Multiparametric MRI for prostate cancer detection: Performance in patients with prostate-specific antigen values between 2.5 and 10 ng/mL

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm² and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

Detection of prostate cancer using prostate imaging reporting and data system score and prostate-specific antigen density in biopsy-naive and prior biopsy-negative patients

Background

Few studies report on indications for prostate biopsy using Prostate Imaging–Reporting and Data System (PI-RADS) score and prostate-specific antigen density (PSAD). No study to date has included biopsy-naïve and prior biopsy-negative patients. Therefore, we evaluated the predictive values of the PI-RADS, version 2 (v2) score combined with PSAD to decrease unnecessary biopsies in biopsy-naïve and prior biopsy-negative patients.

Materials and methods

A total of 1,098 patients who underwent multiparametric magnetic resonance imaging at our hospital before a prostate biopsy and who underwent their second prostate biopsy with an initial benign negative prostatic biopsy were included. We found factors associated with clinically significant prostate cancer (csPca). We assessed negative predictive values by stratifying biopsy outcomes by prior biopsy history and PI-RADS score combined with PSAD.

Results

The median age was 65 years (interquartile range: 59-70), and the median PSA was 5.1 ng/mL (interquartile range: 3.8-7.1). Multivariate logistic regression analysis revealed that age, prostate volume, PSAD, and PI-RADS score were independent predictors of csPca. In a biopsy-naïve group, 4% with PI-RADS score 1 or 2 had csPca; in a prior biopsy-negative group, 3% with PI-RADS score 1 or 2 had csPca. The csPca detection rate was 2.0% for PSA density <0.15 ng/mL/mL and 4.0% for PSA density 0.15-0.3 ng/mL/mL among patients with PI-RADS score 3 in a biopsy-naïve group. The csPca detection rate was 1.8% for PSA density <0.15 ng/mL/mL and 0.15-0.3 ng/mL/mL among patients with PI-RADS score 3 in a prior biopsy-negative group.

Conclusion

Patients with PI-RADS v2 score ≤2, regardless of PSA density, may avoid unnecessary biopsy. Patients with PI-RADS score 3 may avoid unnecessary biopsy through PSA density results.

Correlation of Prostate-Imaging Reporting and Data Scoring System scoring on multiparametric prostate magnetic resonance imaging with histopathological factors in radical prostatectomy material in Turkish prostate cancer patients: a multicenter study of the Urooncology Association

Background

Histopathological features after radical prostatectomy (RP) provide important information for the prognosis of prostate cancer (PCa). The possible correlations between Prostate-Imaging Reporting and Data Scoring System (PIRADS) scores in multiparametric magnetic resonance imaging (mpMRI) may also be predictive for prognosis. In this study, we aimed to evaluate the correlation of PIRADS scores with histopathological data.

Methods

A total of 177 patients who underwent preoperative mpMRI and RP for PCa from eight institutions were included in the study. Correlation of PIRADS score in preoperative mpMRI with adverse histopathological factors in RP specimen was investigated using univariate and multivariate analyses.

Results

The relationship between PIRADS score and postoperative extracapsular extension, lymphovascular invasion, and seminal vesicle involvement was significant (P < 0.001, P = 0.032, and P = 0.007, respectively). Although the PIRADS score was significantly correlated with the number of dissected lymph nodes (p = 0.026), it had no significant correlation with the number of positive nodes (P = 0.611). Total Gleason score, extracapsular extension, seminal vesicle invasion, and number of lymph nodes were found to be independent factors, which correlated with high PIRADS scores in ordinal logistic regression analysis.

Conclusion

PIRADS scoring system in mpMRI showed a statistically significant correlation with adverse histopathological factors in RP specimen. A higher PIRADS score may help to predict a higher Gleason score, indicating clinically important PCa as well as poor prognotic factors such as extracapsular extension, lymphovascular invasion, and seminal vesicle invasion that may indicate a higher risk of recurrence and the need for additional treatment.

Accuracy of multiparametric magnetic resonance imaging for diagnosing prostate Cancer: a systematic review and meta-analysis

BACKGROUND

The application of multiparametric magnetic resonance imaging (mpMRI) for diagnosis of prostate cancer has been recommended by the European Association of Urology (EAU), National Comprehensive Cancer Network (NCCN), and European Society of Urogenital Radiology (ESUR) guidelines. The purpose of this study is to systematically review the literature on assessing the accuracy of mpMRI in patients with suspicion of prostate cancer.

METHOD

We searched Embase, Pubmed and Cochrane online databases from January 12,000 to October 272,018 to extract articles exploring the possibilities that the pre-biopsy mpMRI can enhance the diagnosis accuracy of prostate cancer. The numbers of true- and false-negative results and true- and false-positive ones were extracted to calculate the corresponding sensitivity and specificity of mpMRI. Study quality was assessed using QUADAS-2 tool. Random effects meta-analysis and a hierarchical summary receiver operating characteristic (HSROC) plot were performed for further study.

RESULTS

After searching, we acquired 3741 articles for reference, of which 29 studies with 8503 participants were eligible for inclusion. MpMRI maintained impressive diagnostic value, the area under the HSROC curve was 0.87 (95%CI,0.84-0.90). The sensitivity and specificity for mpMRI were 0.87 [95%CI, 0.81-0.91] and 0.68 [95%CI,0.56-0.79] respectively. The positive likelihood ratio was 2.73 [95%CI 1.90-3.90]; negative likelihood ratio was 0.19 [95% CI 0.14,-0.27]. The risk of publication bias was negligible with P = 0.96.

CONCLUSION

Results of the meta-analysis suggest that mpMRI is a sensitive tool to diagnose prostate cancer. However, because of the high heterogeneity existing among the included studies, further studies are needed to apply the results of this meta-analysis in clinic.

Magnetic resonance spectroscopy imaging in diagnosis of suspicious prostate cancer: A meta-analysis

BACKGROUND

This meta-analysis was conducted to assess the value of magnetic resonance spectroscopy imaging (MRSI) in the diagnosis of suspected prostate cancer (PC).

METHODS

We identified all the relevant papers from the EMBASE, PubMed, EBSCO, Web of Science, and Cochrane Library databases and screened the reference lists. The quality assessment of diagnostic accuracy studies-version 2 tool was used to assess the study quality. Publication bias was analyzed using Deeks' funnel plot asymmetry test. We calculated the pooled sensitivities, specificities, positive likelihood ratios, negative likelihood ratios, diagnostic odds ratio (DOR), and 95% confidence intervals. The results were evaluated by summary receiver-operating characteristic curves (SROCs). Ultimately, a univariable meta-regression and subgroup analysis, Fagan plot, and likelihood matrix were used to analyze this review.

RESULTS

A total of 19 articles, which were based on patient-level analysis of PC, were included. These studies had a pooled sensitivity, specificity, DOR, and an area under the SROC of 0.86, 0.78, 22, and 0.89, respectively, by patient-level analysis. From the likelihood matrix, the summary negative likelihood ratio and positive likelihood ratio for MRSI diagnosis of PC were concentrated on the right lower quadrant, which neither confirmed nor excluded the diagnosis of cancer.

CONCLUSION

MRSI has a relative application value in the diagnosis of cases of suspected PC. While MRSI is still required for diagnosis along with other clinical data and comprehensive analysis.

Histogram analysis of prostate cancer on dynamic contrast-enhanced magnetic resonance imaging: A preliminary study emphasizing on zonal difference

Background

This study evaluated the performance of histogram analysis in the time course of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for differentiating cancerous tissues from benign tissues in the prostate.

Methods

We retrospectively analyzed the histograms of DCE-MRI of 30 patients. Histograms within regions of interest(ROI) in the peripheral zone (PZ) and transitional zone (TZ) were separately analyzed. The maximum difference wash-in slope (MWS) and delay phase slope (DPS) were defined for each voxel. Differences in histogram parameters, namely the mean, standard deviation (SD), the coefficient of variation (CV), kurtosis, skewness, interquartile range (IQR), percentile (P10, P25, P75, P90, and P90P10), Range, and modified full width at half-maximum (mFWHM) between cancerous and benign tissues were assessed.

Results

In the TZ, CV for ROIs of 7.5 and 10mm was the only significantly different parameter of the MWS (P = 0.034 and P = 0.004, respectively), whereas many parameters of the DPS (mean, skewness, P10, P25, P50, P75 and P90) differed significantly (P = <0.001–0.016 and area under the curve [AUC] = 0.73–0.822). In the PZ, all parameters of the MWS exhibited significant differences, except kurtosis and skewness in the ROI of 7.5mm(P = <0.001–0.017 and AUC = 0.865–0.898). SD, IQR, mFWHM, P90P10 and Range were also significant differences in the DPS (P = 0.001–0.035).

Conclusion

The histogram analysis of DCE-MRI is a potentially useful approach for differentiating prostate cancer from normal tissues. Different histogram parameters of the MWS and DPS should be applied in the TZ and PZ.

The diagnostic accuracy of high b-value diffusion- and T2-weighted imaging for the detection of prostate cancer: a meta-analysis

Purpose

This study aims to investigate the role of diffusion-weighted imaging (DWI) and T₂-weighted imaging (T₂WI) in combination for the detection of prostate cancer, specifically assessing the role of high b-values (> 1000 s/mm²), with a systematic review and meta-analysis of the existing published data.

Methods

The electronic databases MEDLINE, EMBASE, and OpenSIGLE were searched between inception and September 1, 2017. Eligible studies were those that reported the sensitivity and specificity of DWI and T₂WI for the diagnosis of prostate cancer by visual assessment using a histopathologic reference standard. The QUADAS-2 critical appraisal tool was used to assess the quality of included studies. A meta-analysis with pooling of sensitivity, specificity, likelihood, and diagnostic odds ratios was undertaken, and a summary receiver-operating characteristics (sROC) curve was constructed. Predetermined subgroup analysis was also performed.

Results

Thirty-three studies were included in the final analysis, evaluating 2949 patients. The pooled sensitivity and specificity were 0.69 (95% CI 0.68–0.69) and 0.84 (95% CI 0.83–0.85), respectively, and the sROC AUC was 0.84 (95% CI 0.81–0.87). Subgroup analysis showed significantly better sensitivity with high b-values (> 1000 s/mm²). There was high statistical heterogeneity between studies.

Conclusion

The diagnostic accuracy of combined DWI and T₂WI is good with high b-values (> 1000 s/mm²) seeming to improve overall sensitivity while maintaining specificity. However, further large-scale studies specifically looking at b-value choice are required before a categorical recommendation can be made.

Electronic supplementary material

The online version of this article (10.1007/s00261-017-1400-4) contains supplementary material, which is available to authorized users.

Multiparametric MRI reporting using Prostate Imaging Reporting and Data System version 2.0 (PI-RADSv2) retains clinical efficacy in a predominantly post-biopsy patient population

Objective

To evaluate the efficacy of multiparametric magnetic resonance imaging (mp-MRI) using Prostate Imaging Reporting and Data System version 2.0 (PI-RADSv2) definitions in detecting organ-confined prostate cancer.

Methods

All patients who underwent radical prostatectomy between January 1, 2014 and December 30, 2014 were identified. All underwent mp-MRI within 180 days before surgery. Those with prior pelvic irradiation or androgen deprivation therapy were excluded. Fully embedded, whole-mount histopathology was centrally reviewed and correlated with imaging for tumour location, Gleason score (GS) and stage.

Results

There were 39 patients included, of which 35 (90%) had mp-MRI done post-biopsy. A total of 93 cancer foci were identified on whole-mount pathology, of which mp-MRI detected 63 (68%). Of those detected by mp-MRI, 14 were PI-RADS 3 (n = 6 for GS 6, n = 8 for GS 7, no GS ≥ 8) and 49 were PI-RADS 4–5 (n = 7 for GS 6, n = 33 for GS 7, and n = 9 for GS ≥ 8). There were 30 (32%) cancer foci missed by mp-MRI (n = 15 for GS 6, n = 13 for GS 7 and n = 2 for GS ≥ 8). A lesion classified as PI-RADS 4–5 predicted a higher grade cancer on pathology as compared to PI-RADS 3 (for GS 7 lesions, odds ratio [OR] = 3.53, 95% CI: 0.93–13.45, p = 0.064). The mp-MRI size detection limit was 20 mm² and 100 mm² for 50% and 75% probability of cancer, respectively. In associating with radiological and pathologic stage, the weighted Kappa value was 0.69 (p < 0.0001). The sensitivity and positive predictive values for this study were 68% (95% CI: 57%–77%) and 78% (95% CI: 67%–86%), respectively.

Conclusion

In this predominantly post-biopsy cohort, mp-MRI using PI-RADSv2 reporting has a reasonably high diagnostic accuracy in detecting clinically significant prostate cancer.

Prostate cancer detection using quantitative T2 and T2 -weighted imaging: The effects of 5-alpha-reductase inhibitors in men on active surveillance

BACKGROUND

T₂ -weighted imaging (T₂ -WI) information has been used in a qualitative manner in the assessment of prostate cancer. Quantitative derivatives (T₂ relaxation time) can be generated from T₂ -WI. These outputs may be useful in helping to discriminate clinically significant prostate cancer from background signal.

PURPOSE/HYPOTHESIS

To investigate changes in quantitative T₂ parameters in lesions and noncancerous tissue of men on active surveillance for prostate cancer taking dutasteride 0.5 mg or placebo daily for 6 months.

STUDY TYPE

Retrospective.

POPULATION/SUBJECTS

Forty men randomized to 6 months of daily dutasteride (n = 20) or placebo (n = 20).

FIELD STRENGTH/SEQUENCE

Multiparametric 3T MRI at baseline and 6 months. This included a multiecho MR sequence for quantification of the T₂ relaxation times, in three regions of interest (index lesion, noncancerous peripheral [PZ] and transitional [TZ] zones). A synthetic signal contrast (T₂ Q contrast) between lesion and noncancerous tissue was assessed using quantitative T₂ values. Signal contrast was calculated using the T₂ -weighted sequence (T₂ W contrast).

ASSESSMENT

Two radiologists reviewed the scans in consensus according to Prostate Imaging Reporting and Data System (PI-RADS v. 2) guidelines.

STATISTICAL TESTS

Wilcoxon and Mann-Whitney U-tests, Spearman's correlation.

RESULTS

When compared to noncancerous tissue, shorter T₂ values were observed within lesions at baseline (83.5 and 80.5 msec) and 6 months (81.5 and 81.9 msec) in the placebo and dutasteride arm, respectively. No significant differences for T₂ W contrast at baseline and after 6 months were observed, both in the placebo (0.40 [0.29-0.49] vs. 0.43 [0.25-0.49]; P = 0.881) and dutasteride arm (0.35 [0.24-0.47] vs. 0.37 [0.22-0.44]; P = 0.668). There was a significant, positive correlation between the T₂ Q contrast and the T₂ W contrast values (r = 0.786; P < 0.001).

DATA CONCLUSION

The exposure to antiandrogen therapy did not significantly influence the T₂ contrast or the T₂ relaxation values in men on active surveillance for prostate cancer.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1646-1653.

A systematic review on multiparametric MR imaging in prostate cancer detection

BACKGROUND

Literature data suggest that multi-parametric Magnetic Resonance Imaging (MRI), including morphologic T2-weigthed images (T2-MRI) and functional approaches such as Dynamic Contrast Enhanced-MRI (DCE-MRI), Diffusion Weighted Imaging (DWI) and Magnetic Resonance Spectroscopic Imaging (MRSI), give an added value in the prostate cancer localization and local staging.

METHODS

We performed a systematic review of literature about the role and the potentiality of morphological and functional MRI in prostate cancer, also in a multimodal / multiparametric approach, and we reported the diagnostic accuracy results for different imaging modalities and for different MR coil settings: endorectal coil (ERC) and phased array coil (PAC). Forest plots and receiver operating characteristic curves were performed. Risk of bias and the applicability at study level were calculated.

RESULTS

Thirty three papers were identified for the systematic review. Sensitivity and specificity values were, respectively, for T2-MRI of 75% and of 60%, for DCE-MRI of 80% and of 72%, for MRSI of 89% and of 69%, for combined T2-MRI and DCE-MRI of 87% and of 46%, for combined T2-MRI and MRSI of 79% and of 57%, for combined T2-MRI, DWI and DCE-MRI of 81% and of 84%, and for combined MRSI and DCE-MRI of 83% and of 83%. For MRI studies performed with ERC we obtained a pooled sensitivity and specificity of 81% and of 66% while the pooled values for MRI studies performed with PAC were of 78% and of 64%, respectively (p>0.05 at McNemar test). No studies were excluded from the analysis based on the quality assessment.

CONCLUSIONS

ERC use yielded no additional benefit in terms of prostate cancer detection accuracy compared to multi-channel PAC use (71% versus 68%) while the use of additional functional imaging techniques (DCE-MRI, DWI and MRSI) in a multiparametric MRI protocol improves the accuracy of prostate cancer detection allowing both the early cure and the guidance of biopsy.

Prebiopsy multiparametric MRI-based risk score for predicting prostate cancer in biopsy-naive men with prostate-specific antigen between 4-10 ng/mL

BACKGROUND

Risk calculators have traditionally utilized serum prostate-specific antigen (PSA) values in addition to clinical variables to predict the likelihood of prostate cancer (PCa).

PURPOSE

To develop a prebiopsy multiparametric MRI (mpMRI)-based risk score (RS) and a statistical equation for predicting the risk of PCa in biopsy-naive men with serum PSA between 4-10 ng/mL that may help reduce unnecessary biopsies.

STUDY TYPE

Prospective cross-sectional study.

SUBJECTS

In all, 137 consecutive men with PSA between 4-10 ng/mL underwent prebiopsy mpMRI (diffusion-weighted [DW]-MRI and MR spectroscopic imaging [MRSI]) during 2009-2015 were recruited for this study.

FIELD STRENGTH/SEQUENCE

1.5T (Avanto, Siemens Health Care, Erlangen, Germany); T₁ -weighted, T₂ -weighted, DW-MRI, and MRSI sequences were used.

ASSESSMENT

All eligible patients underwent mpMRI-directed, cognitive-fusion transrectal ultrasound (TRUS)-guided biopsies.

STATISTICAL TESTS

An equation model and an RS were developed using receiver operating characteristic (ROC) curve analysis and a multivariable logistic regression approach. A 10-fold crossvalidation and simulation analyses were performed to assess diagnostic performance of various combinations of mpMRI parameters.

RESULTS

Of 137 patients, 32 were diagnosed with PCa on biopsy. Multivariable analysis, adjusted with positive pathology, showed apparent diffusion coefficient (ADC), metabolite ratio, and PSA as significant predictors of PCa (P < 0.05). A statistical equation was derived using these predictors. A simple 6-point mpMRI-based RS was derived for calculating the risk of PCa and it showed that it is highly predictive for PCa (odds ratio = 3.74, 95% confidence interval [CI]: 2.24-6.27, area under the curve [AUC] = 0.87). Both models (equation and RS) yielded high predictive performance (AUC ≥0.85) on validation analysis.

DATA CONCLUSION

A statistical equation and a simple 6-point mpMRI-based RS can be used as a point-of-care tool to potentially help limit the number of negative biopsies in men with PSA between 4 and 10 ng/mL.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1227-1236.

What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel

CONTEXT

It remains unclear whether patients with a suspicion of prostate cancer (PCa) and negative multiparametric magnetic resonance imaging (mpMRI) can safely obviate prostate biopsy.

OBJECTIVE

To systematically review the literature assessing the negative predictive value (NPV) of mpMRI in patients with a suspicion of PCa.

EVIDENCE ACQUISITION

The Embase, Medline, and Cochrane databases were searched up to February 2016. Studies reporting prebiopsy mpMRI results using transrectal or transperineal biopsy as a reference standard were included. We further selected for meta-analysis studies with at least 10-core biopsies as the reference standard, mpMRI comprising at least T2-weighted and diffusion-weighted imaging, positive mpMRI defined as a Prostate Imaging Reporting Data System/Likert score of ≥3/5 or ≥4/5, and results reported at patient level for the detection of overall PCa or clinically significant PCa (csPCa) defined as Gleason ≥7 cancer.

EVIDENCE SYNTHESIS

A total of 48 studies (9613 patients) were eligible for inclusion. At patient level, the median prevalence was 50.4% (interquartile range [IQR], 36.4-57.7%) for overall cancer and 32.9% (IQR, 28.1-37.2%) for csPCa. The median mpMRI NPV was 82.4% (IQR, 69.0-92.4%) for overall cancer and 88.1% (IQR, 85.7-92.3) for csPCa. NPV significantly decreased when cancer prevalence increased, for overall cancer (r=-0.64, p<0.0001) and csPCa (r=-0.75, p=0.032). Eight studies fulfilled the inclusion criteria for meta-analysis. Seven reported results for overall PCa. When the overall PCa prevalence increased from 30% to 60%, the combined NPV estimates decreased from 88% (95% confidence interval [95% CI], 77-99%) to 67% (95% CI, 56-79%) for a cut-off score of 3/5. Only one study selected for meta-analysis reported results for Gleason ≥7 cancers, with a positive biopsy rate of 29.3%. The corresponding NPV for a cut-off score of ≥3/5 was 87.9%.

CONCLUSIONS

The NPV of mpMRI varied greatly depending on study design, cancer prevalence, and definitions of positive mpMRI and csPCa. As cancer prevalence was highly variable among series, risk stratification of patients should be the initial step before considering prebiopsy mpMRI and defining those in whom biopsy may be omitted when the mpMRI is negative.

PATIENT SUMMARY

This systematic review examined if multiparametric magnetic resonance imaging (MRI) scan can be used to reliably predict the absence of prostate cancer in patients suspected of having prostate cancer, thereby avoiding a prostate biopsy. The results suggest that whilst it is a promising tool, it is not accurate enough to replace prostate biopsy in such patients, mainly because its accuracy is variable and influenced by the prostate cancer risk. However, its performance can be enhanced if there were more accurate ways of determining the risk of having prostate cancer. When such tools are available, it should be possible to use an MRI scan to avoid biopsy in patients at a low risk of prostate cancer.

1.5-Tesla Multiparametric-Magnetic Resonance Imaging for the detection of clinically significant prostate cancer

BACKGROUND AND AIM

Multiparametric-magnetic resonance imaging (mp-MRI) is the main imaging modality used for prostate cancer detection. The aim of this study is to evaluate the diagnostic performance of mp-MRI at 1.5-Tesla (1.5-T) for the detection of clinically significant prostate cancer.

METHODS

In this ethical board approved prospective study, 39 patients with suspected prostate cancer were included. Patients with a history of positive prostate biopsy and patients treated for prostate cancer were excluded. All patients were examined at 1.5-T MRI, before standard transrectal ultrasonography-guided biopsy.

RESULTS

The overall sensitivity, specificity, positive predictive value and negative predictive value for mp-MRI were 100%, 73.68%, 80% and 100%, respectively.

CONCLUSION

Our results showed that 1.5 T mp-MRI has a high sensitivity for detection of clinically significant prostate cancer and high negative predictive value in order to rule out significant disease.

Multiparametric MRI for prostate cancer detection: Preliminary results on quantitative analysis of dynamic contrast enhanced imaging, diffusion-weighted imaging and spectroscopy imaging

INTRODUCTION

Early promising data suggest that combined use of both morphological and functional MRI (multi-parametric MR, mpMRI) including MRSI, DWI and DCE may be of additional value for prostate cancer localization and its local staging. The objective of this paper is to evaluate the diagnostic performance of mpMRI in the detection of prostate cancer.

METHODS

Thirty-one consecutive male patients were screened to be enrolled in a single center prospective observational study. All eligible patients underwent multi-parametric MRI and TRUS (Trans Rectal Ultra Sound) guided prostate biopsies. A register, approved by the Institutional Ethics Committee, included patients enrolled in this study. All patients who decided to undergo the MRI examination signed an explicit informed consensus. MRI data were aligned on a common spatial grid and several functional parameters (perfusion, diffusion and metabolic parameters) were computed. Statistical analysis was conducted in order to compare mpMRI with biopsy-based analysis.

RESULTS

Statistically significant differences between median values in high Gleason score (≥5) and low Gleason score (<5) to Wilcox on rank sum test were obtained for MRSI parameters and for plasma fraction (Tofts model) of DCE-MRI. The area under curve obtained with ROC analysis showed that the best-performing single-parameter was vp (plasma fraction of Tofts model), while the best parameters combination to discriminate the area with high Gleason score were (Cho+Cr)/Cit and Cho+Cr. Linear Discrimination Analysis showed that the best results were obtained considering the linear combination of all MRSI parameters and the linear combination of all features (perfusion, diffusion and metabolic parameters).

CONCLUSIONS

In conclusion, our findings showed that by combining morphological MRI, DWI, DCE-MRI and MRSI, an increase in sensitivity and specificity correlated to biopsy Gleason grade could be obtained. Furthermore, morphological and functional MRI could have a diagnostic role in patients with prostate cancer, identifying those patients who will have a negative work-up and those patients at high risk for a high Gleason score cancer of the prostate.

Current role of multiparametric magnetic resonance imaging for prostate cancer

Multiparametric magnetic resonance imaging (mp-MRI) has shown promising results in diagnosis, localization, risk stratification and staging of clinically significant prostate cancer, and targeting or guiding prostate biopsy. mp-MRI consists of T2-weighted imaging (T2WI) combined with several functional sequences including diffusion-weighted imaging (DWI), perfusion or dynamic contrast-enhanced imaging (DCEI) and spectroscopic imaging. Recently, mp-MRI has been used to assess prostate cancer aggressiveness and to identify anteriorly located tumors before and during active surveillance. Moreover, recent studies have reported that mp-MRI is a reliable imaging modality for detecting local recurrence after radical prostatectomy or external beam radiation therapy. Because assessment on mp-MRI can be subjective, use of the newly developed standardized reporting Prostate Imaging and Reporting Archiving Data System (PI-RADS) scoring system and education of specialist radiologists are essential for accurate interpretation. This review focuses on the current place of mp-MRI in prostate cancer and its evolving role in the management of prostate cancer.

Multiparametric-MRI in diagnosis of prostate cancer

Multiparametric-magnetic resonance imaging (mp-MRI) has shown promising results in diagnosis, localization, risk stratification and staging of clinically significant prostate cancer. It has also opened up opportunities for focal treatment of prostate cancer. Combinations of T2-weighted imaging, diffusion imaging, perfusion (dynamic contrast-enhanced imaging) and spectroscopic imaging have been used in mp-MRI assessment of prostate cancer, but T2 morphologic assessment and functional assessment by diffusion imaging remains the mainstay for prostate cancer diagnosis on mp-MRI. Because assessment on mp-MRI can be subjective, use of the newly developed standardized reporting Prostate Imaging and Reporting Archiving Data System scoring system and education of specialist radiologists are essential for accurate interpretation. This review focuses on the present status of mp-MRI in prostate cancer and its evolving role in the management of prostate cancer.