Apparent Diffusion Coefficient Values of the Benign Central Zone of the Prostate: Comparison With Low- and High-Grade Prostate Cancer

Clinical-imaging metrics for the diagnosis of prostate cancer in PI-RADS 3 lesions

OBJECTIVE

To explore the feasibility and efficacy of clinical-imaging metrics in the diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in prostate imaging-reporting and data system (PI-RADS) category 3 lesions.

METHODS

A retrospective analysis was conducted on lesions diagnosed as PI-RADS 3. They were categorized into benign, non-csPCa and csPCa groups. Apparent diffusion coefficient (ADC), T2-weighted imaging signal intensity (T2WISI), coefficient of variation of ADC and T2WISI, prostate-specific antigen density (PSAD), ADC density (ADCD), prostate-specific antigen lesion volume density (PSAVD) and ADC lesion volume density (ADCVD) were measured and calculated. Univariate and multivariate analyses were used to identify risk factors associated with PCa and csPCa. Receiver operating characteristic curve (ROC) and decision curves were utilized to assess the efficacy and net benefit of independent risk factors.

RESULTS

Among 202 patients, 133 had benign prostate disease, 25 non-csPCa and 44 csPCa. Age, PSA and lesion location showed no significant differences (P > 0.05) among the groups. T2WISI and coefficient of variation of ADC (ADCcv) were independent risk factors for PCa in PI-RADS 3 lesions, yielding an area under the curve (AUC) of 0.68. ADC was an independent risk factor for csPCa in PI-RADS 3 lesions, yielding an AUC of 0.65. Decision curve analysis showed net benefit for patients at certain probability thresholds.

CONCLUSIONS

T2WISI and ADCcv, along with ADC, respectively showed considerable promise in enhancing the diagnosis of PCa and csPCa in PI-RADS 3 lesions.

Interpretation of Prostate Magnetic Resonance Imaging Using Prostate Imaging and Data Reporting System Version 2.1: A Primer

Prostate magnetic resonance imaging (MRI) is increasingly being used to diagnose and stage prostate cancer. The Prostate Imaging and Data Reporting System (PI-RADS) version 2.1 is a consensus-based reporting system that provides a standardized and reproducible method for interpreting prostate MRI. This primer provides an overview of the PI-RADS system, focusing on its current role in clinical interpretation. It discusses the appropriate use of PI-RADS and how it should be applied by radiologists in clinical practice to assign and report PI-RADS assessments. We also discuss the changes from prior versions and published validation studies on PI-RADS accuracy and reproducibility.

Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole mount histopathology

In this study, the objective was to characterize the MR signatures of the various benign prostate tissues and to differentiate them from cancer. Data was from seventy prostate cancer patients who underwent multiparametric MRI (mpMRI) and subsequent prostatectomy. The scans included T2-weighted imaging (T2W), diffusion weighted imaging, dynamic contrast-enhanced MRI (DCE MRI), and MR spectroscopic imaging. Histopathology tissue information was translated to MRI images. The mpMRI parameters were characterized separately per zone and by tissue type. The tissues were ordered according to trends in tissue parameter means. The peripheral zone tissue order was cystic atrophy, high grade prostatic intraepithelial neoplasia (HGPIN), normal, atrophy, inflammation, and cancer. Decreasing values for tissue order were exhibited by ADC (1.8 10^-3 mm²/s to 1.2 10^-3 mm²/s) and T2W intensity (3447 to 2576). Increasing values occurred for DCE MRI peak (143% to 157%), DCE MRI slope (101%/min to 169%/min), fractional anisotropy (FA) (0.16 to 0.19), choline (7.2 to 12.2), and choline / citrate (0.3 to 0.9). The transition zone tissue order was cystic atrophy, mixed benign prostatic hyperplasia (BPH), normal, atrophy, inflammation, stroma, anterior fibromuscular stroma, and cancer. Decreasing values occurred for ADC (1.6 10^-3 mm²/s to 1.1 10^-3 mm²/s) and T2W intensity (2863 to 2001). Increasing values occurred for DCE MRI peak (143% to 150%), DCE MRI slope (101%/min to 137%/min), FA (0.18 to 0.25), choline (7.9 to 11.7), and choline / citrate (0.3 to 0.7). Logistic regression was used to create parameter model fits to differentiate cancer from benign prostate tissues. The fits achieved AUCs ≥0.91. This study quantified the mpMRI characteristics of benign prostate tissues and demonstrated the capability of mpMRI to discriminate among benign as well as cancer tissues, potentially aiding future discrimination of cancer from benign confounders.

Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study

Background

Apparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value.

Objectives

We aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks.

Methods

This prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm². ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient.

Results

The s-ADC_b1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADC_b50 and s-ADC_b1500 (all P < 0.001). Both z-ADC and s-ADC_b1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC.

Conclusion

The deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.

Prostate Multiparametric MRI: Common Pitfalls in Primary Diagnosis and How to Avoid Them

Purpose of Review

To provide the radiologist with basic knowledge about normal and abnormal findings in the prostatic mp-MRI, taking a look at the possible diagnostic pitfalls commonly seen in daily clinical practice, allowing him to recognize and consequently avoid them.

Recent Findings

Prostate mp-MRI has now become commonly used in most diagnostic imaging centers, as a precise, accurate and above all non-invasive tool, useful in the diagnosis, staging and follow-up of prostate diseases, first of all prostatic carcinoma. For this reason, it is important to take into account the existence of numerous possible anatomic and pathologic processes which can mimick or masquerade as prostate cancer.

Summary

Through the combination of anatomical (T2WI) and functional sequences (DWI/ADC and DCE), the mp-MRI of the prostate provides all the information necessary for a correct classification of patients with prostate disease, cancer in particular. It is not uncommon, however, for the radiologist to make errors in the interpretation of imaging due to conditions, pathological or otherwise, that mimic prostate cancer and that, consequently, affect the diagnostic/therapeutic process of patients. The strategy, and what this pictorial review aims at, is to learn to recognize the potential pitfalls of the prostatic mp-MRI and avoid them.

Perspective: a critical assessment of PI-RADS 2.1

PURPOSE

In this article we take a critical look at the key changes of the newest edition of the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 (v2.1) and indicate future directions for further development of the system.

CONCLUSION

PI-RADS v2.1 addresses some of the shortcomings of its widely embraced precursor version 2, largely to simplify interpretation and improve interobserver agreement without changing the fundamental acquisition and scoring guidelines. Biparametric MRI is acknowledged in the newest version, but multiparametric MRI including dynamic contrast-enhanced imaging is still recommended for most scenarios. Management recommendations and guidance on evaluation of follow-up MRI's are still not included in the system.

Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement

Prostate cancer is the most common malignancy in male population. Over the last few years, magnetic resonance imaging (MRI) has proved to be a robust clinical tool for identification and staging of clinically significant prostate cancer. Though suggestions by the European Society of Urogenital Radiology to use complete multiparametric (mp) T2-weighted/diffusion weighted imaging (DWI)/dynamic contrast enhancement (DCE) acquisition for all prostate MRI examinations, the real advantage of functional DCE remains a matter of debate. Recent studies demonstrate that biparametric (bp) and mp approaches have similar accuracy, but controversial evidences remain, and the specific potential benefits of contrast medium administration are still poorly discussed in literature. The bp approach is in fact sufficient in most cases to adequately identify a negative test, or to accurately define the degree of aggressiveness of a lesion, especially if larger or with major characteristics of malignancy. This feature would give the DCE a secondary role, probably limited to a second evaluation of the lesion location, for detecting small cancer or in case of controversy. However, DCE has proved to increase the sensitivity of prostate MRI, though a less specificity. Therefore, an appropriate decision algorithm is needed to standardize the MRI approach. Aim of this review study was to provide a schematic description of bpMRI and mpMRI approaches in the study of prostatic anatomy, focusing on comparative validity and current DCE application. Additional theoretical considerations on prostate MRI are provided.

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

Prostate MR: pitfalls and benign lesions

Multiparametric MRI (mpMRI) of the prostate has evolved to be an integral component for the diagnosis, risk stratification, staging, and targeting of prostate cancer. However, anatomic and histologic mimics of prostate cancer on mpMRI exist. Anatomic feature that mimic prostate cancer on mpMRI include anterior fibromuscular stroma, normal central zone, periprostatic venous plexus, and thickened surgical capsule (transition zone pseudocapsule). Benign conditions such as post-biopsy hemorrhage, prostatitis or inflammation, focal prostate atrophy, benign prostatic hyperplasia nodules, and prostatic calcifications can also mimic prostate cancer on mpMRI. Technical challenges and other pitfalls such as image distortion, motion artifacts, and endorectal coil placements can also limit the efficacy of mpMRI. Knowledge of prostate anatomy, location of the lesion and its imaging features on different sequences, and being familiar with the common pitfalls are critical for the radiologists who interpret mpMRI. Therefore, this article reviews the pitfalls (anatomic structures and technical challenges) and benign lesions or abnormalities that may mimic prostate cancer on mpMRI and how to interpret them.

Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence

Purpose of this study was to evaluate the diagnostic performance of T1 relaxation time (T1) for differentiating prostate cancer (PCa) from benign tissue as well as high- from low-grade PCa. Twenty-three patients with suspicion for PCa were included in this prospective study. 3 T MRI including a Modified Look-Locker inversion recovery sequence was acquired. Subsequent targeted and systematic prostate biopsy served as a reference standard. T1 and apparent diffusion coefficient (ADC) value in PCa and reference regions without malignancy as well as high- and low-grade PCa were compared using the Mann-Whitney U test. The performance of T1, ADC value, and a combination of both to differentiate PCa and reference regions was assessed by receiver operating characteristic (ROC) analysis. T1 and ADC value were lower in PCa compared to reference regions in the peripheral and transition zone (p < 0.001). ROC analysis revealed high AUCs for T1 (0.92; 95%-CI, 0.87-0.98) and ADC value (0.97; 95%-CI, 0.94 to 1.0) when differentiating PCa and reference regions. A combination of T1 and ADC value yielded an even higher AUC. The difference was statistically significant comparing it to the AUC for ADC value alone (p = 0.02). No significant differences were found between high- and low-grade PCa for T1 (p = 0.31) and ADC value (p = 0.8). T1 relaxation time differs significantly between PCa and benign prostate tissue with lower T1 in PCa. It could represent an imaging biomarker for PCa.

Magnetic resonance imaging/transrectal ultrasonography fusion targeted prostate biopsy finds more significant prostate cancer in biopsy-naïve Japanese men compared with the standard biopsy

OBJECTIVE

To assess the clinical benefits of magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy for biopsy-naïve Japanese men.

METHODS

Between February 2017 and August 2018, 131 biopsy-naïve men who underwent targeted biopsy together with 10-core systematic biopsy at Hiroshima University Hospital were retrospectively investigated. Multiparametric magnetic resonance imaging findings were reported based on Prostate Imaging Reporting and Data System version 2.

RESULTS

The overall cancer detection rates per patient were 69.5% in systematic biopsy + targeted biopsy cores, 61.1% in systematic biopsy cores and 61.1% in targeted biopsy cores. The detection rates for clinically significant prostate cancer were 43.5% in targeted biopsy cores and 35.9% in systematic biopsy cores (P = 0.04), whereas the detection rates for clinically insignificant prostate cancer were 17.6% and 25.2% respectively (P = 0.04). Lesions in the peripheral zone were diagnosed more with clinically significant prostate cancer (54.8% vs 20.7%, P < 0.001) and International Society of Urological Pathology grade (3.2 vs 2.7, P = 0.02) than that in the inner gland. Just 4.2% (3/71) of Prostate Imaging Reporting and Data System category 2 and 3 lesions in the middle or base of the inner gland were found to have clinically significant prostate cancer. The cancer detection rate per core was 42.3% in targeted biopsy cores, whereas it was 17.9% in systematic biopsy cores (P < 0.001).

CONCLUSIONS

Targeted biopsy is able to improve the diagnostic accuracy of biopsy in detection of clinically significant prostate cancer by reducing the number of clinically insignificant prostate cancer detections compared with 10-core systematic biopsy in biopsy-naïve Japanese men. In addition, the present findings suggest that patients with Prostate Imaging Reporting and Data System category 2 or 3 lesions at the middle or base of the inner gland might avoid biopsies.

PI-RADS version 2.1: one small step for prostate MRI

Multiparametric (mp) prostate magnetic resonance imaging (MRI) is playing an increasingly prominent role in the diagnostic work-up of patients with suspected prostate cancer. Performing mpMRI before biopsy offers several advantages including biopsy avoidance under certain clinical circumstances and targeting biopsy of suspicious lesions to enable the correct diagnosis. The success of the technique is heavily dependent on high-quality image acquisition, interpretation, and report communication, all areas addressed by previous versions of the Prostate Imaging-Reporting and Data System (PI-RADS) recommendations. Numerous studies have validated the approach, but the widespread adoption of PI-RADS version 2 has also highlighted inconsistencies and limitations, particularly relating to interobserver variability for evaluation of the transition zone. These limitations are addressed in the recently released version 2.1. In this article, we highlight the key changes proposed in PI-RADS v2.1 and explore the background reasoning and evidence for the recommendations.

VERDICT-AMICO: Ultrafast fitting algorithm for non-invasive prostate microstructure characterization

VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumours) estimates and maps microstructural features of cancerous tissue non-invasively using diffusion MRI. The main purpose of this study is to address the high computational time of microstructural model fitting for prostate diagnosis, while retaining utility in terms of tumour conspicuity and repeatability. In this work, we adapt the accelerated microstructure imaging via convex optimization (AMICO) framework to linearize the estimation of VERDICT parameters for the prostate gland. We compare the original non-linear fitting of VERDICT with the linear fitting, quantifying accuracy with synthetic data, and computational time and reliability (performance and precision) in eight patients. We also assess the repeatability (scan-rescan) of the parameters. Comparison of the original VERDICT fitting versus VERDICT-AMICO showed that the linearized fitting (1) is more accurate in simulation for a signal-to-noise ratio of 20 dB; (2) reduces the processing time by three orders of magnitude, from 6.55 seconds/voxel to 1.78 milliseconds/voxel; (3) estimates parameters more precisely; (4) produces similar parametric maps and (5) produces similar estimated parameters with a high Pearson correlation between implementations, r2  > 0.7. The VERDICT-AMICO estimates also show high levels of repeatability. Finally, we demonstrate that VERDICT-AMICO can estimate an extra diffusivity parameter without losing tumour conspicuity and retains the fitting advantages. VERDICT-AMICO provides microstructural maps for prostate cancer characterization in seconds.

New prostate cancer prognostic grade group (PGG): Can multiparametric MRI (mpMRI) accurately separate patients with low-, intermediate-, and high-grade cancer?

PURPOSE

Our objective is to determine the accuracy of multiparametric MRI (mpMRI) in predicting pathologic grade of prostate cancer (PCa) after radical prostatectomy (RP) using simple apparent diffusion coefficient metrics and, specifically, whether mpMRI can accurately separate disease into one of two risk categories (low vs. higher grade) or one of three risk categories (low, intermediate, or high grade) corresponding to the new prognostic grade group (PGG) criteria.

METHODS

This retrospective, HIPAA-compliant, IRB-approved study included 140 patients with PCa who underwent 3 T mpMRI with endorectal coil and transrectal ultrasound-guided (TRUS-G) biopsy before RP. MpMRI was used to classify lesions using a two-tier (low-grade/PGG 1 vs. high-grade/PGG 2-5) or a three-tier system (low-grade/PGG 1 vs. intermediate-grade/PGG 2 vs. high-grade/PGG 3-5). Accuracy of mpMRI was compared against RP for each system.

RESULTS

The predictive accuracy of mpMRI using the two-tier system is higher than when using three-tier system (0.77 and 0.45, respectively). There were similar rates of undergrading between mpMRI and TRUS-G biopsy compared to RP (16% & 21%; respectively); rate of overgrading was higher for mpMRI vs. TRUS-G biopsy compared to RP (42% & 17%, respectively). When mpMRI and TRUS-G biopsy are combined, rate of undergrading is 1.4% and overgrading is 11%.

CONCLUSIONS

MpMRI predictive accuracy is higher when using a two-tier vs. a three-tier system, suggesting that advanced metrics may be necessary to delineate intermediate- from high-grade disease. Rates of under- and overgrading decreased when mpMRI and TRUS-G biopsy are combined, suggesting that these techniques may be complementary in predicting tumor grade.

Multiparametric magnetic resonance imaging for transition zone prostate cancer: essential findings, limitations, and future directions

OBJECTIVE

Review the multiparametric MRI (mpMRI) findings of transition zone (TZ) prostate cancer (PCa) using T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI and to integrate mpMRI findings with clinical history, laboratory values, and histopathology.

CONCLUSION

TZ prostate tumors are challenging to detect clinically and at MRI. mpMRI using the combination of sequences has the potential to improve accuracy of TZ cancer detection and staging.

Risk Stratification Among Men With Prostate Imaging Reporting and Data System version 2 Category 3 Transition Zone Lesions: Is Biopsy Always Necessary?

OBJECTIVE

The objective of our study was to determine the clinical and MRI characteristics of clinically significant prostate cancer (PCA) (Gleason score ≥ 3 + 4) in men with Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) category 3 transition zone (TZ) lesions.

MATERIALS AND METHODS

From 2014 to 2016, 865 men underwent prostate MRI and MRI/ultrasound (US) fusion biopsy (FB). A subset of 90 FB-naïve men with 96 PI-RADSv2 category 3 TZ lesions was identified. Patients were imaged at 3 T using a body coil. Images were assigned a PI-RADSv2 category by an experienced radiologist. Using clinical data and imaging features, we performed univariate and multivariate analyses to identify predictors of clinically significant PCA.

RESULTS

The mean patient age was 66 years, and the mean prostate-specific antigen density (PSAD) was 0.13 ng/mL². PCA was detected in 34 of 96 (35%) lesions, 14 of which (15%) harbored clinically significant PCA. In univariate analysis, DWI score, prostate volume, and PSAD were significant predictors (p < 0.05) of clinically significant PCA with a suggested significance for apparent diffusion coefficient (ADC) and prostate-specific antigen value (p < 0.10). On multivariate analysis, PSAD and lesion ADC were the most important covariates. The combination of both PSAD of 0.15 ng/mL² or greater and an ADC value of less than 1000 mm²/s yielded an AUC of 0.91 for clinically significant PCA (p < 0.001). If FB had been restricted to these criteria, only 10 of 90 men would have undergone biopsy, resulting in diagnosis of clinically significant PCA in 60% with eight men (9%) misdiagnosed (false-negative).

CONCLUSION

The yield of FB in men with PI-RADSv2 category 3 TZ lesions for clinically significant PCA is 15% but significantly improves to 60% (AUC > 0.9) among men with PSAD of 0.15 ng/mL² or greater and lesion ADC value of less than 1000 mm²/s.

[Selected clinically established and scientific techniques of diffusion-weighted MRI. In the context of imaging in oncology]

BACKGROUND

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that was established in the clinical routine primarily for the detection of brain ischemia. In the past 15 years its clinical use has been extended to oncological radiology, as tumor and metastases can be depicted in DWI due to their hypercellular nature.

PRINCIPLES

The basis of DWI is the Stejskal-Tanner experiment. The diffusion properties of tissue can be visualized after acquisition of at least two diffusion-weighted series using echo planar imaging and a specific sequence of gradient pulses.

CLINICAL APPLICATIONS

The use of DWI in prostate MRI was reported to be one of the first established applications that found its way into internationally recognized clinical guidelines of the European Society of Urological Radiology (ESUR) and the prostate imaging reporting and data system (PI-RADS) scale. Due to recently reported high specificity and negative predictive values of 94% and 92%, respectively, its regular use for breast MRI is expected in the near future. Furthermore, DWI can also reliably be used for whole-body imaging in patients with multiple myeloma or for measuring the extent of bone metastases.

OUTLOOK

New techniques in DWI, such as intravoxel incoherent motion imaging, diffusion kurtosis imaging and histogram-based analyses represent promising approaches to achieve a more quantitative evaluation for tumor detection and therapy response.

Multiparametric prostate MRI: focus on T2-weighted imaging and role in staging of prostate cancer

Multiparametric MRI (mpMRI) represents a growing modality for the non-invasive evaluation of prostate cancer (PCa) and is increasingly being used for patients with persistently elevated PSA and prior negative biopsies, for monitoring patients in active surveillance protocols, for preoperative characterization of cancer for surgical planning, and in planning for MRI-targeted biopsy. The focus of this work is twofold. First, we review the key role of T2-weighted imaging (T2WI) in mpMRI, specifically outlining how it is used for anatomic evaluation of the prostate, detection of clinically significant PCa, assessment of extraprostatic extension (EPE), and mimics of PCa on this sequence. We will also discuss optimal technical acquisition parameters for this sequence and recent technical advancements in T2WI. Second, we will delineate the role that mpMRI plays in the staging of PCa and describe the implications of the information that mpMRI can provide in determining the most appropriate management plan for the patient with PCa.