MRI-Derived Restriction Spectrum Imaging Cellularity Index is Associated with High Grade Prostate Cancer on Radical Prostatectomy Specimens

Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model

BACKGROUND

Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF).

PURPOSE

To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model.

METHODS

Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF.

RESULTS

According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471 × 10-3, 1.653 × 10-3, 9.487 × 10-3, and > 30 × 10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002).

CONCLUSION

The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment

Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.

Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer

Purpose

Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.

Experimental design

Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.

Results

Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.

Conclusion

The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10^-3, 2.8 × 10^-3, and >3.0 ×10^-2 mm²/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

Feasibility of diffusion weighting with a local inside-out nonlinear gradient coil for prostate MRI

PURPOSE

Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI quality image is directly related to low gradient amplitudes, since weak gradients must be compensated with long echo times.

METHODS

We propose a new type of MRI accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients.

RESULTS

Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC.

CONCLUSIONS

Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.

New prostate MRI techniques and sequences

Prostate MRI has seen increasing interest in recent years and has led to the development of new MRI techniques and sequences to improve prostate cancer (PCa) diagnosis which are reviewed in this article. Numerous studies have focused on improving image quality (segmented DWI) and faster acquisition (compressed sensing, k-t-SENSE, PROPELLER). An increasing number of studies have developed new quantitative and computer-aided diagnosis methods including artificial intelligence (PROSTATEx challenge) that mitigate the subjective nature of mpMRI interpretation. MR fingerprinting allows rapid, simultaneous generation of quantitative maps of multiple physical properties (T1, T2), where PCa are characterized by lower T1 and T2 values. New techniques like luminal water imaging (LWI), restriction spectrum imaging (RSI), VERDICT and hybrid multi-dimensional MRI (HM-MRI) have been developed for microstructure imaging, which provide information similar to histology. The distinct MR properties of tissue components and their change with the presence of cancer is used to diagnose prostate cancer. LWI is a T2-based imaging technique where long T2-component corresponding to luminal water is reduced in PCa. RSI and VERDICT are diffusion-based techniques where PCa is characterized by increased signal from intra-cellular restricted water and increased intracellular volume fraction, respectively, due to increased cellularity. VERDICT also reveal loss of extracellular-extravascular space in PCa due to loss of glandular structure. HM-MRI measures volumes of prostate tissue components, where PCa has reduced lumen and stromal and increased epithelium volume similar to results shown in histology. Similarly, molecular imaging using hyperpolarized ¹³C imaging has been utilized.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

BACKGROUND

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.

PURPOSE

To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.

FIELD STRENGTH/SEQUENCE

3T multishell diffusion-weighted sequence.

ASSESSMENT

Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.

STATISTICAL TESTS

Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.

RESULTS

The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm² /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order.

DATA CONCLUSION

The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Inflammation appears as high Prostate Imaging-Reporting and Data System scores on prostate magnetic resonance imaging (MRI) leading to false positive MRI fusion biopsy

Purpose

To investigate if inflammation as a potential cause of false-positive lesions from recent UroNav magnetic resonance imaging (MRI) fusion prostate biopsy patients.

Materials and Methods

We retrospectively identified 43 men with 61 MRI lesions noted on prostate MRI before MRI ultrasound-guided fusion prostate biopsy. Men underwent MRI with 3T Siemens TIM Trio MRI system (Siemens AG, Germany), and lesions were identified and marked in DynaCAD system (Invivo Corporation, USA) with subsequent biopsy with MRI fusion with UroNav. We obtained targeted and standard 12-core needle biopsies. We retrospectively reviewed pathology reports for inflammation.

Results

We noted a total of 43 (70.5%) false-positive lesions with 28 having no cancer on any cores, and 15 lesions with cancer noted on systematic biopsy but not in the target region. Of the men with cancer, 6 of the false positive lesions had inflammation in the location of the targeted region of interest (40.0%, 6/15). However, when we examine the 21/28 lesions with an identified lesion on MRI with no cancer in all cores, 54.5% had inflammation on prostate biopsy pathology (12/22, p=0.024). We noted the highest proportion of inflammation.

Conclusions

Inflammation can confound the interpretation of MRI by mimicking prostate cancer. We suggested focused efforts to differentiate inflammation and cancer on prostate MRI.

Utility of Restriction Spectrum Imaging Among Men Undergoing First-Time Biopsy for Suspected Prostate Cancer

OBJECTIVE. The purpose of this article is to evaluate restriction spectrum imaging (RSI) in men undergoing MRI-ultrasound fusion biopsy for suspected prostate cancer (PCa) and to compare the performance of RSI with that of conventional DWI. MATERIALS AND METHODS. One hundred ninety-eight biopsy-naïve men enrolled in a concurrent prospective clinical trial evaluating MRI-targeted prostate biopsy underwent multiparametric MRI with RSI. Clinical and imaging features were compared between men with and without clinically significant (CS) PCa (MRI-ultrasound fusion biopsy Gleason score ≥ 3 + 4). RSI z score and apparent diffusion coefficient (ADC) were correlated, and their diagnostic performances were compared. RESULTS. CS PCa was detected in 109 of 198 men (55%). Using predefined thresholds of ADC less than or equal to 1000 μm²/s and RSI z score greater than or equal to 3, sensitivity and specificity for CS PCa were 86% and 38%, respectively, for ADC and 61% and 70%, respectively, for RSI. In the transition zone (n = 69), the sensitivity and specificity were 94% and 17%, respectively, for ADC and 59% and 69%, respectively, for RSI. Among lesions with CS PCa, RSI z score and ADC were significantly inversely correlated in the peripheral zone (ρ = -0.4852; p < 0.01) but not the transition zone (ρ = -0.2412; p = 0.17). Overall diagnostic accuracies of RSI and DWI were 0.70 and 0.68, respectively (p = 0.74). CONCLUSION. RSI and DWI achieved equivalent diagnostic performance for PCa detection in a large population of men undergoing first-time prostate biopsy for suspected PCa, but RSI had superior specificity for transition zone lesions.

Diagnostic utility of restriction spectrum imaging (RSI) in glioblastoma patients after concurrent radiation-temozolomide treatment: A pilot study

Discriminating between tumor recurrence and treatment effects in glioblastoma patients undergoing radiation-temozolomide (RT/TMZ) therapy remains a major clinical challenge. Here, we report a pilot study to determine the utility of restriction spectrum imaging (RSI), an advanced diffusion-weighted MRI (DWI) technique that affords meso-scale resolution of cell density, in this assessment. A retrospective review of 31 patients with glioblastoma treated between 2011 and 2017 who underwent surgical resection or biopsy over radiographic concern for tumor recurrence following RT/TMZ was performed. All patients underwent RSI prior to surgical resection. Diagnostic utility of RSI for tumor recurrence was determined in comparison to histopathology. Analysis of surgical specimens revealed treatment effects in 6/31 patients (19%) and tumor recurrence in 25/31 patients (81%). There was general concordance between the measured RSI signal and histopathologic diagnosis. RSI was negative in 5/6 patients (83%) in patients with histological evidence of treatment effects. RSI was positive in 21/25 patients (84%) in patients with tumor recurrence. The sensitivity, specificity, positive and negative predictive values of RSI for glioblastoma recurrence were 84%, 86%, 95%, and 60%, respectively. Histopathologic review showed agreement between the RSI signal and cellularity of the tumor specimen. These data support the use of RSI in the evaluation of treatment effects versus tumor recurrence in glioblastoma patients after RT-TMZ therapy.

Voxel Level Radiologic-Pathologic Validation of Restriction Spectrum Imaging Cellularity Index with Gleason Grade in Prostate Cancer

PURPOSE

Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially circumvent current limitations in tumor conspicuity, in vivo characterization, and location demonstrated by multiparametric magnetic resonance imaging (MP-MRI) techniques in prostate cancer detection. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive prostate cancer as measured by Gleason grade (GG). We evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated prostate cancer regions.

EXPERIMENTAL DESIGN

Ten cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified by an uropathologist. Stained prostate sections were scanned at high resolution (75 μm/pixel). A grid of tiles corresponding to voxel dimensions was graded using the GG system. RSI-MRI cellularity index was calculated from presurgical prostate MR scans and presented as normalized z-score maps. In total, 2,795 tiles were analyzed and compared with RSI-MRI cellularity.

RESULTS

RSI-MRI cellularity index was found to distinguish between prostate cancer and benign tumor (t = 25.48, P < 0.00001). Significant differences were also found between benign tissue and prostate cancer classified as low-grade (GG = 3; t = 11.56, P < 0.001) or high-grade (GG ≥ 4; t = 24.03, P < 0.001). Furthermore, RSI-MRI differentiated between low and high-grade prostate cancer (t = 3.23; P = 0.003).

CONCLUSIONS

Building on our previous findings of correlation between GG and the RSI-MRI among whole tumors, our current study reveals a similar correlation at voxel resolution within tumors. Because it can detect variations in tumor grade with voxel-level precision, RSI-MRI may become an option for planning targeted procedures where identifying the area with the most aggressive disease is important. Clin Cancer Res; 22(11); 2668-74. ©2016 AACR.

Role of MRI in the Risk Assessment of Primary Prostate Cancer

A successful paradigm shift toward personalized management strategies for patients with prostate cancer (PCa) is heavily dependent on the availability of noninvasive diagnostic tools capable of accurately establishing the true extent of disease at the time of diagnosis and estimating the risk of subsequent disease progression and related mortality. Although there is still considerable scope for improvement in its diagnostic, predictive, and prognostic capabilities, multiparametric prostate magnetic resonance imaging (MRI) is currently regarded as the imaging modality of choice for local staging of PCa. A negative MRI, that is, the absence of any MRI-visible intraprostatic lesion, has a high negative predictive value for the presence of clinically significant PCa and can substantiate the consideration of active surveillance as a preferred initial management approach. MRI-derived quantitative and semi-quantitative parameters can be utilized to noninvasively characterize MRI-visible prostate lesions and identify those patients who are most likely to benefit from radical treatment, and differentiate them from patients with benign or indolent prostate pathology that may also be visible on MRI. This literature review summarizes current strategies how MRI can be used to determine a tailored management strategy for an individual patient.

Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI

Restriction spectrum imaging (RSI) is a novel diffusion-weighted MRI technique that uses the mathematically distinct behavior of water diffusion in separable microscopic tissue compartments to highlight key aspects of the tissue microarchitecture with high conspicuity. RSI can be acquired in less than 5 min on modern scanners using a surface coil. Multiple field gradients and high b-values in combination with postprocessing techniques allow the simultaneous resolution of length-scale and geometric information, as well as compartmental and nuclear volume fraction filtering. RSI also uses a distortion correction technique and can thus be fused to high resolution T2-weighted images for detailed localization, which improves delineation of disease extension into critical anatomic structures. In this review, we discuss the acquisition, postprocessing, and interpretation of RSI for prostate MRI. We also summarize existing data demonstrating the applicability of RSI for prostate cancer detection, in vivo characterization, localization, and targeting.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2017;45:323-336.

In vivo prostate cancer detection and grading using restriction spectrum imaging-MRI

BACKGROUND

Magnetic resonance imaging (MRI) is emerging as a robust, noninvasive method for detecting and characterizing prostate cancer (PCa), but limitations remain in its ability to distinguish cancerous from non-cancerous tissue. We evaluated the performance of a novel MRI technique, restriction spectrum imaging (RSI-MRI), to quantitatively detect and grade PCa compared with current standard-of-care MRI.

METHODS

In a retrospective evaluation of 33 patients with biopsy-proven PCa who underwent RSI-MRI and standard MRI before radical prostatectomy, receiver-operating characteristic (ROC) curves were performed for RSI-MRI and each quantitative MRI term, with area under the ROC curve (AUC) used to compare each term's ability to differentiate between PCa and normal prostate. Spearman rank-order correlations were performed to assess each term's ability to predict PCa grade in the radical prostatectomy specimens.

RESULTS

RSI-MRI demonstrated superior differentiation of PCa from normal tissue, with AUC of 0.94 and 0.85 for RSI-MRI and conventional diffusion MRI, respectively (P=0.04). RSI-MRI also demonstrated superior performance in predicting PCa aggressiveness, with Spearman rank-order correlation coefficients of 0.53 (P=0.002) and -0.42 (P=0.01) for RSI-MRI and conventional diffusion MRI, respectively, with tumor grade.

CONCLUSIONS

RSI-MRI significantly improves upon current noninvasive PCa imaging and may potentially enhance its diagnosis and characterization.

Restriction spectrum imaging improves MRI-based prostate cancer detection

PURPOSE

To compare the diagnostic performance of restriction spectrum imaging (RSI), with that of conventional multi-parametric (MP) magnetic resonance imaging (MRI) for prostate cancer (PCa) detection in a blinded reader-based format.

METHODS

Three readers independently evaluated 100 patients (67 with proven PCa) who underwent MP-MRI and RSI within 6 months of systematic biopsy (N = 67; 23 with targeting performed) or prostatectomy (N = 33). Imaging was performed at 3 Tesla using a phased-array coil. Readers used a five-point scale estimating the likelihood of PCa present in each prostate sextant. Evaluation was performed in two separate sessions, first using conventional MP-MRI alone then immediately with MP-MRI and RSI in the same session. Four weeks later, another scoring session used RSI and T2-weighted imaging (T2WI) without conventional diffusion-weighted or dynamic contrast-enhanced imaging. Reader interpretations were then compared to prostatectomy data or biopsy results. Receiver operating characteristic curves were performed, with area under the curve (AUC) used to compare across groups.

RESULTS

MP-MRI with RSI achieved higher AUCs compared to MP-MRI alone for identifying high-grade (Gleason score greater than or equal to 4 + 3=7) PCa (0.78 vs. 0.70 at the sextant level; P < 0.001 and 0.85 vs. 0.79 at the hemigland level; P = 0.04). RSI and T2WI alone achieved AUCs similar to MP-MRI for high-grade PCa (0.71 vs. 0.70 at the sextant level). With hemigland analysis, high-grade disease results were similar when comparing RSI + T2WI with MP-MRI, although with greater AUCs compared to the sextant analysis (0.80 vs. 0.79).

CONCLUSION

Including RSI with MP-MRI improves PCa detection compared to MP-MRI alone, and RSI with T2WI achieves similar PCa detection as MP-MRI.

Prostate cancer risk stratification with magnetic resonance imaging

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