR in identifying clinically significant prostate cancer using prostate biopsy as a reference test.

Patients and Methods:

Three hundred and forty two patients with suspected prostate cancer were evaluated with mpMR and prostate biopsy. Agreement between imaging findings and histopathological findings was assessed using the Kappa index. The accuracy of mpMR in relation to biopsy was assessed by calculations of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results:

A total of 342 biopsies were performed. In 201 (61.4%), mpMR had a negative result for cancer, which was confirmed on biopsy in 182 (53%) of the cases, 17 (4.9%) presented non-clinically significant cancer and only 2 (0.5%) clinically significant cancer. 131 (38.3%) patients had a positive biopsy. Clinically significant cancer corresponded to 83 (34.2%), of which 81 (97.5%) had a positive result in mpMR. Considering only the clinically significant cancers the mpMR had a sensitivity of 97.6%, specificity of 76.8%, PPV 57.4% and VPN of 99%.

Conclusions:

mpMR is a useful tool to safely identify which patients at risk for prostate cancer need to undergo biopsy and has high sensitivity and specificity in identifying clinically significant prostate cancer.

Evaluation of prostate-specific antigen density in the diagnosis of prostate cancer combined with magnetic resonance imaging before biopsy in men aged 70 years and older with elevated PSA

There is an increasing proportion of individuals aged 70 years and older, as well as an increasing life expectancy worldwide. The present study may guide the management of older patients with elevated prostate specific antigen (PSA). The medical records of 241 older men aged >70 years who underwent multiparametric magnetic resonance imaging (mpMRI) before prostate biopsy (PBx) at our institution were reviewed retrospectively. Multiple variables were evaluated as predictors for the diagnosis of prostate cancer (PCa). The variables included serum PSA level, digital rectal examination, size of region of interest on mpMRI, prostate volume and PSA density. PCa was positive in 162 (67.2%). Prostate volume and PSA density were significant PCa predictors (P<0.001). In patients aged 70-75 and >75 years, PSA density was significantly higher in patients with PCa (0.21 ng/ml/cc, P=0.014 and 0.24 ng/ml/cc, P<0.001, respectively). Similarly, PSA density was significant higher in patients with significant PCa (0.24 ng/ml/cc, P=0.004 and 0.29 ng/ml/cc, P<0.001, respectively). The cut-off value of PSA density was calculated using receiver operating characteristic curves. Area under curve of PSA density was 0.698, and the best cut-off value was 0.20 ng/ml/cc. These results indicate that the combination of PSA density with mpMRI before PBx is a helpful method and can be a decision-making model for a selection of PBx.

The association between the outcomes of extraperitoneal laparoscopic radical prostatectomy and the anthropometric measurements of the prostate by magnetic resonance imaging

Introduction and objective

To determine the association between the anthropometric measurements by magnetic resonance imaging (MRI) and perioperative outcomes of extraperitoneal laparoscopic radical prostatectomy (ELRP).

Materials and Methods

From 2008 to June 2016, 86 patients underwent preoperative MRI prior to undergoing ELRP for localized prostate cancer. We analyzed the associations between anthropometric measurements of MRI and the perioperative outcomes of patients who underwent ELRP.

Results

The mean patient age was 69.61±8.30 years. The medians of operating time and blood loss were 2.30 hours and 725.30ml, respectively. The total post-surgical complication rate was 1.16%. The median hospital stay was 6.50 days. The pathological stages for T2 and T3 were 45.74% and 34.04%, respectively. The rate as positive surgical margins (PSMs) was 18.09% (pT2 and pT3; 6.38% and 9.57%). The angles between pubic bone and prostate gland (angle 1&2), were significantly associated with operative time and hospital stay, respectively (p<0.05). There was no correlation between the pelvimetry and positive surgical margin.

Conclusions

The findings of the present study suggest that anthropometric measurements of the MRI are related to operative difficulties in ELRP. This study confirmed that MRI planning is the key to preventing complications in ELRP.

Risk Stratification of Newly Diagnosed Prostate Cancer with Genomic Platforms

INTRODUCTION

Interest in novel risk stratification tools for men with newly diagnosed prostate cancer has flourished, aiming to offer increasingly accurate predictions of future disease behavior to ultimately better guide clinical management. We highlight the use of genomic platforms attempting to refine clinical decisions at the point of initial diagnosis.

METHODS

In the context of a benchmark standard of clinical risk stratification tools we reviewed the role of genomic tests, including individual gene expression assays, as well as a growing number of tissue based expression tests assessing multiple gene panels, to improve predictions at initial diagnosis.

RESULTS

The role of single gene status including TMPRSS2:ERG fusion and PTEN expression has been investigated among men with newly diagnosed prostate cancer. Gene expression profiles incorporating panels of genes associated with prostate cancer outcome have received external validation and have commercial application in assays that incorporate baseline clinical risk to offer predictions of immediate pathological and downstream disease end points. Comparisons of gene signatures have offered insights into relative predictive performance in archival tissue. However, to date no studies appear to directly support a single genomic assay offering superior clinical usefulness for decision making at the time of diagnosis.

CONCLUSIONS

Risk stratification tools incorporating genomic analysis of prostate cancer have been developed which seek to improve the accuracy of initial predictions. Further study is warranted to define the additive clinical benefit associated with their use if implemented broadly.

DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic

Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice.

Review of Prostate Imaging Reporting and Data System version 2

Prostate MRI has been a hot topic in recent years in large part due to the high incidence of prostate cancer worldwide. Advances in technology have allowed multiparametric MRI to improve lesion detection and characterization in prostate imaging. Additionally, prostate MRI has shown great promise in the detection of clinically significant cancer. In 2012, the European Society of Urogenital Radiology established clinical guidelines for multiparametric MRI of the prostate to facilitate a greater level of standardization and consistency, which became known as the Prostate Imaging Reporting and Data System (PI-RADS). Subsequently, the American College of Radiology, European Society of Urogenital Radiology and the AdMeTech Foundation jointly created PI-RADS version 2. This article focuses on summarizing the key points of PI-RADS version 2.

Risk stratification of prostate cancer: integrating multiparametric MRI, nomograms and biomarkers

Accurate risk stratification of prostate cancer is achieved with a number of existing tools to ensure the identification of at-risk patients, characterization of disease aggressiveness, prediction of cancer burden and extrapolation of treatment outcomes for appropriate management of the disease. Statistical tables and nomograms using classic clinicopathological variables have long been the standard of care. However, the introduction of multiparametric MRI, along with fusion-guided targeted prostate biopsy and novel biomarkers, are being assimilated into clinical practice. The majority of studies to date present the outcomes of each in isolation. The current review offers a critical and objective assessment regarding the integration of multiparametric MRI and fusion-guided prostate biopsy with novel biomarkers and predictive nomograms in contemporary clinical practice.

Preoperative Multiparametric Magnetic Resonance Imaging Predicts Biochemical Recurrence in Prostate Cancer after Radical Prostatectomy

Objectives

To evaluate the utility of preoperative multiparametric magnetic resonance imaging (MP-MRI) in predicting biochemical recurrence (BCR) following radical prostatectomy (RP).

Materials/Methods

From March 2007 to January 2015, 421 consecutive patients with prostate cancer (PCa) underwent preoperative MP-MRI and RP. BCR-free survival rates were estimated using the Kaplan-Meier method. Cox proportional hazards models were used to identify clinical and imaging variables predictive of BCR. Logistic regression was performed to generate a nomogram to predict three-year BCR probability.

Results

Of the total cohort, 370 patients met inclusion criteria with 39 (10.5%) patients experiencing BCR. On multivariate analysis, preoperative prostate-specific antigen (PSA) (p = 0.01), biopsy Gleason score (p = 0.0008), MP-MRI suspicion score (p = 0.03), and extracapsular extension on MP-MRI (p = 0.03) were significantly associated with time to BCR. A nomogram integrating these factors to predict BCR at three years after RP demonstrated a c-index of 0.84, outperforming the predictive value of Gleason score and PSA alone (c-index 0.74, p = 0.02).

Conclusion

The addition of MP-MRI to standard clinical factors significantly improves prediction of BCR in a post-prostatectomy PCa cohort. This could serve as a valuable tool to support clinical decision-making in patients with moderate and high-risk cancers.

Prediction of biochemical recurrence after radical prostatectomy with PI-RADS version 2 in prostate cancers: initial results

OBJECTIVES

To determine whether the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) helps predict biochemical recurrence (BCR) after radical prostatectomy for prostate cancer (PCa).

METHODS

We included 158 patients with PCa who underwent magnetic resonance imaging (MRI) and radical prostatectomy (RP). Clinical (prostate-specific antigen, greatest percentage of core, and percentage of positive core number), PI-RADSv2 score on MRI, and surgical parameters (Gleason score, extracapsular extension, seminal vesicle invasion, and tumour volume) were investigated. Univariate and multivariate analyses using Cox's proportional hazards model were performed to assess parameters predictive of BCR (two consecutive prostate specific antigens ≥0.2 ng/ml). Kaplan-Meier survival curves were analyzed.

RESULTS

The rate of BCR was 13.3 % (21/158) after surgery (median follow-up, 25 months; range, 12-36). No subject with a PI-RADS score <4 had BCR. In univariate analysis, all parameters were significant for BCR (p < 0.05), except seminal vesicle invasion (p = 0.254). Meanwhile, PI-RADS score was the only independent parameter for BCR in multivariate analysis (p < 0.05). Two-year, BCR-free survival post-RP was significantly lower for PI-RADS ≥4 (84.7-85.5 %) than for PI-RADS <4 (100 %; p < 0.05).

CONCLUSION

As a preoperative imaging tool, PI-RADSv2 may be useful to predict BCR after radical prostatectomy for PCa.

KEY POINTS

• No subject with PI-RADS <4 had BCR after RP • PI-RADSv2 was the only predictor of BCR in multivariate analysis • Two-year, BCR-free survival following RP was lower for PI-RADS≥4 than for PI-RADS<4 • Inter-rater agreement was good for PI-RADS ≥4 or not.

Value of preoperative MRI for prostate cancer staging and continence outcomes prior to prostatectomy: A review of the literature

Pelvic imaging in newly diagnosed prostate cancer is primarily used for staging prior to definitive treatment. Over the past decade use of magnetic resonance imaging (MRI) for pre-surgical planning has increased, as well has he technology and methods for performing prostate MRI. To investigate and define the different MRI technologies available and further assess MRI technology ability to predict pathologic stage. Searching PubMed, we identified current published literature, where the cohort population underwent pre-operative MRI followed by prostatectomy. Keywords used in the PubMed literature search included: MRI, prostate cancer, prostate cancer staging, multiparamentric MRI and incontinence. Papers were included for review if they discussed use of MRI prior to prostatectomy and had corresponding pathologic data, staging, incontinence, and surgical outcomes. Primary information noted was MRI sensitivity, specificity and overall accuracy for detecting extracapsular extension (ECE) and seminal vesicle involvement (SVI). Secondary information derived included assessing the surgical influence of staging information, and identifying predictors of urinary incontinence recovery. Review of the literature showed that in regards to extracapsular extension the reported MRI accuracy ranged from 76%-98%, sensitivity from 20%-90% and specificity from 82%-99%. As for seminal vesicle involvement the reported MRI accuracy ranged from 76%-98%, sensitivity from 20%-90% and specificity from 82%-99%. There is a widely varying sensitivity and specificity for both ECE and SVI and the wide variability in the MRI technology used in the literature supports that use of MRI technology for prostate cancer remains investigational.