Multiparametric Magnetic Resonance Imaging Before Prostate Biopsy: A Chain is Only as Strong as its Weakest Link

MRI-targeted biopsy cores from prostate index lesions: assessment and prediction of the number needed

BACKGROUND

Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model.

METHODS

We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates.

RESULTS

The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model.

CONCLUSION

The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates.

Comparing the Diagnostic Performance of Multiparametric Prostate MRI Versus 68Ga-PSMA PET-CT in the Evaluation Lymph Node Involvement and Extraprostatic Extension

PURPOSE

Our research aims to compare the efficacy of PET and MRI for lymph node metastasis and extraprostatic extension in cases with newly diagnosed prostate cancer undergoing radical prostatectomy with extended pelvic lymph node dissection.

METHODS

Thirty-nine cases who underwent radical prostatectomy with pelvic lymph node dissection between June 2015 and January 2020 were included in the study. Patients with gallium (ga-68 Prostate-specific membrane antigen (PSMA) PET) PSMA PET-CT and multiparametric (mp) prostate MRI performed according to PIRADS v2 criteria in our clinic were included.

RESULTS

The extraprostatic extension was observed in 16 cases. The sensitivity of MR in detecting extracapsular invasion was calculated as 56.2%, specificity 82.6%, positive predictive value (PPV) 69.2%, negative predictive value (NPV) 73.0%. The sensitivity of PET was 62.5%, specificity 60.8%, PPV 52.6%, NPV 70%. Eleven lymph node metastases were observed in nine cases. The sensitivity, specificity, PPV and NPV of metastatic lymph node detection were; 36.3%, 99.6%, 57.1%, 99.0% for MRI and; 18.1%, 99.4%, 33.3%, 98.8% for PET CT, respectively.

CONCLUSION

Mp prostate MRI showed low sensitivity and high specificity compared to PSMA PET CT in extracapsular invasion evaluation. The sensitivity of both modalities in the detection of metastatic lymph nodes was low.

Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data

Objectives

Prostate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy.

Materials and Methods

The data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which consisted of 342 patients, were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, decision curve analysis was also performed.

Results

The results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (PI-RADS) grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected.

Conclusions

In this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.

Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

Diagnostic Yield of Incremental Biopsy Cores and Second Lesion Sampling for In-Gantry MRI-Guided Prostate Biopsy

BACKGROUND. In-gantry MRI-guided biopsy (MRGB) of the prostate has been shown to be more accurate than other targeted prostate biopsy methods. However, the optimal number of cores to obtain during in-gantry MRGB remains undetermined. OBJECTIVE. The purpose of this study was to assess the diagnostic yield of obtaining an incremental number of cores from the primary lesion and of second lesion sampling during in-gantry MRGB of the prostate. METHODS. This retrospective study included 128 men with 163 prostate lesions who underwent in-gantry MRGB between 2016 and 2019. The men had a total of 163 lesions sampled with two or more cores, 121 lesions sampled with three or more cores, and 52 lesions sampled with four or more cores. A total of 40 men underwent sampling of a second lesion. Upgrade on a given core was defined as a greater International Society of Urological Pathology (ISUP) grade group (GG) relative to the previously obtained cores. Clinically significant prostate cancer (csPCa) was defined as ISUP GG 2 or greater. RESULTS. The frequency of any upgrade was 12.9% (21/163) on core 2 versus 10.7% (13/121) on core 3 (p = .29 relative to core 2) and 1.9% (1/52) on core 4 (p = .03 relative to core 3). The frequency of upgrade to csPCa was 7.4% (12/163) on core 2 versus 4.1% (5/121) on core 3 (p = .13 relative to core 2) and 0% (0/52) on core 4 (p = .07 relative to core 3). The frequency of upgrade on core 2 was higher for anterior lesions (p < .001) and lesions with a higher PI-RADS score (p = .007); the frequency of upgrade on core 3 was higher for apical lesions (p = .01) and lesions with a higher PI-RADS score (p = .01). Sampling of a second lesion resulted in an upgrade in a single patient (2.5%; 1/40); both lesions were PI-RADS category 4 and showed csPCa. CONCLUSION. When performing in-gantry MRGB of the prostate, obtaining three cores from the primary lesion is warranted to optimize csPCa diagnosis. Obtaining a fourth core from the primary lesion or sampling a second lesion has very low yield in upgrading cancer diagnoses. CLINICAL IMPACT. To reduce patient discomfort and procedure times, operators may refrain from obtaining more than three cores or second lesion sampling.

Managing Discordant Findings Between Multiparametric Magnetic Resonance Imaging and Transrectal Magnetic Resonance Imaging-directed Prostate Biopsy-The Key Role of Magnetic Resonance Imaging-directed Transperineal Biopsy

BACKGROUND

Discordant findings between multiparametric magnetic resonance imaging (mpMRI) and transrectal image-guided biopsies of the prostate (TRUS-P) may result in inadequate risk stratification of localized prostate cancer.

OBJECTIVE

To assess transperineal image-guided biopsies of the index target (TPER-IT) in terms of disease reclassification and treatment recommendations.

DESIGN, SETTING, AND PARTICIPANTS

Cases referred for suspicion or treatment of localized prostate cancer were reviewed in a multidisciplinary setting, and discordance was characterized into three scenarios: type I-negative biopsies or International Society of Urological Pathology (ISUP) grade 1 cancer in Prostate Imaging Reporting and Data System (PI-RADS) ≥4 index target (IT); type II-negative biopsies or ISUP grade 1 cancer in anterior IT; and type III-<3 mm stretch of cancer in PI-RADS ≥3 IT. Discordant findings were characterized in 132/558 (23.7%) patients after TRUS-P. Of these patients, 102 received reassessment TPER-IT.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary objective was to report changes in treatment recommendations after TPER-IT. Therefore, cores obtained by primary TRUS-P and TPER-IT were analyzed in terms of cancer detection, ISUP grade, and Cambridge Prognostic Group classification using descriptive statistics.

RESULTS AND LIMITATIONS

TPER-IT biopsies that consisted of fewer cores than the initial TRUS-P (seven vs 14, p < 0.0001) resulted in more cancer tissue materials for analysis (56 vs 42.5 mm, p = 0.0003). As a result, 40% of patients initially considered for follow-up (12/30) and 49% for active surveillance (30/61) were reassigned after TPER-IT to surgery or intensity-modulated radiotherapy.

CONCLUSIONS

Nonconcordance between pathology and imaging was observed in a significant proportion of patients receiving TRUS-P. TPER-IT better informed the presence and grade of cancer, resulting in a significant impact on treatment recommendations. A multidisciplinary review of mpMRI and TRUS-P findings and reassessment TPER-IT in type I-II discordances is recommended.

PATIENT SUMMARY

In this report, patients with suspicious imaging of the prostate, but no or well-differentiated cancer on transrectal image-guided -biopsies, were offered transperineal image-guided biopsies for reassessment. We found that a large share of these had a more aggressive cancer than initially suspected. We conclude that discordant results warrant reassessment transperineal image-guided biopsies as these may impact disease risk classification and treatment recommendations.

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

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

Comparison of MRI, PSMA PET/CT, and Fusion PSMA PET/MRI for Detection of Clinically Significant Prostate Cancer

PURPOSE

The aim of our study is to compare the efficacy of positron emission tomography (PET) and magnetic resonance imaging (MRI) for detecting intraprostatic lesions in patients with clinically significant prostate cancer who underwent radical prostatectomy; additionally, investigate the benefits of rostate-specific membrane antigen (PSMA) PET-MR software fusion images to the diagnosis.

METHODS

Thirty patients, who underwent radical prostatectomy between June 2015 and April 2018, were included in the study. Subjects with gallium PSMA PET-CT and multiparametric prostate MRI performed according to Prostate Imaging Reporting and Data System v2 criteria in our clinic were included in the study. 68Ga-PSMA PET-CT images were fused with MR sequences for analysis.

RESULTS

The mean age of cases was 63.2 years (ranged from 45 to 79 years). Index lesions of 29 cases were detected by MRI and 22 of them by PET CT. Both modalities were found to be less sensitive for detection of bilaterality and multifocality (42.85% and 20% for MRI, 28.57% and 20% for PET CT, respectively). There was no statistically significant difference between modalities. It was observed that if a clinically significant tumor focus was not detected by MRI, it was small (6 mm or less) in diameter or had a low Gleason score.

CONCLUSIONS

Software fusion PSMA PET-MRI increased the sensitivity of the index lesion identification compared with PSMA PET-CT and also increased the sensitivity of real lesion size identification compared with multiparametric prostate MRI.

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

OBJECTIVES

To develop an automatic method for identification and segmentation of clinically significant prostate cancer in low-risk patients and to evaluate the performance in a routine clinical setting.

METHODS

A consecutive cohort (n = 292) from a prospective database of low-risk patients eligible for the active surveillance was selected. A 3-T multi-parametric MRI at 3 months after inclusion was performed. Histopathology from biopsies was used as reference standard. MRI positivity was defined as PI-RADS score ≥ 3, histopathology positivity was defined as ISUP grade ≥ 2. The selected cohort contained four patient groups: (1) MRI-positive targeted biopsy-positive (n = 116), (2) MRI-negative systematic biopsy-negative (n = 55), (3) MRI-positive targeted biopsy-negative (n = 113), (4) MRI-negative systematic biopsy-positive (n = 8). Group 1 was further divided into three sets and a 3D convolutional neural network was trained using different combinations of these sets. Two MRI sequences (T2w, b = 800 DWI) and the ADC map were used as separate input channels for the model. After training, the model was evaluated on the remaining group 1 patients together with the patients of groups 2 and 3 to identify and segment clinically significant prostate cancer.

RESULTS

The average sensitivity achieved was 82-92% at an average specificity of 43-76% with an area under the curve (AUC) of 0.65 to 0.89 for different lesion volumes ranging from > 0.03 to > 0.5 cc.

CONCLUSIONS

The proposed deep learning computer-aided method yields promising results in identification and segmentation of clinically significant prostate cancer and in confirming low-risk cancer (ISUP grade ≤ 1) in patients on active surveillance.

KEY POINTS

• Clinically significant prostate cancer identification and segmentation on multi-parametric MRI is feasible in low-risk patients using a deep neural network. • The deep neural network for significant prostate cancer localization performs better for lesions with larger volumes sizes (> 0.5 cc) as compared to small lesions (> 0.03 cc). • For the evaluation of automatic prostate cancer segmentation methods in the active surveillance cohort, the large discordance group (MRI positive, targeted biopsy negative) should be included.

Measuring the Quality of Diagnostic Prostate Magnetic Resonance Imaging: A Urologist's Perspective

Focus on the quality of magnetic resonance imaging (MRI) by radiologists is welcome, but the clinical impacts that arise from MRI scans still need urological expertise. The urologist perspective is required in a multidisciplinary team setting when making decisions on whether to repeat a scan or perform a biopsy. This can ensure effective use of the prostate MRI diagnostic pathway in delivering desired clinical benefits for patients.

The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma

Five years after the last prostatic carcinoma grading consensus conference of the International Society of Urological Pathology (ISUP), accrual of new data and modification of clinical practice require an update of current pathologic grading guidelines. This manuscript summarizes the proceedings of the ISUP consensus meeting for grading of prostatic carcinoma held in September 2019, in Nice, France. Topics brought to consensus included the following: (1) approaches to reporting of Gleason patterns 4 and 5 quantities, and minor/tertiary patterns, (2) an agreement to report the presence of invasive cribriform carcinoma, (3) an agreement to incorporate intraductal carcinoma into grading, and (4) individual versus aggregate grading of systematic and multiparametric magnetic resonance imaging-targeted biopsies. Finally, developments in the field of artificial intelligence in the grading of prostatic carcinoma and future research perspectives were discussed.

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

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

Advanced ultrasound in the diagnosis of prostate cancer

The diagnosis of prostate cancer (PCa) can be challenging due to the limited performance of current diagnostic tests, including PSA, digital rectal examination and transrectal conventional US. Multiparametric MRI has improved PCa diagnosis and is recommended prior to biopsy; however, mp-MRI does miss a substantial number of PCa. Advanced US modalities include transrectal prostate elastography and contrast-enhanced US, as well as improved B-mode, micro-US and micro-Doppler techniques. These techniques can be combined to define a novel US approach, multiparametric US (mp-US). Mp-US improves PCa diagnosis but is not sufficiently accurate to obviate the utility of mp-MRI. Mp-US using advanced techniques and mp-MRI provide complementary information which will become even more important in the era of focal therapy, where precise identification of PCa location is needed.

Detection of individual prostate cancer via multiparametric magnetic resonance imaging in own material - initial experience

Purpose

Multiparametric magnetic resonance imaging (mpMRI) is an evolving non-invasive imaging modality that increases the accurate localization of prostate cancer (PCa) at the time of MRI targeted biopsy, enhancing clinical risk assessment, and improving the ability to appropriately counsel patients regarding therapy.

Material and methods

A total of forty patients with prostate-specific antigen (PSA), mpMRI and Gleason score (based on MRI template-guided cognitive biopsy) results were analyzed in this study, with eight patients (20%) diagnosed with PCa. The mpMRI was performed to facilitate the decision to perform prostate biopsy. Spearman’s coefficient analysis was used to evaluate the relationships between characteristics. Diagnostic performance was assessed measuring the area under the curve (AUC) of the receiver operating characteristic (ROC) analysis. Diagnostic accuracy, sensitivity, and specificity were determined using the best cut-off on each ROC.

Results

Out of all the study group, 55% of patients were subjected to primary biopsy and 45% were directed to repeated TRUS-Bx with the suspicion of prostate cancer. Forty suspected lesions on MRI images were identified with 5% of PI-RADS 1, 17.5% of PI-RADS 2, 32.5% of PI-RADS 3, 27.5% of PI-RADS 4 (27.5%) and 17.5% of PI-RADS 5. The highest correlation was observed for mpMRI results and Gleason score with Spearman’s coefficient equal to 0.41 (95% CI: 0.104-0.646). ROC analysis revealed that mpMRI discriminates between directing the patients for prostate biopsy or active surveillance with AUC = 0.771 (0.117, 95% CI: 0.542-1.001).

Conclusions

Introducing pre-biopsy mpMRI into our contemporary PCa diagnosis pathway increased the diagnostic yield of transrectal biopsy by increasing the prostate cancer detection. This enabled the introduction of clinically significant prostate cancer (csPCa) treatment. mpMRI application also allowed biopsy to be avoided among patients with no csPCa.

Can the addition of clinical information improve the accuracy of PI-RADS version 2 for the diagnosis of clinically significant prostate cancer in positive MRI?

AIM

To report prostate cancer (PCa) prevalence in Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) categories and investigate the potential to avoid unnecessary, magnetic resonance imaging (MRI)-guided in-bore biopsies by adding clinical and biochemical patient characteristics.

MATERIALS AND METHODS

The present institutional review board-approved, prospective study on 137 consecutive men with 178 suspicious lesions on 3 T MRI was performed. Routine data collected for each patient included patient characteristics (age, prostate volume), clinical background information (prostate-specific antigen [PSA] levels, PSA density), and PI-RADS v2 scores assigned in a double-reading approach.

RESULTS

Histopathological evaluation revealed a total of 93/178 PCa (52.2%). The mean age was 66.3 years and PSA density was 0.24 ng/ml² (range, 0.04-0.89 ng/ml). Clinically significant PCa (csPCa, Gleason score >6) was confirmed in 50/93 (53.8%) lesions and was significantly associated with higher PI-RADS v2 scores (p=0.0044). On logistic regression analyses, age, PSA density, and PI-RADS v2 scores contributed independently to the diagnosis of csPCa (p=7.9×10^-7, p=0.097, and p=0.024, respectively). The resulting area under the receiver operating characteristic curve (AUC) to predict csPCa was 0.76 for PI-RADS v2, 0.59 for age, and 0.67 for PSA density. The combined regression model yielded an AUC of 0.84 for the diagnosis of csPCa and was significantly superior to each single parameter (p≤0.0009, respectively). Unnecessary biopsies could have been avoided in 50% (64/128) while only 4% (2/50) of csPCa lesions would have been missed.

CONCLUSIONS

Adding age and PSA density to PI-RADS v2 scores improves the diagnostic accuracy for csPCa. A combination of these variables with PI-RADS v2 can help to avoid unnecessary in-bore biopsies while still detecting the majority of csPCa.

Urinary DNA methylation biomarkers for prediction of prostate cancer upgrading and upstaging

BACKGROUND

Significant numbers of prostate cancer (PCa) patients experience tumour upstaging and upgrading in surgical specimens that cause serious problems in timely and proper selection of the treatment strategy. This study was aimed at the evaluation of a set of established epigenetic biomarkers as a noninvasive tool for more accurate PCa categorization before radical prostatectomy (RP).

METHODS

Quantitative methylation-specific PCR was applied for the methylation analysis of RARB, RASSF1, and GSTP1 in 514 preoperatively collected voided or catheterized urine samples from the single-centre cohort of 1056 treatment-naïve PCa patients who underwent RP. The rates of biopsy upgrading and upstaging were analysed in the whole cohort.

RESULTS

Pathological examination of RP specimens revealed Gleason score upgrading in 27.2% and upstaging in 20.3% of the patients with a total misclassification rate of 39.0%. DNA methylation changes in at least one gene were detected in more than 80% of urine samples. Combination of the PSA test with the three-gene methylation analysis in urine was a significant predictor of pathological upstaging and upgrading (P < 0.050), however, with limited increase in overall accuracy. The PSA test or each gene alone was not informative enough.

CONCLUSIONS

The urinary DNA methylation assay in combination with serum PSA may predict tumour stage or grade migration post-RP aiding in improved individual risk assessment and appropriate treatment selection. Clinical utility of these biomarkers should be proven in larger multi-centre studies.

Preoperative PI-RADS Version 2 scores helps improve accuracy of clinical nomograms for predicting pelvic lymph node metastasis at radical prostatectomy

BACKGROUND

Lymph node invasion (LNI) is a strong adverse prognostic factor in prostate cancer (PCa). The purpose of this study was to evaluate the role of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) scores for estimating the risk of LN metastasis. The study also aimed to investigate the additional value of PI-RADSv2 scores when used in combination with clinical nomograms for the prediction of LNI in patients with PCa.

METHODS

We retrospectively identified 308 patients who underwent multiparametric magnetic resonance imaging (mpMRI) and RP with pelvic lymph node dissection (PLND). Clinicopathological parameters and PI-RADSv2 scores were assessed. Univariate and multivariate logistic analyses were performed. The area under the receiver operating characteristic curves (AUCs) and decision curve analysis (DCA) were generated for assessing the incremental value of PI-RADSv2 scores combined with the Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms.

RESULTS

Overall, 20 (6.5%) patients had LNI. At univariate analysis, all clinicopathological characteristics and PI-RADSv2 scores were significantly associated to LNI (p < 0.04). However, multivariate analysis revealed that only PI-RADSv2 scores and percentage of positive cores were independently significant (p ≤ 0.006). The PI-RADSv2 score was the most accurate predictor (AUC, 80.2%). The threshold of PI-RADSv2 score was 5, which provided high sensitivity (18/20, 90.0%) and negative predictive value (203/205, 99.0%). When PI-RADSv2 scores were combined with Briganti and MSKCC nomograms, the AUC value increased from 75.1 to 86.3% and from 79.2 to 87.9%, respectively (p ≤ 0.001). The DCA also demonstrated that the two nomograms plus PI-RADSv2 scores improved clinical risk prediction of LNI.

CONCLUSIONS

The patients with a PI-RADSv2 score <5 were associated with a very low risk of LNI in PCa. Preoperative PI-RADSv2 scores could help improve the accuracy of clinical nomograms for predicting pelvic LN metastasis at radical prostatectomy.

Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer

BACKGROUND

Multiparametric magnetic resonance imaging (MRI), with or without MRI-targeted biopsy, is an alternative test to systematic transrectal ultrasonography-guided biopsy in men suspected of having prostate cancer. At present, evidence on which test to use is insufficient to inform detailed evidence-based decision-making.

OBJECTIVES

To determine the diagnostic accuracy of the index tests MRI only, MRI-targeted biopsy, the MRI pathway (MRI with or without MRI-targeted biopsy) and systematic biopsy as compared to template-guided biopsy as the reference standard in detecting clinically significant prostate cancer as the target condition, defined as International Society of Urological Pathology (ISUP) grade 2 or higher. Secondary target conditions were the detection of grade 1 and grade 3 or higher-grade prostate cancer, and a potential change in the number of biopsy procedures.

SEARCH METHODS

We performed a comprehensive systematic literature search up to 31 July 2018. We searched CENTRAL, MEDLINE, Embase, eight other databases and one trials register.

SELECTION CRITERIA

We considered for inclusion any cross-sectional study if it investigated one or more index tests verified by the reference standard, or if it investigated the agreement between the MRI pathway and systematic biopsy, both performed in the same men. We included only studies on men who were biopsy naïve or who previously had a negative biopsy (or a mix of both). Studies involving MRI had to report on both MRI-positive and MRI-negative men. All studies had to report on the primary target condition.

DATA COLLECTION AND ANALYSIS

Two reviewers independently extracted data and assessed the risk of bias using the QUADAS-2 tool. To estimate test accuracy, we calculated sensitivity and specificity using the bivariate model. To estimate agreement between the MRI pathway and systematic biopsy, we synthesised detection ratios by performing random-effects meta-analyses. To estimate the proportions of participants with prostate cancer detected by only one of the index tests, we used random-effects multinomial or binary logistic regression models. For the main comparisions, we assessed the certainty of evidence using GRADE.

MAIN RESULTS

The test accuracy analyses included 18 studies overall.MRI compared to template-guided biopsy: Based on a pooled sensitivity of 0.91 (95% confidence interval (CI): 0.83 to 0.95; 12 studies; low certainty of evidence) and a pooled specificity of 0.37 (95% CI: 0.29 to 0.46; 12 studies; low certainty of evidence) using a baseline prevalence of 30%, MRI may result in 273 (95% CI: 249 to 285) true positives, 441 false positives (95% CI: 378 to 497), 259 true negatives (95% CI: 203 to 322) and 27 (95% CI: 15 to 51) false negatives per 1000 men. We downgraded the certainty of evidence for study limitations and inconsistency.MRI-targeted biopsy compared to template-guided biopsy: Based on a pooled sensitivity of 0.80 (95% CI: 0.69 to 0.87; 8 studies; low certainty of evidence) and a pooled specificity of 0.94 (95% CI: 0.90 to 0.97; 8 studies; low certainty of evidence) using a baseline prevalence of 30%, MRI-targeted biopsy may result in 240 (95% CI: 207 to 261) true positives, 42 (95% CI: 21 to 70) false positives, 658 (95% CI: 630 to 679) true negatives and 60 (95% CI: 39 to 93) false negatives per 1000 men. We downgraded the certainty of evidence for study limitations and inconsistency.The MRI pathway compared to template-guided biopsy: Based on a pooled sensitivity of 0.72 (95% CI: 0.60 to 0.82; 8 studies; low certainty of evidence) and a pooled specificity of 0.96 (95% CI: 0.94 to 0.98; 8 studies; low certainty of evidence) using a baseline prevalence of 30%, the MRI pathway may result in 216 (95% CI: 180 to 246) true positives, 28 (95% CI: 14 to 42) false positives, 672 (95% CI: 658 to 686) true negatives and 84 (95% CI: 54 to 120) false negatives per 1000 men. We downgraded the certainty of evidence for study limitations, inconsistency and imprecision.Systemic biopsy compared to template-guided biopsy: Based on a pooled sensitivity of 0.63 (95% CI: 0.19 to 0.93; 4 studies; low certainty of evidence) and a pooled specificity of 1.00 (95% CI: 0.91 to 1.00; 4 studies; low certainty of evidence) using a baseline prevalence of 30%, systematic biopsy may result in 189 (95% CI: 57 to 279) true positives, 0 (95% CI: 0 to 63) false positives, 700 (95% CI: 637 to 700) true negatives and 111 (95% CI: 21 to 243) false negatives per 1000 men. We downgraded the certainty of evidence for study limitations and inconsistency.Agreement analyses: In a mixed population of both biopsy-naïve and prior-negative biopsy men comparing the MRI pathway to systematic biopsy, we found a pooled detection ratio of 1.12 (95% CI: 1.02 to 1.23; 25 studies). We found pooled detection ratios of 1.44 (95% CI 1.19 to 1.75; 10 studies) in prior-negative biopsy men and 1.05 (95% CI: 0.95 to 1.16; 20 studies) in biopsy-naïve men.

AUTHORS' CONCLUSIONS

Among the diagnostic strategies considered, the MRI pathway has the most favourable diagnostic accuracy in clinically significant prostate cancer detection. Compared to systematic biopsy, it increases the number of significant cancer detected while reducing the number of insignificant cancer diagnosed. The certainty in our findings was reduced by study limitations, specifically issues surrounding selection bias, as well as inconsistency. Based on these findings, further improvement of prostate cancer diagnostic pathways should be pursued.