Description of magnetic resonance imaging-derived enhancement variables in pathologically confirmed prostate cancer and normal peripheral zone regions

Optimal length and temporal resolution of dynamic contrast-enhanced MR imaging for the differentiation between prostate cancer and normal peripheral zone tissue

The value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the detection of prostate cancer is controversial. There are currently insufficient peer reviewed published data or expert consensus to support routine adoption of DCE-MRI for clinical use. Thus, the objective of this study was to explore the optimal temporal resolution and measurement length for DCE-MRI to differentiate cancerous from normal prostate tissue of the peripheral zone of the prostate by non-parametric MRI analysis and to compare with a quantitative MRI analysis. Predictors of interest were onset time, relative signal intensity (RSI), wash-in slope, peak enhancement, wash-out and wash-out slope determined from non-parametric characterisation of DCE-MRI intensity-time profiles. The discriminatory power was estimated from C-statistics based on cross validation. We analyzed 54 patients with 97 prostate tissue specimens (47 prostate cancer, 50 normal prostate tissue) of the peripheral zone, mean age 63.8 years, mean prostate-specific antigen 18.9 ng/mL and mean of 10.5 days between MRI and total prostatectomy. When comparing prostate cancer tissue with normal prostate tissue, median RSI was 422% vs 330%, and wash-in slope 0.870 vs 0.539. The peak enhancement of 67 vs 42 was higher with prostate cancer tissue, while wash-out (-30% vs -23%) and wash-out slope (-0.037 vs -0.029) were lower, and the onset time (32 seconds) was comparable. The optimal C-statistics was 0.743 for temporal resolution of 8.0 seconds and measurement length of 2.5 minutes compared with 0.656 derived from a quantitative MRI analysis. This study provides evidence that the use of a non-parametric approach instead of a more established parametric approach resulted in greater precision to differentiate cancerous from normal prostate tissue of the peripheral zone of the prostate.

Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution

METHODS

A retrospective study (from January 2016 to July 2019) including 75 subjects (mean, 65 years; 46-80 years) with 2.5-second temporal resolution DCE-MRI and PIRADS 4 or 5 lesions was performed. Fifty-four subjects had biopsy-proven prostate cancer (Gleason 6, 15; Gleason 7, 20; Gleason 8, 13; Gleason 9, 6), whereas 21 subjects had negative MRI/ultrasound fusion-guided biopsies. Voxel-wise analysis of contrast signal enhancement was performed for all time points using custom-developed software, including automatic arterial input function detection. Seven descriptive parameter maps were calculated: normalized maximum signal intensity, time to start, time to maximum, time-to-maximum slope, and maximum slope with normalization on maximum signal and the arterial input function (SMN1, SMN2). The parameters were compared with ADC using multiparametric machine-learning models to determine classification accuracy. A Wilcoxon test was used for the hypothesis test and the Spearman coefficient for correlation.

RESULTS

There were significant differences (P < 0.05) for all 7 DCE-derived parameters between the normal peripheral zone versus PIRADS 4 or 5 lesions and the biopsy-positive versus biopsy-negative lesions. Multiparametric analysis showed better performance when combining ADC + DCE as input (accuracy/sensitivity/specificity, 97%/93%/100%) relative to ADC alone (accuracy/sensitivity/specificity, 94%/95%/95%) and to DCE alone (accuracy/sensitivity/specificity, 78%/79%/77%) in differentiating the normal peripheral zone from PIRADS lesions, biopsy-positive versus biopsy-negative lesions (accuracy/sensitivity/specificity, 68%/33%/81%), and Gleason 6 versus ≥7 prostate cancer (accuracy/sensitivity/specificity, 69%/60%/72%).

CONCLUSIONS

Descriptive perfusion characteristics derived from high-resolution DCE-MRI using model-free computations show significant differences between normal and cancerous tissue but do not reach the accuracy achieved with solely ADC-based classification. Combining ADC with DCE-based input features improved classification accuracy for PIRADS lesions, discrimination of biopsy-positive versus biopsy-negative lesions, and differentiation between Gleason 6 versus Gleason ≥7 lesions.

Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: A patient-level classification framework

Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multiparametric magnetic resonance imaging (mpMRI) is actively being investigated as a means to provide clinical decision support to radiologists. Typically, these systems are trained using lesion annotations. However, lesion annotations are expensive to obtain and inadequate for characterizing certain tumor types e.g. diffuse tumors and MRI invisible tumors. In this work, we introduce a novel patient-level classification framework, denoted PCF, that is trained using patient-level labels only. In PCF, features are extracted from three-dimensional mpMRI and derived parameter maps using convolutional neural networks and subsequently, combined with clinical features by a multi-classifier support vector machine scheme. The output of PCF is a probability value that indicates whether a patient is harboring clinically significant PCa (Gleason score ≥3+4) or not. PCF achieved mean area under the receiver operating characteristic curves of 0.79 and 0.86 on the PICTURE and PROSTATEx datasets respectively, using five-fold cross-validation. Clinical evaluation over a temporally separated PICTURE dataset cohort demonstrated comparable sensitivity and specificity to an experienced radiologist. We envision PCF finding most utility as a second reader during routine diagnosis or as a triage tool to identify low-risk patients who do not require a clinical read.

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists

OBJECTIVE

The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists.

METHODS

A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists.

RESULTS

The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82).

CONCLUSIONS

Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists.

KEY POINTS

• Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer

OBJECTIVES

Compare the performance of zone-specific multi-parametric-MRI (mp-MRI) diagnostic models in prostate cancer detection with experienced radiologists.

METHODS

A single-centre, IRB approved, prospective STARD compliant 3 T MRI test dataset of 203 patients was generated to test validity and generalisability of previously reported 1.5 T mp-MRI diagnostic models. All patients included within the test dataset underwent 3 T mp-MRI, comprising T2, diffusion-weighted and dynamic contrast-enhanced imaging followed by transperineal template ± targeted index lesion biopsy. Separate diagnostic models (transition zone (TZ) and peripheral zone (PZ)) were applied to respective zones. Sensitivity/specificity and the area under the receiver operating characteristic curve (ROC-AUC) were calculated for the two zone-specific models. Two radiologists (A and B) independently Likert scored test 3 T mp-MRI dataset, allowing ROC analysis for each radiologist for each prostate zone.

RESULTS

Diagnostic models applied to the test dataset demonstrated a ROC-AUC = 0.74 (95% CI 0.67-0.81) in the PZ and 0.68 (95% CI 0.61-0.75) in the TZ. Radiologist A/B had a ROC-AUC = 0.78/0.74 in the PZ and 0.69/0.69 in the TZ. Radiologists A and B each scored 51 patients in the PZ and 41 and 45 patients respectively in the TZ as Likert 3. The PZ model demonstrated a ROC-AUC = 0.65/0.67 for the patients Likert scored as indeterminate by radiologist A/B respectively, whereas the TZ model demonstrated a ROC-AUC = 0.74/0.69.

CONCLUSION

Zone-specific mp-MRI diagnostic models demonstrate generalisability between 1.5 and 3 T mp-MRI protocols and show similar classification performance to experienced radiologists for prostate cancer detection. Results also indicate the ability of diagnostic models to classify cases with an indeterminate radiologist score.

KEY POINTS

• MRI diagnostic models had similar performance to experienced radiologists for classification of prostate cancer. • MRI diagnostic models may help radiologists classify tumour in patients with indeterminate Likert 3 scores.

The Role of Lactate Metabolism in Prostate Cancer Progression and Metastases Revealed by Dual-Agent Hyperpolarized 13C MRSI

This study applied a dual-agent, ¹³C-pyruvate and ¹³C-urea, hyperpolarized ¹³C magnetic resonance spectroscopic imaging (MRSI) and multi-parametric (mp) ¹H magnetic resonance imaging (MRI) approach in the transgenic adenocarcinoma of mouse prostate (TRAMP) model to investigate changes in tumor perfusion and lactate metabolism during prostate cancer development, progression and metastases, and after lactate dehydrogenase-A (LDHA) knock-out. An increased Warburg effect, as measured by an elevated hyperpolarized (HP) Lactate/Pyruvate (Lac/Pyr) ratio, and associated Ldha expression and LDH activity were significantly higher in high- versus low-grade TRAMP tumors and normal prostates. The hypoxic tumor microenvironment in high-grade tumors, as measured by significantly decreased HP ¹³C-urea perfusion and increased PIM staining, played a key role in increasing lactate production through increased Hif1α and then Ldha expression. Increased lactate induced Mct4 expression and an acidic tumor microenvironment that provided a potential mechanism for the observed high rate of lymph node (86%) and liver (33%) metastases. The Ldha knockdown in the triple-transgenic mouse model of prostate cancer resulted in a significant reduction in HP Lac/Pyr, which preceded a reduction in tumor volume or apparent water diffusion coefficient (ADC). The Ldha gene knockdown significantly reduced primary tumor growth and reduced lymph node and visceral metastases. These data suggested a metabolic transformation from low- to high-grade prostate cancer including an increased Warburg effect, decreased perfusion, and increased metastatic potential. Moreover, these data suggested that LDH activity and lactate are required for tumor progression. The lactate metabolism changes during prostate cancer provided the motivation for applying hyperpolarized ¹³C MRSI to detect aggressive disease at diagnosis and predict early therapeutic response.

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.

Prostate imaging features that indicate benign or malignant pathology on biopsy

Accurate diagnosis of clinically significant prostate cancer is essential in identifying patients who should be offered treatment with curative intent. Modifications to the Gleason grading system in recent years show that accurate grading and reporting at needle biopsy can improve identification of clinically significant prostate cancers. Extracapsular extension of prostate cancer has been demonstrated to be an adverse prognostic factor with greater risk of metastatic spread than organ-confined disease. Tumor volume may be an independent prognostic factor and should be considered in conjunction with other factors. Multi-parametric magnetic resonance imaging (MP-MRI) has become an increasingly important tool in the diagnosis and characterization of prostate cancer. MP-MRI allows T2-weighted (T2W) anatomical imaging to be combined with functional and physiological assessment. Diffusion-weighted imaging (DWI) has shown greater sensitivity, specificity and negative predictive value compared to prostate specific antigen (PSA) testing and T2W imaging alone and has a more positive correlation with Gleason score and tumour volume. Dynamic gadolinium contrast-enhanced (DCE) imaging can exhibit difficulties in distinguishing prostatitis from malignancy in the peripheral zone, and between benign prostatic hyperplasia (BPH) and malignancies in the transition zone (TZ). Computer aided diagnosis utilizes software to aid radiologists in detecting and diagnosing abnormalities from diagnostic imaging. New techniques of quantitative MRI, such as VERDICT MRI use tissue-specific factors to delineate different cellular and microstructural phenotypes, characterizing tissue properties with greater detail. Proton MR spectroscopic imaging (MRSI) is a more technically challenging imaging modality than DCE and DWI MRI. Over the last decade, choline and prostate-specific membrane antigen (PSMA) positron emission tomography (PET) have developed as better tools for staging than conventional imaging. While hyperpolarized MRI shows promise in improving the imaging and differentiation of benign and malignant lesions there is further work required. Accurate reading and interpretation of diagnostic investigations is key to accurate identification of abnormal areas requiring biopsy, sparing those in whom benign or indolent disease can be managed by non-invasive means. Embracing and advancing existing technologies is essential in furthering this process.

Magnetic resonance imaging in the early detection of prostate cancer and review of the literature on magnetic resonance imaging-stratified clinical pathways

INTRODUCTION

With level 1 evidence now available on the diagnostic accuracy of multiparametric magnetic resonance imaging (MRI) we must now utilise this data in developing an MRI-stratified diagnostic pathway for the early detection of prostate cancer. Areas covered: A literature review was conducted and identified seven randomised control trials (RCT's) assessing the diagnostic accuracy of such a pathway against the previously accepted systematic/random trans-rectal ultrasound guided (TRUS) biopsy pathway. The studies were heterogeneous in their design. Five studies assessed the addition of MRI-targeted biopsies to a standard care systematic TRUS biopsy pathway. Three of these studies showed either an increase in their diagnostic accuracy or the potential to remove systematic biopsies. Two studies looked specifically at a targeted biopsy only pathway and although the results were again mixed, there was no decrease in the diagnostic rate and overall significantly fewer biopsy cores were taken in the MRI group. Expert commentary: Results from these RCT's together with multiple retrospective and prospective studies point towards either an improved diagnostic rate for clinically significant cancer and/or a reduction in the need for systematic biopsies with a MRI-stratified pathway. The challenge for the urological community will be to implement pre-biopsy MRI into a routine clinical pathway with likely independent monitoring of standards.

Prostate magnetic resonance imaging for brachytherapists: Anatomy and technique

PURPOSE

To present an overview of mp MRI techniques necessary for high-resolution imaging of prostate.

METHODS

We summarize examples from our clinical experience and concepts from the current literature that illustrate normal prostate anatomy on multiparametric MRI (mp MRI).

RESULTS

Our experience regarding optimal mp MRI image acquisition is provided, as well as a summary of prostate and periprostatic anatomy and anatomical variants that pose challenges for BT.

CONCLUSIONS

mp MRI provides unparalleled assessment of the prostate and periprostatic anatomy, making it the most appropriate imaging modality to facilitate prostate BT treatment planning, implantation, and followup. This work provides an introduction to prostate mp MR imaging, anatomy, and anatomical variants essential for successful integration mp MRI into prostate brachytherapy practice.

Evolution of multi-parametric MRI quantitative parameters following transrectal ultrasound-guided biopsy of the prostate

BACKGROUND

To determine the evolution of prostatic multi-parametric magnetic resonance imaging (mp-MRI) signal following transrectal ultrasound (TRUS)-guided biopsy.

METHODS

Local ethical permission and informed written consent was obtained from all the participants (n=14, aged 43-69, mean 64 years). Patients with a clinical suspicion of prostate cancer (PSA range 2.2-11.7, mean 6.2) and a negative (PIRAD 1-2/5) pre-biopsy mp-MRI (pre-contrast T1, T2, diffusion-weighted and dynamic-contrast-enhanced MRI) who underwent 10-core TRUS-guided biopsy were recruited for additional mp-MRI examinations performed at 1, 2 and 6 months post biopsy. We quantified mp-MRI peripheral zone (PZ) and transition zone (TZ) normalized T2 signal intensity (nT2-SI); T1 relaxation time (T10); diffusion-weighted MRI, apparent diffusion coefficient (ADC); dynamic contrast-enhanced MRI, maximum enhancement (ME); slope of enhancement (SoE) and area-under-the-contrast-enhancement-curve at 120 s (AUC120). Significant changes in mp-MRI parameters were identified by analysis of variance with Dunnett's post testing.

RESULTS

Diffuse signal changes were observed post-biopsy throughout the PZ. No significant signal change occurred following biopsy within the TZ. Left and right PZ mean nT2-SI (left PZ: 5.73, 5.16, 4.90 and 5.12; right PZ: 5.80, 5.10, 4.84 and 5.05 at pre-biopsy, 1, 2 and 6 months post biopsy, respectively) and mean T10 (left PZ: 1.02, 0.67, 0.78, 0.85; right PZ: 1.29, 0.64, 0.78, 0.87 at pre-biopsy, 1, 2 and 6 months post biopsy, respectively) were reduced significantly (P<0.05) from pre-biopsy values for up to 6 months post biopsy. Significant changes (P<0.05) of PZ-ME and AUC120 were observed at 1 month but resolved by 2 months post biopsy. PZ ADC did not change significantly following biopsy (P=0.23-1.0). There was no significant change of any TZ mp-MRI parameter at any time point following biopsy (P=0.1-1.0).

CONCLUSIONS

Significant PZ (but not TZ) T2 signal changes persist up to 6 months post biopsy, whereas PZ and TZ ADC is not significantly altered as early as 1 month post biopsy. Caution must be exercised when interpreting T1- and T2-weighted imaging early post biopsy, whereas ADC images are more likely to maintain clinical efficacy.

Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI

OBJECTIVES

To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer.

METHODS

Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models.

RESULTS

The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer.

CONCLUSION

LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application.

KEY POINTS

• The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.

The role of pathology correlation approach in prostate cancer index lesion detection and quantitative analysis with multiparametric MRI

Rationale and Objectives

Development of imaging biomarkers often relies on their correlation with histopathology. Our aim was to compare two approaches for correlating pathology to multiparametric magnetic resonance (MR) imaging (mpMRI) for localization and quantitative assessment of prostate cancer (PCa) index tumor using whole mount (WM) pathology (WMP) as the reference.

Materials and Methods

Patients (N = 30) underwent mpMRI that included diffusion-weighted imaging and dynamic contrast-enhanced (DCE) MRI at 3 T before radical prostatectomy (RP). RP specimens were processed using WM technique (WMP) and findings summarized in a standard surgical pathology report (SPR). Histology index tumor volumes (HTVs) were compared to MR tumor volumes (MRTVs) using two approaches for index lesion identification on mpMRI using annotated WMP slides as the reference (WMP) and using routine SPR as the reference. Consistency of index tumor localization, tumor volume, and mean values of the derived quantitative parameters (mean apparent diffusion coefficient [ADC], K^trans, and v_e) were compared.

Results

Index lesions from 16 of 30 patients met the selection criteria. There was WMP/SRP agreement in index tumor in 13 of 16 patients. ADC-based MRTVs were larger (P < .05) than DCE-based MRTVs. ADC MRTVs were smaller than HTV (P < .005). There was a strong correlation between HTV and MRTV (Pearson r > 0.8; P < .05). No significant differences were observed in the mean values of K^trans and ADC between the WMP and SPR.

Conclusions

WMP correlation is superior to SPR for accurate localization of all index lesions. The use of WMP is however not required to distinguish significant differences of mean values of quantitative MRI parameters within tumor volume.

1.5-T multiparametric MRI using PI-RADS: a region by region analysis to localize the index-tumor of prostate cancer in patients undergoing prostatectomy

BACKGROUND

The use of multiparametric magnetic resonance imaging (mpMRI) to detect and localize prostate cancer has increased in recent years. In 2010, the European Society of Urogenital Radiology (ESUR) published guidelines for mpMRI and introduced the Prostate Imaging Reporting and Data System (PI-RADS) for scoring the different parameters.

PURPOSE

To evaluate the reliability and diagnostic performance of endorectal 1.5-T mpMRI using the PI-RADS to localize the index tumor of prostate cancer in patients undergoing prostatectomy.

MATERIAL AND METHODS

This institutional review board IRB-approved, retrospective study included 63 patients (mean age, 60.7 years, median PSA, 8.0). Three observers read mpMRI parameters (T2W, DWI, and DCE) using the PI-RADS, which were compared with the results from whole-mount histopathology that analyzed 27 regions of interest. Inter-observer agreement was calculated as well as sensitivity, specificity, positive predictive value (PPV), and negative predicted value (NPV) by dichotomizing the PI-RADS criteria scores ≥3. A receiver-operating curve (ROC) analysis was performed for the different MR parameters and overall score.

RESULTS

Inter-observer agreement on the overall score was 0.41. The overall score in the peripheral zone achieved sensitivities of 0.41, 0.60, and 0.55 with an NPV of 0.80, 0.84, and 0.83, and in the transitional zone, sensitivities of 0.26, 0.15, and 0.19 with an NPV of 0.92, 0.91, and 0.92 for Observers 1, 2, and 3, respectively. The ROC analysis showed a significantly increased area under the curve (AUC) for the overall score when compared to T2W alone for two of the three observers.

CONCLUSION

1.5 T mpMRI using the PI-RADS to localize the index tumor achieved moderate reliability and diagnostic performance.

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.

Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI

OBJECTIVES

We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI).

METHODS

One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance.

RESULTS

Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively.

CONCLUSIONS

LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists.

KEY POINTS

• MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.

Semi-quantitative parameter analysis of DCE-MRI revisited: monte-carlo simulation, clinical comparisons, and clinical validation of measurement errors in patients with type 2 neurofibromatosis

PURPOSE

To compare semi-quantitative (SQ) and pharmacokinetic (PK) parameters for analysis of dynamic contrast enhanced MR data (DCE-MRI) and investigate error-propagation in SQ parameters.

METHODS

Clinical data was collected from five patients with type 2-neurofibromatosis (NF2) receiving anti-angiogenic therapy for rapidly growing vestibular schwannoma (VS). There were 7 VS and 5 meningiomas. Patients were scanned prior to therapy and at days 3 and 90 of treatment. Data was collected using a dual injection technique to permit direct comparison of SQ and PK parameters. Monte Carlo modeling was performed to assess potential measurement errors in SQ parameters in persistent, washout, and weakly enhancing tissues. The simulation predictions for five semi-quantitative parameters were tested using the clinical DCE-MRI data.

RESULTS

In VS, SQ parameters and Ktrans showed close correlation and demonstrated similar therapy induced reductions. In meningioma, only the denoised Signal Enhancement Ratio (Rse1/se2(DN)) showed a significant therapy induced reduction (p<0.05). Simulation demonstrated: 1) Precision of SQ metrics normalized to the pre-contrast-baseline values (MSErel and ∑MSErel) is improved by use of an averaged value from multiple baseline scans; 2) signal enhancement ratio Rmse1/mse2 shows considerable susceptibility to noise; 3) removal of outlier values to produce a new parameter, Rmse1/mse2(DN), improves precision and sensitivity to therapy induced changes. Direct comparison of in-vivo analysis with Monte Carlo simulation supported the simulation predicted error distributions of semi-quantitative metrics.

CONCLUSION

PK and SQ parameters showed similar sensitivity to anti-angiogenic therapy induced changes in VS. Modeling studies confirmed the benefits of averaging baseline signal from multiple images for normalized SQ metrics and demonstrated poor noise tolerance in the widely used signal enhancement ratio, which is corrected by removal of outlier values.

Perfusion MRI: the five most frequently asked clinical questions

OBJECTIVE

This article addresses questions that radiologists frequently ask when planning, performing, processing, and interpreting MRI perfusion studies in CNS imaging.

CONCLUSION

Perfusion MRI is a promising tool in assessing stroke, brain tumors, and neurodegenerative diseases. Most of the impediments that have limited the use of per-fusion MRI can be overcome to allow integration of these methods into modern neuroimaging protocols.

MRI in prostate cancer

Imaging studies play an important role in detection and management of prostate cancer and MRI especially with the use of endorectal coil because of high contrast resolution is recognized as the best imaging modality in evaluation of prostate cancer. Multiparametric MR study including T1 and T2 weighted images, diffusion weighted images, dynamic contrast study and MR spectroscopy is useful for detection and local staging of prostate cancer as well as posts treatment evaluation of patients either after surgery or radiation therapy for detection of local recurrence.

Prostate MRI: can we do without DCE sequences in 2013?

Multiparametric MRI (mp-MRI) of the prostate currently provides stable and reproducible performances. The usefulness of dynamic contrast-enhanced (DCE) sequences is currently challenged, as they sometimes only confirm what has already been observed on diffusion-weighted imaging (DWI) and require the additional purchase of a contrast agent. Eliminating these sequences may help accelerate the use of MRI in addition to, or in lieu of, prostate biopsies in selected patients. However, many studies show that these sequences can detect lesions invisible on T2-weighted and diffusion-weighted images, better assess cancer extension and aggressiveness, and finally help detecting recurrence after treatment. We present the various applications of dynamic MRI and discuss the possible consequences of its omission from the current protocol.

Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer

Magnetic resonance (MR) examinations of men with prostate cancer are most commonly performed for detecting, characterizing, and staging the extent of disease to best determine diagnostic or treatment strategies, which range from biopsy guidance to active surveillance to radical prostatectomy. Given both the exam's importance to individual treatment plans and the time constraints present for its operation at most institutions, it is essential to perform the study effectively and efficiently. This article reviews the most commonly employed modern techniques for prostate cancer MR examinations, exploring the relevant signal characteristics from the different methods discussed and relating them to intrinsic prostate tissue properties. Also, a review of recent articles using these methods to enhance clinical interpretation and assess clinical performance is provided. J. Magn. Reson. Imaging 2013;37:1035-1054. © 2013 Wiley Periodicals, Inc.

Using intraoperative dynamic contrast-enhanced T1-weighted MRI to identify residual tumor in glioblastoma surgery

OBJECT

The goal of surgery in high-grade gliomas is to maximize the resection of contrast-enhancing tumor without causing additional neurological deficits. Intraoperative MRI improves surgical results. However, when using contrast material intraoperatively, it may be difficult to differentiate between surgically induced enhancement and residual tumor. The purpose of this study was to assess the usefulness of intraoperative dynamic contrast-enhanced T1-weighted MRI to guide this differential diagnosis and test it against tissue histopathology.

METHODS

Preoperative and intraoperative dynamic contrast-enhanced MRI was performed in 21 patients with histopathologically confirmed WHO Grade IV gliomas using intraoperative 3-T MRI. Standardized regions of interest (ROIs) were placed manually at 2 separate contrast-enhancing areas at the resection border for each patient. Time-intensity curves (TICs) were generated for each ROI. All ROIs were biopsied and the TIC types were compared with histopathological results. Pharmacokinetic modeling was performed in the last 10 patients to confirm nonparametric TIC analysis findings.

RESULTS

Of the 42 manually selected ROIs in 21 patients, 25 (59.5%) contained solid tumor tissue and 17 (40.5%) retained the brain parenchymal architecture but contained infiltrating tumor cells. Time-intensity curves generated from residual contrast-enhancing tumor and their preoperative counterparts were comparable and showed a quick and persistently increasing slope ("climbing type"). All 17 TICs obtained from regions that did not contain solid tumor tissue were undulating and low in amplitude, compared with those obtained from residual tumors ("low-amplitude type"). Pharmacokinetic findings using the transfer constant, extravascular extracellular volume fraction, rate constant, and initial area under the curve parameters were significantly different for the tumor mass, nontumoral regions, and surgically induced contrast-enhancing areas.

CONCLUSIONS

Intraoperative dynamic contrast-enhanced MRI provides quick, reproducible, high-quality, and simply interpreted dynamic MR images in the intraoperative setting and can aid in differentiating surgically induced enhancement from residual tumor.

Measurements of tumor vascular leakiness using DCE in brain tumors: clinical applications

Various imaging techniques have been employed to evaluate blood-brain-barrier leakiness in brain tumors, as higher tumor vascular leakiness is known to be associated with higher grade and malignant potential of the tumor, and hence can help provide additional diagnostic and prognostic information. These imaging techniques range from routine post-contrast T1 -weighted images that highlight degree of contrast enhancement to absolute measurement of quantitative metrics of vascular leakiness employing complex pharmacokinetic modeling. The purpose of this article is to discuss the clinical applications of available imaging techniques, and in particular dynamic contrast-enhanced T1 -weighted MR imaging (DCE-MRI), to evaluate tumor vascular leakiness.

Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 T

BACKGROUND

A challenge in the diagnosis of prostate cancer (PCa) is the accurate assessment of aggressiveness.

OBJECTIVE

To validate the performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of the prostate at 3 tesla (T) for the assessment of PCa aggressiveness, with prostatectomy specimens as the reference standard.

DESIGN, SETTINGS, AND PARTICIPANTS

A total of 45 patients with PCa scheduled for prostatectomy were included. This study was approved by the institutional review board; the need for informed consent was waived.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Subjects underwent a clinical MRI protocol including DCE-MRI. Blinded to DCE-images, PCa was indicated on T2-weighted images based on histopathology results from prostatectomy specimens with the use of anatomical landmarks for the precise localization of the tumor. PCa was classified as low-, intermediate-, or high-grade, according to Gleason score. DCE-images were used as an overlay on T2-weighted images; mean and quartile values from semi-quantitative and pharmacokinetic model parameters were extracted per tumor region. Statistical analysis included Spearman's ρ, the Kruskal-Wallis test, and a receiver operating characteristics (ROC) analysis.

RESULTS AND LIMITATIONS

Significant differences were seen for the mean and 75th percentile (p75) values of wash-in (p = 0.024 and p = 0.017, respectively), mean wash-out (p = 0.044), and p75 of transfer constant (K(trans)) (p = 0.035), all between low-grade and high-grade PCa in the peripheral zone. ROC analysis revealed the best discriminating performance between low-grade versus intermediate-grade plus high-grade PCa in the peripheral zone for p75 of wash-in, K(trans), and rate constant (Kep) (area under the curve: 0.72). Due to a limited number of tumors in the transition zone, a definitive conclusion for this region of the prostate could not be drawn.

CONCLUSIONS

Quantitative parameters (K(trans) and Kep) and semi-quantitative parameters (wash-in and wash-out) derived from DCE-MRI at 3 T have the potential to assess the aggressiveness of PCa in the peripheral zone. P75 of wash-in, K(trans), and Kep offer the best possibility to discriminate low-grade from intermediate-grade plus high-grade PCa.

Prostate cancer magnetic resonance imaging (MRI): multidisciplinary standpoint

Prostate cancer is the most common cancer diagnosed in men and a leading cause of death. Accurate assessment is a prerequisite for optimal clinical management and therapy selection of prostate cancer. There are several parameters and nomograms to differentiate between patients with clinically insignificant disease and patients in need of treatment. Magnetic resonance imaging (MRI) is a technique which provides more detailed anatomical images due to high spatial resolution, superior contrast resolution, and multiplanar capability. State-of-the-art MRI techniques, such as diffusion weighted imaging (DWI), MR spectroscopic imaging (MRSI), dynamic contrast enhanced MRI (DCE-MRI), improve interpretation of prostate cancer imaging. In this article, we review the major role of MRI in the advanced management of prostate cancer to noninvasively improve tumor staging, biologic potential, treatment planning, therapy response, local recurrence, and to guide target biopsy for clinical suspected cancer with previous negative biopsy. Finally, future challenges and opportunities in prostate cancer management in the area of functional MRI are discussed as well.

Embolization therapy for benign prostatic hyperplasia: influence of embolization particle size on gland perfusion

PURPOSE

To assess the influence of embolic size on the therapy response of prostatic arterial embolization (PAE) based on perfusional changes seen on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Twelve beagles underwent PAE, four dogs with each particle size: A: 100-300 μm; B: 300-500 μm; and C: 500-700 μm. Prior to and 1 month after the embolization all dogs underwent prostate DCE MRI.

RESULTS

After embolization, time to maximal perfusion intensity for prostate parenchyma increased in B (188 vs. 135 sec, P = 0.023) and C (200 vs. 120 sec, P = 0.001), while it did not change for A (139 vs. 124 sec, P = 0.39). The maximal relative intensity increased after embolization in C (3.84 vs. 2.38, P < 0.001), while it did not change for A (2.50 vs. 2.44, P = 0.36) and B (3.23 vs. 2.9, P = 0.21). The extent of visualized intraprostatic urethral wall increased after embolization in B compared with A and C, 239.5 ± 138.1% vs. 56.1 ± 34.3, P = 0.04. Enhancement changes correlated with prostate volume changes: prostate volumes in A decreased less as compared with B and C (77 ± 34% vs. 56 ± 14%), P = 0.02.

CONCLUSION

The enhancement and morphological data are useful to monitor response to therapy after embolization. Embolization with 300-500 and 500-700 μm particle may provide better results than with 100-300 μm particles in a canine model.

Magnetic resonance imaging (MRI) and dynamic MRI evaluation of extranodal non-Hodgkin lymphoma in oral and maxillofacial regions

OBJECTIVE

The purpose of this study was to evaluate the diagnostic value of magnetic resonance imaging (MRI), especially dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), in extranodal non-Hodgkin lymphoma (NHL) of oral and maxillofacial regions.

STUDY DESIGN

Thirteen cases with extranodal NHL were examined using MRI. T1-weighted images (T1WI) and T2-weighted images (T2WI) or short TI inversion recovery (STIR) images were obtained in all cases. Contrast-enhanced images and DCE-MRI were acquired in 10 and 7 cases, respectively. On DCE-MRIs, we analyzed the parameters as follows: contrast index at maximal contrast enhancement (CImax), maximum contrast index (CI) gain/CImax ratio, and washout ratios (WR(300), WR(600), and WR(900)) at 300, 600, and 900 seconds after contrast medium injection.

RESULTS

The signal intensity of all lesions was hypointense to isointense on T1WIs and showed variable contrast enhancement patterns. On T2WIs and STIR images, the signal intensity was isointense to hyperintense in almost all cases. Analysis of DCE-MRI parameters in extranodal NHLs resulted in the identification of 4 types of CI curves according to CImax and WR: (1) CImax greater than 2.0 and WR(900) greater than 40%, (2) CImax greater than 2.0 and WR(900) less than 40%, (3) CImax less than 1.5 and WR(900) greater than 40%, and (4) CImax less than 1.5 and WR(900) greater than 40%.

CONCLUSIONS

The signal intensities on MRI were not specific to extranodal NHL and resembled those of other tumor types. When CImax was less than 1.5 or WR900 was less than 40%, these parameters contributed to diagnosis in extranodal NHLs.

Assessment of the effect of haematocrit-dependent arterial input functions on the accuracy of pharmacokinetic parameters in dynamic contrast-enhanced MRI

The detection and prognosis of prostate cancer in its early stages are critically important. It is therefore essential to improve the existing dynamic contrast-enhanced MRI (DCE MRI) techniques commonly used for the assessment of the tumour vascular environment. The goal of this study was to describe a method for the estimation of the arterial input function (AIF) in DCE MRI by measuring R(2) * values in the femoral artery of patients with early-stage prostate cancer. The calculation of contrast agent concentrations was based on calibration curves determined in whole blood samples for a range of normal haematocrit (HCT) values (HCT = 0.35-0.525). Individual AIFs corrected for HCT were compared with individual AIFs calibrated with a mean whole blood [R(2)*-Gd-DTPA-BMA] [Gd-DTPA-BMA, gadolinium diethylenetriaminepentaacetate-bis(methylamide) (gadodiamide)] curve at an assumed HCT = 0.44, as well as a population AIF at an assumed HCT = 0.45. The area under the curve of the first-pass bolus ranged between 0.6 min mM at HCT = 0.53 and 1.3 min mM at HCT = 0.39. Significant differences in magnitude at peak contrast agent concentrations (HCT = 0.36, [Gd-DTPA-BMA](max) = 9 ± 0.4 mM; HCT = 0.46, [Gd-DTPA-BMA](max) = 4.0 ± 0.2 mM) were found. Using model-based simulations, the accuracy of the kinetic parameters estimated using individual AIFs corrected for HCT demonstrated that, for the use of individual calibration curves with HCT values differing by more than 10%, K(trans) and k(ep) values were largely underestimated (up to 60% difference for K(trans)). Moreover, blood volume estimates were severely underestimated. Estimates of kinetic parameters in early-stage prostate cancer patients demonstrated that the efflux rate constant (k(ep)) was influenced significantly by the definition of AIF. Regardless of whether an individually calibrated AIF or a population AIF (average of all individually calibrated AIFs) was used, pixel-by-pixel mapping of k(ep) and v(b) in the prostate gland appeared to be more sensitive than with the usual biexponential approach.

Differentiating treatment-induced necrosis from recurrent/progressive brain tumor using nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion

Differentiating treatment-induced necrosis (TIN) from recurrent/progressive tumor (RPT) in brain tumor patients using conventional morphologic imaging features is a very challenging task. Functional imaging techniques also offer moderate success due to the complexity of the tissue microenvironment and the inherent limitation of the various modalities and techniques. The purpose of this retrospective study was to assess the utility of nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion (DCET1MRP) in differentiating TIN from RPT. Twenty-nine patients with previously treated brain tumors who showed recurrent or progressive enhancing lesion on follow-up MRI underwent DCET1MRP. Another 8 patients with treatment-naive high-grade gliomas who also underwent DCET1MRP were included as the control group. Semiquantitative indices derived from DCET1MRP included maximum slope of enhancement in initial vascular phase (MSIVP), normalized MSIVP (nMSIVP), normalized slope of delayed equilibrium phase (nSDEP), and initial area under the time-intensity curve (IAUC) at 60 and 120 s (IAUC(60) and IAUC(120)) obtained from the enhancement curve. There was a statistically significant difference between the 2 groups (P < .01), with the RPT group showing higher MSIVP (15.78 vs 8.06), nMSIVP (0.046 vs 0.028), nIAUC(60) (33.07 vs 6.44), and nIAUC(120) (80.14 vs 65.55) compared with the TIN group. nSDEP was significantly lower in the RPT group (7.20 × 10(-5) vs 15.35 × 10(-5)) compared with the TIN group. Analysis of the receiver-operating-characteristic curve showed nMSIVP to be the best single predictor of RPT, with very high (95%) sensitivity and high (78%) specificity. Thus, nonmodel-based semiquantitative indices derived from DCET1MRP that are relatively easy to derive and do not require a complex model-based approach may aid in differentiating RPT from TIN and can be used as robust noninvasive imaging biomarkers.

Therapeutic response in musculoskeletal soft tissue sarcomas: evaluation by MRI

This article provides a literature review of the use of MRI in monitoring the treatment response of soft tissue sarcomas. The basic classification and physiology of soft tissue tumors are introduced. Then, the major treatment options for soft tissue sarcomas are summarized with brief coverage of possible responses and grading systems. Four major branches of MRI techniques are covered, including conventional T(1) - and T(2) -weighted imaging, contrast-enhanced MRI, MR diffusion and perfusion imaging, and MRS, with a focus on the tumor microenvironment. Although this literature survey focuses on recent clinical developments using these MRI techniques, research venues in preclinical studies, as well as in potential applications other than soft tissue sarcomas, are also included when comparable and/or mutually supporting. Examples from other less-discussed MRI modalities are also briefly covered, not only to complement, but also to expand, the scope and depth of information for various kinds of lesions.

MRI for men undergoing active surveillance or with rising PSA and negative biopsies

The role of imaging in treatment decision-making for patients with prostate cancer is to characterize the cancer already diagnosed on biopsy, to determine tumor location, to assess tumor volume, and to exclude more-extensive disease. MRI is currently the most established imaging modality for this purpose, with the highest sensitivity and specificity for detection and staging of prostate tumors. The development and wider adoption of active surveillance and focal treatment approaches would also benefit from accurate localization of cancer. As such, 3 T MRI and multiparametric approaches are being developed as tools for the localization and staging of prostate cancer. Men wishing to commence or remain on active surveillance might benefit by having larger cancers identified before embarking on this management strategy. MRI might have its greatest role in patients where there is a discrepancy between PSA and biopsy results suggesting a potential missed prostate tumor.

Focal therapy for prostate cancer: possibilities and limitations

CONTEXT

A significant proportion of patients diagnosed with prostate cancer have well-differentiated, low-volume tumors at minimal risk of impacting their quality of life or longevity. The selection of a treatment strategy, among the multitude of options, has enormous implications for individuals and health care systems.

OBJECTIVE

Our aim was to review the rationale, patient selection criteria, diagnostic imaging, biopsy schemes, and treatment modalities available for the focal therapy of localized prostate cancer. We gave particular emphasis to the conceptual possibilities and limitations.

EVIDENCE ACQUISITION

A National Center for Biotechnology Information PubMed search (www.pubmed.gov) was performed from 1995 to 2009 using medical subject headings "focal therapy" or "ablative" and "prostate cancer." Additional articles were extracted based on recommendations from an expert panel of authors.

EVIDENCE SYNTHESIS

Focal therapy of the prostate in patients with low-risk cancer characteristics is a proposed treatment approach in development that aims to eradicate all known foci of cancer while minimizing damage to adjacent structures necessary for the preservation of urinary, sexual, and bowel function. Conceptually, focal therapy has the potential to minimize treatment-related toxicity without compromising cancer-specific outcome. Limitations include the inability to stage or grade the cancer(s) accurately, suboptimal imaging capabilities, uncertainty regarding the natural history of untreated cancer foci, challenges with posttreatment monitoring, and the lack of quality-of-life data compared with alternative treatment strategies. Early clinical experiences with modest follow-up evaluating a variety of modalities are encouraging but hampered by study design limitations and small sample sizes.

CONCLUSIONS

Prostate focal therapy is a promising and emerging treatment strategy for men with a low risk of cancer progression or metastasis. Evaluation in formal prospective clinical trials is essential before this new strategy is accepted in clinical practice. Adequate trials must include appropriate end points, whether absence of cancer on biopsy or reduction in progression of cancer, along with assessments of safety and longitudinal alterations in quality of life.

Diffusion-weighted imaging with apparent diffusion coefficient mapping and spectroscopy in prostate cancer

Prostate cancer is a major health problem, and the exploration of noninvasive imaging methods that have the potential to improve specificity while maintaining high sensitivity is still critically needed. Tissue changes induced by tumor growth can be visualized by magnetic resonance imaging (MRI) methods. Current MRI methods include conventional T2-weighted imaging, diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping and magnetic resonance spectroscopy (MRS). Techniques such as DWI/ADC provide functional information about the behavior of water molecules in tissue; MRS can provide biochemical information about the presence or absence of certain metabolites, such as choline, creatine, and citrate. Finally, vascular parameters can be investigated using dynamic contrast-enhanced MRI. Moreover, with whole-body MRI and DWI, metastatic disease can be evaluated in 1 session and may provide a way to monitor treatment. Therefore, when combining these various methods, a multiparametric data set can be built to assist in the detection, localization, assessment of prostate cancer aggressiveness, and tumor staging. Such a comprehensive approach offers more power to evaluate prostate disease than any single measure alone. In this article, we focus on the role of DWI/ADC and MRS in the detection and characterization using both in vivo and ex vivo imaging of prostate pathology.