Diffusion-weighted MRI and its role in prostate cancer

Development and benchmarking diffusion magnetic resonance imaging analysis for integration into radiation treatment planning

PURPOSE

The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones.

METHODS

Three software packages were used for generating ADC and FA maps in this study; one tool was developed within a commercial treatment planning system, another by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library FSL (Analysis Group, FMRIB, Oxford, United Kingdom), and an in-house tool developed at the M.D. Anderson Cancer Center. The ADC and FA maps generated by all three packages for 35 subjects were subtracted from one another, and the standard deviation of the images' differences was used to compare the reproducibility. The reproducibility of the ADC maps was compared with the Quantitative Imaging Biomarkers Alliance (QIBA) protocol, while that of the FA maps was compared to data in published literature.

RESULTS

Results show that the discrepancies between the ADC maps calculated for each patient using the three different software algorithms are less than 2% which meets the 3.6% recommended QIBA requirement. Except for a small number of isolated points, the majority of differences in FA maps for each patient produced by the three methods did not exceed 0.02 which is 10 times lower than the differences seen in healthy gray and white matter. The results were also compared to the maps generated by existing MR Imaging consoles and showed that the robustness of console generated ADC and FA maps is largely dependent on the correct application of scaling factors, that only if correctly placed; the differences between the three tested methods and the console generated values were within the recommended QIBA guidelines.

CONCLUSIONS

Cross-comparison difference maps demonstrated that quantitative reproducibility of ADC and FA metrics generated using our tested commercial treatment planning system were comparable to in-house and established tools as benchmarks. This integrated approach facilitates the clinical utility of diffusion imaging in radiation treatment planning workflow.

Comparison of single-shot EPI and multi-shot EPI in prostate DWI at 3.0 T

In prostate MRI, single-shot EPI (ssEPI) DWI still suffers from distortion and blurring. Multi-shot EPI (msEPI) overcomes the drawbacks of ssEPI DWI. The aim of this article was to compare the image quality and diagnostic performance for clinically significant prostate cancer (csPC) between ssEPI DWI and msEPI DWI. This retrospective study included 134 patients with suspected PC who underwent 3.0 T MRI and subsequent MRI-guided biopsy. Three radiologists independently assessed anatomical distortion, prostate edge clarity, and lesion conspicuity score for pathologically confirmed csPC. Lesion apparent diffusion coefficient (ADC) and benign ADC were also calculated. In 17 PC patients who underwent prostatectomy, three radiologists independently assessed eight prostate regions by DWI score in PI-RADS v 2.1. Anatomical distortion and prostate edge clarity were significantly higher in msEPI DWI than in ssEPI DWI in the three readers. Lesion conspicuity score was significantly higher in msEPI DWI than in ssEPI DWI in reader 1 and reader 3. Regarding discrimination ability between PC with GS ≤ 3 + 4 and PC with GS ≥ 4 + 3 using lesion ADC, AUC was comparable between ssEPI DWI and msEPI DWI. For diagnostic performance of csPC using DWI score, AUC was comparable between msEPI DWI and ssEPI DWI in all readers. Compared with ssEPI DWI, msEPI DWI had improved image quality and similar or higher diagnostic performance.

Variability in contrast and apparent diffusion coefficient of kiwifruit used as prostate MRI phantom: 1-week validation

Kiwifruit has been proposed as a phantom for prostate magnetic resonance imaging (MRI) to adjust multiparametric MRI factors. This study evaluated the variability in contrasts and apparent diffusion coefficients (ADCs) via repeated scans of kiwifruits for 1 week. All scans were performed using a 3T MRI system. Six kiwifruits were prepared as phantoms. Each kiwifruit was scanned consecutively for 1 week, and T2-weighted and diffusion-weighted images were acquired. Contrasts between the peripheral placenta (PP) and central placenta (CP), and between the outer pericarp (OP) and CP were calculated. The ADCs of the PP, OP, and CP were also determined. The Friedman test with Scheffé's post hoc comparison was used to assess contrast and ADC variability over 1 week; no significant differences between the contrasts or ADCs were found. Thus, each kiwifruit phantom exhibited low variability when used as a prostate MRI phantom.

Clinical application of single-shot echo-planar diffusion-weighted imaging with compressed SENSE in prostate MRI at 3T: preliminary experience

OBJECTIVES

Image quality (IQ) of diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (ssEPI) suffers from low signal-to-noise ratio (SNR) in high b-value acquisitions. Compressed SENSE (C-SENSE), which combines SENSE with compressed sensing, enables SNR to be improved by reducing noise. The aim of this study was to compare IQ and prostate cancer (PC) detectability between DWI with ssEPI using SENSE (EPIS) and using C-SENSE (EPICS).

MATERIALS AND METHODS

Twenty-five patients with pathologically proven PC underwent multi-parametric magnetic resonance imaging at 3T. DW images acquired with EPIS and EPICS were assessed for the following: lesion conspicuity (LC), SNR, contrast-to-noise ratio (CNR), mean and standard deviation (SD) of apparent diffusion coefficient (ADC) of lesion (lADCm and lADCsd), coefficient of variation of lesion ADC (lADCcv), and mean ADC of benign prostate (bADCm).

RESULTS

LC were comparable between EPIS and EPICS (p > 0.050), and SNR and CNR were significantly higher in EPICS than EPIS (p = 0.001 and p < 0.001). In both EPIS and EPICS, lADCm was significantly lower than bADCm (p < 0.001). In addition, lADCcv was significantly lower in EPICS than in EPIS (p < 0.001).

CONCLUSION

Compared with EPIS, EPICS has improved IQ and comparable diagnostic performance in PC.

Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology

Purpose

Relationships between diffusion‐weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution from extracellular/extravascular water and exchange?

Methods

Monte Carlo simulations were performed within synthetic hepatocytes for varying cell size/diffusivity L/D0, and clinical protocols (single diffusion encoding; maximum b‐value: {1000, 1500, 2000} s/mm²; 5 unique gradient duration/separation pairs; SNR = {∞, 100, 80, 40, 20}), accounting for heterogeneity in (D0,L) and perfusion contamination. Diffusion (D) and kurtosis (K) coefficients were calculated, and relationships between (D0,L) and (D,K) were visualized. Functions mapping (D,K) to (D0,L) were computed to predict unseen (D0,L) values, tested for their ability to classify discrete cell‐size contrasts, and deployed on 9.4T ex vivo MRI‐histology data of fixed mouse livers

Results

Relationships between (D,K) and (D0,L) are complex and depend on the diffusion encoding. Functions mapping D,K to (D0,L) captures salient characteristics of D0(D,K) and L(D,K) dependencies. Mappings are not always accurate, but they enable just under 70% accuracy in a three‐class cell‐size classification task (for SNR = 20, bmax = 1500 s/mm², δ = 20 ms, and Δ = 75 ms). MRI detects cell‐size contrasts in the mouse livers that are confirmed by histology, but overestimates the largest cell sizes.

Conclusion

Salient information about liver cell size and diffusivity may be retrieved from minimal diffusion encodings at fixed diffusion time, in experimental conditions and pathological scenarios for which extracellular, extravascular water and exchange are negligible.

Diffusion-weighted imaging in prostate cancer

Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI (bpMRI), which incorporates only T2-weighted imaging and DWI, compared with mpMRI with dynamic contrast-enhanced MRI. Therefore, further optimization of DWI is essential to achieve clinical application of bpMRI for efficient detection of csPC in patients with elevated PSA levels. Although DWI acquisition is routinely performed using single-shot echo-planar imaging, this method suffers from such as susceptibility artifact and anatomic distortion, which remain to be solved. In this review article, we will outline existing problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.

Quantitative diffusion-weighted imaging and dynamic contrast-enhanced MR imaging for assessment of tumor aggressiveness in prostate cancer at 3T

PURPOSE

To compare diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) for characterization of prostate cancer (PC).

METHODS

104 PC patients who underwent prostate multiparametric MRI at 3T including DWI and DCE-MRI before MRI-guided biopsy or radical prostatectomy. Apparent diffusion coefficient (ADC) with histogram analysis (mean, 0-25th percentile, skewness, and kurtosis), intravoxel incoherent motion model including D and f; stretched exponential model including distributed diffusion coefficient (DDC) and a; and permeability parameters including K^trans, K_ep, and V_e were obtained from a region of interest placed on the dominant tumor of each patient.

RESULTS

ADC_mean, ADC_0-25, D, DDC, and V_e were significantly lower and K_ep was significantly higher in GS ≥ 3 + 4 tumors (n = 89) than in GS = 3 + 3 tumors (n = 15), and also in GS ≥ 4 + 3 tumors (n = 57) than in GS ≤ 3 + 4 tumors (n = 47) (P < 0.001 to P = 0.040). f was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors (P = 0.022), but there was no significant difference between GS = 3 + 3 tumors and GS ≥ 3 + 4 tumors, or between the remaining metrics in both comparisons. In metrics with area under the curve (AUC) >0.80, there was a significant difference in AUC between ADC_0-25 and D, and DDC for separating GS ≤ 3 + 4 tumors from GS ≥ 4 + 3 tumors (P = 0.040 and P = 0.022, respectively). There were no significant differences between metrics with AUC > 0.80 for separating GS = 3 + 3 tumors from GS ≥ 3 + 4 tumors. ADC_0-25 had the highest correlation with Gleason grade (ρ = -0.625, P < 0.001).

CONCLUSIONS

DWI and DCE-MRI showed no apparent clinical superiority of non-Gaussian models or permeability MRI over the mono-exponential model for assessment of tumor aggressiveness in PC.

Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

Background

Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel ¹H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading.

Methods

Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient.

Results

The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01).

Conclusion

The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

Evaluation of Weighted Diffusion Subtraction for Detection of Clinically Significant Prostate Cancer

BACKGROUND

Diffusion-weighted imaging (DWI) is an important method for clinically significant prostate cancer (csPCa) diagnosis; however, the Prostate Imaging-Reporting and Data System (PI-RADS) requires the subjective assessment of "markedly hypointense or not" on apparent diffusion coefficient (ADC) map. We hypothesize that weighted diffusion subtraction (WDS) images, created by weighted subtraction of high and low b-value DWIs, might better show areas of ADC values below a set threshold, thus decreasing the subjectivity of the assessment.

PURPOSE

To evaluate the diagnostic ability of WDS for csPCa by comparing scores based on WDS images (DWI/WDS) with those based on PI-RADS DWI (DWI/ADC).

STUDY TYPE

Retrospective.

SUBJECTS

Eighty-six PCa patients.

FIELD STRENGTH/SEQUENCES

3.0 T; DWI.

ASSESSMENT

Four readers assessed the probability of csPCa in lesions (overall, in the peripheral zone [PZ] and transitional zone [TZ]) using 5-point DWI/ADC and DWI/WDS scores. Prostatectomy specimens were the reference standard. ADC values and contrast between csPCa and normal prostate tissue on ADC maps and WDS images were calculated with reference to the pathological map.

STATISTICAL TESTS

Diagnostic ability was evaluated by Jackknife alternative free-response receiver-operating characteristic curve. Figure of merit (FOM), sensitivity, and positive predictive value (PPV) between the DWI/ADC and DWI/WDS scores were compared using paired t-test. Inter-reader agreement was analyzed using κ statistics, and the significance probability was calculated using the Z statistic. Wilcoxon signed-rank test was used to compare contrast between csPCa and normal prostate tissue on ADC maps and WDS images. A P value <0.05 was considered statistically significant.

RESULTS

FOM and sensitivity of the DWI/WDS scores were significantly better than those of the DWI/ADC scores overall, in the PZ and TZ (FOM: overall, 0.715 vs. 0.783; PZ, 0.756 vs. 0.815; TZ, 0.653 vs. 0.738. Sensitivity: overall, 0.512 vs. 0.607; PZ, 0.485 vs. 0.573; TZ, 0.636 vs. 0.761). For PPV, a statistically significant difference was observed overall (0.727 vs. 0.777). The κ value of DWI/WDS score was significantly higher than that of DWI/ADC score overall and in the PZ (overall, 0.614 vs. 0.792; PZ, 0.609 vs. 0.797). Contrast was significantly higher overall in the PZ and TZ in WDS images (median, 1.26, 1.19, and 1.61) than in ADC maps (0.46, 0.47, and 0.41).

DATA CONCLUSION

WDS images performed better than ADC maps in the diagnosis of csPCa and in inter-reader agreement of the diagnosis.

LEVEL OF EVIDENCE

4 Technical Efficacy Stage: 2.

Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation

The purpose of this study was to develop Bi-parametric Magnetic Resonance Imaging (BP-MRI) based radiomics models for differentiation between benign and malignant prostate lesions, and to cross-vendor validate the generalization ability of the models. The prebiopsy BP-MRI data (T2-Weighted Image [T2WI] and the Apparent Diffusion Coefficient [ADC]) of 459 patients with clinical suspicion of prostate cancer were acquired using two scanners from different vendors. The prostate biopsies are the reference standard for diagnosing benign and malignant prostate lesions. The training set was 168 patients' data from Siemens (Vendor 1), and the inner test set was 70 patients' data from the same vendor. The external test set was 221 patients' data from GE (Vendor 2). The lesion Region of Interest (ROI) was manually delineated by experienced radiologists. A total of 851 radiomics features including shape, first-order statistical, texture, and wavelet features were extracted from ROI in T2WI and ADC, respectively. Two feature-ranking methods (Minimum Redundancy Maximum Relevance [MRMR] and Wilcoxon Rank-Sum Test [WRST]) and three classifiers (Random Forest [RF], Support Vector Machine [SVM], and the Least Absolute Shrinkage and Selection Operator [LASSO] regression) were investigated for their efficacy in building single-parametric radiomics signatures. A biparametric radiomics model was built by combining the optimal single-parametric radiomics signatures. A comprehensive diagnosis model was built by combining the biparametric radiomics model with age and Prostate Specific Antigen (PSA) value using multivariable logistic regression. All models were built in the training set and independently validated in the inner and external test sets, and the performances of models in the diagnosis of benign and malignant prostate lesions were quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). The mean AUCs of the inner and external test sets were calculated for each model. The non-inferiority test was used to test if the AUC of model in external test was not inferior to the AUC of model in inner test. Combining MRMR and LASSO produced the best-performing single-parametric radiomics signatures with the highest mean AUC of 0.673 for T2WI (inner test AUC = 0.729 vs. external test AUC = 0.616, p = 0.569) and the highest mean AUC of 0.810 for ADC (inner test AUC = 0.822 vs. external test AUC = 0.797, p = 0.102). The biparametric radiomics model produced a mean AUC of 0.833 (inner test AUC = 0.867 vs. external test AUC = 0.798, p = 0.051). The comprehensive diagnosis model had an improved mean AUC of 0.911 (inner test AUC = 0.935 vs. external test AUC = 0.886, p = 0.010). The comprehensive diagnosis model for differentiating benign from malignant prostate lesions was accurate and generalizable.

Ultrahypofractionated radiotherapy for localized prostate cancer with simultaneous boost to the dominant intraprostatic lesion: a plan comparison

OBJECTIVE

To compare different stereotactic body techniques-intensity-modulated radiotherapy with photons and protons, applied to radiotherapy of prostatic cancer-with simultaneous integrated boost (SIB) on the dominant intraprostatic lesion (DIL).

METHODS

Ten patients were selected for this planning study. Dosimetric results were compared between volumetric modulated arc therapy, intensity-modulated radiation therapy (IMRT), and intensity-modulated proton therapy both with two (IMPT 2F) and five fields (IMPT 5F) planning while applying the prescription schemes of 7.25 Gy/fraction to the prostate gland and 7.5 Gy/fraction to the DIL in 5 fractions.

RESULTS

Comparison of the coverages of the planning target volumes showed that small differences exist. The IMPT-2F-5F techniques allowed higher doses in the targets; conformal indexes resulted similar; homogeneity was better in the photon techniques (2%-5%). Regarding the organs at risk, all the techniques were able to maintain the dose well below the prescribed constraints: in the rectum, the IMPT-2F-5F and IMRT were more efficient in lowering the intermediate doses; in the bladder, the median dose was significantly better in the case of IMPT (2F-5F). In the urethra, the best sparing was achieved only by IMPT-5F.

CONCLUSIONS

Stereotactic radiotherapy with SIB for localized prostate cancer is feasible with all the investigated techniques. Concerning IMPT, the two-beam technique does not seem to have a greater advantage compared to the standard techniques; the 5-beam technique seems more promising also accounting for the range uncertainty.

Apparent Diffusion Coefficient and Other Preoperative Magnetic Resonance Imaging Features for the Prediction of Positive Surgical Margins in Prostate Cancer Patients Undergoing Radical Prostatectomy

PURPOSE

To investigate the use of apparent diffusion coefficient (ADC) values and other MRI features for predicting positive surgical margins (PSMs) in patients undergoing radical prostatectomy.

MATERIALS AND METHODS

We retrospectively identified 400 consecutive patients who underwent surgery for prostate cancer between January 2015 and June 2016. ADC values of the index lesion and other preoperative magnetic resonance imaging features, including tumor site, laterality, level, Prostate Imaging Reporting and Data System category, European Society of Urogenital Radiology extracapsular extension score, and prostate volume, were assessed. Univariate and multivariable logistic regression were performed. Performance in predicting the occurrence of PSMs was measured using the area under the curve (AUC). AUC differences were evaluated with the DeLong method. The Youden index was calculated to identify the ADC threshold to best discriminate patients with PSMs.

RESULTS

Of the 400 patients, 105 (26.2%) had PSMs after radical prostatectomy. ADC values, Prostate Imaging Reporting and Data System category, extracapsular extension score, tumor site, and laterality were significantly associated with PSMs (P < .001) in univariate analysis. The AUC of the predictive model based on ADC alone was 68.2% (95% confidence interval, 62.2-74.2%) and did not significantly differ from the best multivariable predictive model which combined laterality, and site with ADC to attain an AUC of 70.0% (95% confidence interval, 64.2-75.8%; DeLong P = .318). The ADC threshold that maximized the Youden index was 960.3 µm²/s.

CONCLUSION

ADC values and preoperative magnetic resonance imaging features can help estimate the risk of PSMs after radical prostatectomy.

Fusion of high B-value diffusion-weighted and T2-weighted MR images increases sensitivity for identification of extraprostatic disease in prostate cancer

PURPOSE

To evaluate whether fusion of high b-value diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) increases radiologists' ability to detect pathologic features responsible for upstaging in prostate cancer patients prior to radical prostatectomy (RP).

BASIC PROCEDURES

This was a retrospective study including 103 patients who underwent RP and a prostate MRI performed at 3T. High b-value DWI and T2WI were fused and interpreted by three radiologists with different degrees of experience. Prior to and after fusion, readers answered questionnaires about cancer presence, extraprostatic extension (EPE), seminal vesicle (SV) invasion, lymph node (LN) involvement, and reader confidence. Pathology reports served as the reference standard.

MAIN FINDINGS

High b-value DWI-T2WI fusion increased sensitivity for detection of EPE from 65.6% to 77.4% (p < 0.05), SV invasion from 40.5% to 48.8% (p < 0.05), and LN metastasis by 23.8% to 44.4% (p < 0.05). Readers' confidence significantly improved with the use of fusion imaging. Across all readers, confidence of cancer detection increased by 12.5% (p < 0.05), EPE by 14.7% (p < 0.05), SV invasion by 8.1% (p < 0.05), and LN metastasis by 2.5% (p < 0.05) using Wilcoxon signed rank test.

PRINCIPAL CONCLUSIONS

Fusion overlay of high b-value DWI and T2WI increases sensitivity for detection of extraprostatic disease resulting in upstaging at the time of RP.

Stimulated-echo diffusion-weighted imaging with moderate b values for the detection of prostate cancer

OBJECTIVES

Conventional spin-echo (SE) DWI leads to a fundamental trade-off depending on the b value: high b value provides better lesion contrast-to-noise ratio (CNR) by sacrificing signal-to-noise ratio (SNR), image quality, and quantitative reliability. A stimulated-echo (STE) DWI acquisition is evaluated for high-CNR imaging of prostate cancer while maintaining SNR and reliable apparent diffusion coefficient (ADC) mapping.

METHODS

In this prospective, IRB-approved study, 27 patients with suspected prostate cancer (PCa) were scanned with three DWI sequences (SE b = 800 s/mm², SE b = 1500 s/mm², and STE b = 800 s/mm²) after informed consent was obtained. ROIs were drawn on biopsy-confirmed cancer and non-cancerous tissue to perform quantitative SNR, CNR, and ADC measurements. Qualitative metrics (SNR, CNR, and overall image quality) were evaluated by three experienced radiologists. Metrics were compared pairwise between the three acquisitions using a t test (quantitative metrics) and Wilcoxon rank test (qualitative metrics).

RESULTS

Quantitative measurements showed that STE DWI at b = 800 s/mm² has significantly better SNR compared to SE DWI at b = 1500 s/mm² (p < 0.0001) and comparable CNR to high-b value SE DWI at b = 1500 s/mm² (p < 0.05) in the peripheral zone. Qualitative assessment showed preference to STE b = 800 s/mm² in SNR and SE b = 1500 s/mm² in CNR. The overall image quality and lesion detectability among most readers showed no significant preference between STE b = 800 s/mm² and SE b = 1500 s/mm². Further, STE DWI had similar ADC contrast between lesion and normal tissue as SE DWI at b = 800 s/mm² (p = 0.90).

CONCLUSION

STE DWI has the potential to provide high-SNR, high-CNR imaging of prostate cancer while also enabling reliable ADC mapping.

KEY POINTS

• Quantitative analysis showed that STE DWI at b = 800 s/mm²is able to achieve simultaneously high CNR, high SNR, and reliable ADC mapping, compared to SE b = 800 s/mm²and SE b = 1500 s/mm². • Qualitative assessment by three readers showed that STE DWI at b = 800 s/mm²has significantly higher SNR than SE b = 1500 s/mm². No preference between SE b = 1500 s/mm²and STE b = 800 s/mm²was determined in terms of CNR with no missed lesions were found in both acquisitions. • A single STE DWI acquisition at moderate b value (800-1000 s/mm²) may provide sufficient image quality and quantitative reliability for prostate cancer imaging within a shorter scan time, in place of two DWI acquisitions (one with moderate b value and one with high b value).

Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I2 statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.

Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Histogram Analysis of Apparent Diffusion Coefficient Maps With Standard and Ultrahigh b-value Diffusion-weighted MR Imaging

OBJECTIVE

To compare the diagnostic performance of standard and ultrahigh b-value Diffusion-weighted Imaging (DWI) using volumetric histogram analysis in differentiating transition zone (TZ) cancer from benign prostatic hyperplasia (BPH).

METHODS

57 TZ cancer and 61 BPH patients received standard (1000 s/mm) and ultrahigh b-value (2000 s/mm) DWI. The diagnostic ability of ADC histogram parameters derived from two DWI for differentiating TZ cancer from BPH was determined by receiver operating characteristic curve.

RESULTS

Median, minimum, the 10th, 25th percentile ADC in both ADC1000 and ADC2000 and skewness in ADC2000 had significant differences between TZ cancer and BPH (for all, P < 0.05).The 10th percentile ADC showed highest area under the ROC curve (AUC) in both ADC1000 and ADC2000.The 10th percentile ADC of ADC2000 showed significantly higher AUC than did ADC1000 (P = 0.0385).

CONCLUSIONS

The 10th percentile ADC obtained from ultrahigh b-value DWI performed better for differentiating TZ cancer from BPH.

Deep Learning Role in Early Diagnosis of Prostate Cancer

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.

Basic concepts and applications of functional magnetic resonance imaging for radiotherapy of prostate cancer

Recently, the interest to integrate magnetic resonance imaging (MRI) in radiotherapy for prostate cancer has increased considerably. MRI can contribute in all steps of the radiotherapy workflow from diagnosis, staging, and target definition to treatment follow-up. Of particular interest is the ability of MRI to provide a wide range of functional measures. The complexity of MRI as an imaging modality combined with the growing interest of the application to prostate cancer radiotherapy, emphasize the need for dedicated education within the radiation oncology community. In this context, an overview of the most common as well as a few upcoming functional MR imaging techniques is presented: the basic methodology and measurement is described, the link between the functional measures and the underlying biology is established, and finally relevant applications of functional MRI useful for prostate cancer radiotherapy are given.

Computed Diffusion-Weighted Imaging in Prostate Cancer: Basics, Advantages, Cautions, and Future Prospects

Computed diffusion-weighted MRI is a recently proposed post-processing technique that produces b-value images from diffusion-weighted imaging (DWI), acquired using at least two different b-values. This article presents an argument for computed DWI for prostate cancer by viewing four aspects of DWI: fundamentals, image quality and diagnostic performance, computing procedures, and future uses.

Apparent diffusion coefficient in the analysis of prostate cancer: determination of optimal b-value pair to differentiate normal from malignant tissue

PURPOSE

Determining optimal b-value pair for differentiation between normal and prostate cancer (PCa) tissues.

METHODS

Forty-three patients with diagnosis or PCa symptoms were included. Apparent diffusion coefficient (ADC) was estimated using minimum and maximum b-values of 0, 50, 100, 150, 200, 500s/mm2 and 500, 800, 1100, 1400, 1700 and 2000s/mm2, respectively. Diagnostic performances were evaluated when Area-under-the-curve (AUC)>95%.

RESULTS

15 of the 35 b-values pair surpassed this AUC threshold. The pair (50, 2000s/mm2) provided the highest AUC (96%) with ADC cutoff 0.89×10-³mm²/s, sensitivity 95.5%, specificity 93.2% and accuracy 94.4%.

CONCLUSIONS

The best b-value pair was b=50, 2000s/mm2.

Assessment of prostate cancer aggressiveness using apparent diffusion coefficient values: impact of patient race and age

PURPOSE

To assess the impact of patient race and age on the performance of apparent diffusion coefficient (ADC) values for assessment of prostate cancer aggressiveness.

MATERIALS AND METHODS

457 prostate cancer patients who underwent 3T phased-array coil prostate MRI including diffusion-weighted imaging (DWI; maximal b-value 1000 s/mm²) before prostatectomy were included. Mean ADC of a single dominant lesion was measured in each patient, using histopathologic findings from the prostatectomy specimen as reference. In subsets defined by race and age, ADC values were compared between Gleason score (GS) ≤ 3 + 4 and GS ≥ 4 + 3 tumors.

RESULTS

81% of patients were Caucasian, 12% African-American, 7% Asian-American. 13% were <55 years, 42% 55-64 years, 41% 65-74 years, and 4% ≥75 years. 63% were GS ≤ 3 + 4, 37% GS ≥ 4 + 3. ADC was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors in the entire cohort, as well as in Caucasian, African-American, and all four age groups (P ≤ 0.015). AUC for differentiation of GS ≤ 3 + 4 and GS ≥ 4 + 3 as well as optimal ADC threshold was Caucasian: 0.73/≤848; African-American: 0.76/≤780; Asian-American: 0.66/≤839: <55 years, 0.73/≤830; 55-64 years, 0.71/≤800; 65-74 years, 0.74/≤872; ≥75 years, 0.79/≤880. A race-optimized ADC threshold resulted in higher specificity in African-American than Caucasian men (84.9% vs. 67.1%, P = 0.045); age-optimized ADC threshold resulted in higher sensitivity in patients aged ≥75 years than <55 years or 55-64 years (100.0% vs. 53.6%-73.3%; P < 0.001).

CONCLUSION

Patients' race and age may impact the diagnostic performance and optimal threshold when applying ADC values for evaluation of prostate cancer aggressiveness.

Prostate Cancer: Diffusion-weighted MR Imaging for Detection and Assessment of Aggressiveness-Comparison between Conventional and Kurtosis Models

Purpose To compare standard diffusion-weighted (DW) imaging and diffusion kurtosis (DK) imaging for prostate cancer (PC) detection and characterization in a large patient cohort, with attention to the potential added value of DK imaging. Materials and Methods This retrospective institutional review board-approved study received a waiver of informed consent. Two hundred eighty-five patients with PC underwent 3.0-T phased-array coil prostate magnetic resonance (MR) imaging, including a DK imaging sequence (b values 0, 500, 1000, 1500, and 2000 sec/mm²) before prostatectomy. Maps of apparent diffusion coefficient (ADC) and diffusional kurtosis (K) were derived by using maximal b values of 1000 and 2000 sec/mm², respectively. Mean ADC and K were obtained from volumes of interest (VOIs) placed on each patient's dominant tumor and benign prostate tissue. Metrics were compared between benign and malignant tissue, between Gleason score (GS) ≤ 3 + 3 and GS ≥ 3 + 4 tumors, and between GS ≤ 3 + 4 and GS ≥ 4 + 3 tumors by using paired t tests, analysis of variance, receiver operating characteristic (ROC) analysis, and exact tests. Results ADC and K showed significant differences for benign versus tumor tissues, GS ≤ 3 + 3 versus GS ≥ 3 + 4 tumors, and GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (P < .001 for all). ADC and K were highly correlated (r = -0.82; P < .001). Area under the ROC curve was significantly higher (P = .002) for ADC (0.921) than for K (0.902) for benign versus malignant tissue but was similar for GS ≤ 3 + 3 versus GS ≥ 3 + 4 tumors (0.715-0.744) and GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (0.694-0.720) (P > .15). ADC and K were concordant for these various outcomes in 80.0%-88.6% of patients; among patients with discordant results, ADC showed better performance than K for GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (P = .016) and was similar to K for other outcomes (P > .136). Conclusion ADC and K were highly correlated, had similar diagnostic performance, and were concordant for the various outcomes in the large majority of cases. These observations did not show a clear added value of DK imaging compared with standard DW imaging for clinical PC evaluation. ^© RSNA, 2017 Online supplemental material is available for this article.

Diffusion imaging of the vertebral bone marrow

Diffusion-weighted MRI (DWI) of the vertebral bone marrow is a clinically important tool for the characterization of bone-marrow pathologies and, in particular, for the differentiation of benign (osteoporotic) and malignant vertebral compression fractures. DWI of the vertebral bone marrow is, however, complicated by some unique MR and tissue properties of vertebral bone marrow. Due to both the spongy microstructure of the trabecular bone and the proximity of the lungs, soft tissue, or large vessels, substantial magnetic susceptibility variations occur, which severely reduce the magnetic field homogeneity as well as the transverse relaxation time T^*₂ , and thus complicate MRI in particular with echoplanar imaging (EPI) techniques. Therefore, alternative diffusion-weighting pulse sequence types such as single-shot fast-spin-echo sequences or segmented EPI techniques became important alternatives for quantitative DWI of the vertebral bone marrow. This review first describes pulse sequence types that are particularly important for DWI of the vertebral bone marrow. Then, data from 24 studies that made diffusion measurements of normal vertebral bone marrow are reviewed; summarizing all results, the apparent diffusion coefficient (ADC) of normal vertebral bone marrow is typically found to be between 0.2 and 0.6 × 10^-3  mm² /s. Finally, DWI of vertebral compression fractures is discussed. Numerous studies demonstrate significantly greater ADCs in osteoporotic fractures (typically between 1.2 and 2.0 × 10^-3  mm² /s) than in malignant fractures or lesions (typically 0.7-1.3 × 10^-3  mm² /s). Alternatively, several studies used the (qualitative) image contrast of diffusion-weighted acquisitions for differentiation of lesion etiology: a very good lesion differentiation can be achieved, particularly with diffusion-weighted steady-state free precession sequences, which depict malignant lesions as hyperintense relative to normal-appearing vertebral bone marrow, in contrast to hypointense or isointense osteoporotic lesions. Copyright © 2015 John Wiley & Sons, Ltd.

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

BACKGROUND AND AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

A comprehensive non-invasive framework for diagnosing prostate cancer

Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.

T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results

PURPOSE

To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers.

MATERIALS AND METHODS

3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (K^trans and V_e) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically.

RESULTS

ASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with K^trans and V_e. GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, K^trans, and V_e. The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets.

CONCLUSION

T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers.

KEY POINTS

• T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

Incremental value of high b value diffusion-weighted magnetic resonance imaging at 3-T for prediction of extracapsular extension in patients with prostate cancer: preliminary experience

PURPOSE

To investigate whether high b value diffusion-weighted imaging (DWI) contributes to the improvement of diagnostic ability of extracapsular extension (ECE) in prostate cancer (PC).

MATERIALS AND METHODS

Forty-three patients with PC underwent multiparametric MRI including DWI (b values: 0, 2000 s/mm²) at 3-T. Two radiologists assessed the presence of ECE and the diagnostic certainty degree using conventional diagnostic method by consensus. Tumor apparent diffusion coefficient (ADC, ×10^-3 mm²/s) was also measured. Independent predictors of ECE were identified among PSA, tumor ADC, Gleason score, and conventional MRI. ECE in patients with low diagnostic certainty by conventional MRI was further reevaluated using ADC cutoff value, and the results were combined with those of patients with high diagnostic certainty by conventional MRI (MRI + ADC method).

RESULTS

Tumor ADC was an independent predictor of ECE, and the ADC cutoff value was 0.72. The sensitivity, specificity, and accuracy of conventional MRI and MRI + ADC method in the diagnosis of ECE were 44, 92, and 72%, and 78, 96, and 88%, respectively. Among MRI findings leading to the judgement of low diagnostic certainty, broad tumor contact was most common (72% of the patients).

CONCLUSIONS

The addition of ADC obtained with high b value DWI at 3-T to conventional MRI improved the diagnostic ability of ECE.

Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool in imaging-based prostate cancer screening and detection. Endorectal coils are commonly used in DW-MR imaging to improve the signal-to-noise ratio (SNR) of the acquisition, at the expense of significant intensity inhomogeneities (bias field) that worsens as we move away from the endorectal coil. The presence of bias field can have a significant negative impact on the accuracy of different image analysis tasks, as well as prostate tumor localization, thus leading to increased inter- and intra-observer variability. Retrospective bias correction approaches are introduced as a more efficient way of bias correction compared to the prospective methods such that they correct for both of the scanner and anatomy-related bias fields in MR imaging. Previously proposed retrospective bias field correction methods suffer from undesired noise amplification that can reduce the quality of bias-corrected DW-MR image. Here, we propose a unified data reconstruction approach that enables joint compensation of bias field as well as data noise in DW-MR imaging. The proposed noise-compensated, bias-corrected (NCBC) data reconstruction method takes advantage of a novel stochastically fully connected joint conditional random field (SFC-JCRF) model to mitigate the effects of data noise and bias field in the reconstructed MR data. The proposed NCBC reconstruction method was tested on synthetic DW-MR data, physical DW-phantom as well as real DW-MR data all acquired using endorectal MR coil. Both qualitative and quantitative analysis illustrated that the proposed NCBC method can achieve improved image quality when compared to other tested bias correction methods. As such, the proposed NCBC method may have potential as a useful retrospective approach for improving the consistency of image interpretations.

Correlation between apparent diffusion coefficient value on diffusion-weighted MR imaging and Gleason score in prostate cancer

OBJECTIVES

To investigate whether diffusion-weighted imaging (DWI) apparent diffusion coefficient (ADC) correlates with prostate cancer aggressiveness and further to compare the diagnostic performance of ADC and normalized ADC (nADC: normalized to non-tumor tissue).

PATIENTS AND METHODS

Thirty pre-treatment patients (mean age, 69years; range: 59-78years) with prostate cancer underwent magnetic resonance imaging (MRI) examination, including DWI with three b values: 50, 400, and 800s/mm². Both ADC and nADC were correlated with the Gleason score obtained through transrectal ultrasound-guided biopsy.

RESULTS

The tumor minimum ADC (ADC_min: the lowest ADC value within tumor) had an inverse correlation with the Gleason score (r=-0.43, P<0.05), and it was lower in patients with Gleason score 3+4 than in those with Gleason score 3+3 (0.54±0.11×10³mm²/s vs. 0.64±0.12×10^-3mm²/s, P<0.05). Both the nADC_min and nADC_mean correlated with the Gleason score (r=-0.52 and r=-0.55, P<0.01; respectively), and they were lower in patients with Gleason score 3+4 than those with Gleason score 3+3 (P<0.01; respectively). Receiver operating characteristic (ROC) analysis showed that the area under the ROC curve was 0.765, 0.818, or 0.833 for the ADC_min, nADC_min, or nADC_mean; respectively, in differentiating between Gleason score 3+4 and 3+3 tumors.

CONCLUSION

Tumor ADC_min, nADC_min, and nADC_mean are useful markers to predict the aggressiveness of prostate cancer.

Optimal high b-value for diffusion weighted MRI in diagnosing high risk prostate cancers in the peripheral zone

PURPOSE

To retrospectively determine the optimal b-value(s) of diffusion-weighted imaging (DWI) associated with intermediate-high risk cancer in the peripheral zone (PZ) of the prostate.

MATERIALS AND METHODS

Forty-two consecutive patients underwent multi b-value (16 evenly spaced b-values between 0 and 2000 s/mm² ) DWI along with multi-parametric MRI (MP-MRI) of the prostate at 3 Tesla followed by trans-rectal ultrasound/MRI fusion guided targeted biopsy of suspicious lesions detected at MP-MRI. Computed DWI images up to a simulated b-value of 4000 s/mm² were also obtained using a pair of b-values (b = 133 and 400 or 667 or 933 s/mm² ) from the multi b-value DWI. The contrast ratio of average intensity of the targeted lesions and the background PZ was determined. Receiver operator characteristic curves and the area under the curve (AUCs) were obtained for separating patients eligible for active surveillance with low risk prostate cancers from intermediate-high risk prostate cancers as per the cancer of the prostate risk assessment (CAPRA) scoring system.

RESULTS

The AUC first increased then decreased with the increase in b-values reaching maximum at b = 1600 s/mm² (0.74) with no statistically significant different AUC of DWI with b-values 1067-2000 s/mm² . The AUC of computed DWI increased then decreased with the increase in b-values reaching a maximum of 0.75 around b = 2000 s/mm² . There was no statistically significant difference between the AUC of optimal acquired DWI and either of optimal computed DWI.

CONCLUSION

The optimal b-value for acquired DWI in differentiating intermediate-high from low risk prostate cancers in the PZ is b = 1600 s/mm² . The computed DWI has similar performance as that of acquired DWI with the optimal performance around b = 2000 s/mm² .

LEVEL OF EVIDENCE

4 J. Magn. Reson. Imaging 2017;45:125-131.

Value of preoperative 3T multiparametric MRI for surgical margin status in patients with prostate cancer

PURPOSE

To examine the value of preoperative multiparametric magnetic resonance imaging (MRI) as a predictor of surgical margin (SM) status in patients with prostate cancer (PC).

MATERIALS AND METHODS

The Institutional Review Board approved this retrospective study; the requirement for informed consent was waived. Fifty-six male patients with histologically proven PC underwent preoperative 3T multiparametric MRI including high b value (0, 2000 s/mm(2) ) diffusion-weighted imaging. In each patient, clinical data, such as biopsy Gleason score and D'Amico clinical risk score, and multiparametric MRI findings, such as tumor location, tumor size, tumor extension in the apical or proximal region, tumor apparent diffusion coefficient (ADC), and the presence or absence of MRI findings of extracapsular extension (ECE) were evaluated. Statistical evaluations included the Fisher's exact test, χ(2) test, Mann-Whitney U-test, and logistic regression analysis.

RESULTS

On histopathological evaluation, 15 patients (27%) were SM-positive (SMP group), and 41 (73%) were SM-negative (SMN group). The tumor ADC was significantly lower in the SMP group than in the SMN group (P = 0.001). The frequency of tumor extension in the apex or base and suspected ECE on MRI were significantly higher in the SMP group than in the SMN group (P = 0.037 and 0.011, respectively). On multivariate analysis, tumor ADC was the only predictor of SM status in PC (P = 0.003).

CONCLUSION

PC with positive SM was characterized by tumor extension in the apical and proximal regions, lower tumor ADC, and tumors with positive MRI findings of ECE, compared to tumors with negative SM. J. Magn. Reson. Imaging 2016;44:584-593.

Oncological outcomes following robotic-assisted radical prostatectomy in a multiracial Asian population

This study evaluates the oncological outcomes of RARP in a multiracial Asian population from a single institution. All suitable patients from 1st January 2003-30th June 2013 were identified from a prospectively maintained cancer registry. Peri-operative and oncological outcomes were analysed. Significance was defined as p < 0.05. There were n = 725 patients identified with a mean follow-up duration 28 months. The mean operative time, EBL and LOS were 186 min, 215 ml and 3 days, respectively. The pathological stage was pT2 in 68.6% (n = 497/725), pT3 in 31.3% (n = 227/725) and n = 1 patient with pT4 disease. The pathological Gleason scores (GS) were 6 in 27.9% (n = 202/725), GS 7 in 63.6% (n = 461/725) and GS ≥ 8 in 8.0 % (n = 58/725). The node positivity rate was 5.8% (n = 21/360). The positive margin rates were 31.0% (n = 154/497) and 70.9% (n = 161/227) for pT2 and pT3, respectively, and decreasing PSM rates are observed with surgical maturity. The biochemical recurrence rates were 9.7% (n = 48/497) and 34.2% (n = 78/228) for pT2 and pT3/T4, respectively. On multivariate analysis, independent predictors of BCR were pathological T stage and pathological Gleason score. Post-operatively, 78.5% (n = 569/725) of patients had no complications and 17.7% (n = 128/725) had minor (Clavien grade I-II) complications. This series, representing the largest from Southeast Asia, suggests that RARP can be a safe and oncologically feasible treatment for localised prostate cancer in an institution with moderate workload.

Multiparametric magnetic resonance imaging for prostate cancer: A review and update for urologists

Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mpMRI). mpMRI consists of T2-weighted sequences combined with several functional sequences including diffusion-weighted imaging, dynamic contrast-enhanced imaging, and/or magnetic resonance spectroscopy imaging. Interest has been growing in mpMRI because no single MRI sequence adequately detects and characterizes prostate cancer. During the last decade, the role of mpMRI has been expanded in prostate cancer detection, staging, and targeting or guiding prostate biopsy. Recently, mpMRI has been used to assess prostate cancer aggressiveness and to identify anteriorly located tumors before and during active surveillance. Moreover, recent studies have reported that mpMRI is a reliable imaging modality for detecting local recurrence after radical prostatectomy or external beam radiation therapy. In this regard, some urologic clinical practice guidelines recommended the use of mpMRI in the diagnosis and management of prostate cancer. Because mpMRI is the evolving reference standard imaging modality for prostate cancer, urologists should acquire cutting-edge knowledge about mpMRI. In this article, we review the literature on the use of mpMRI in urologic practice and provide a brief description of techniques. More specifically, we state the role of mpMRI in prostate biopsy, active surveillance, high-risk prostate cancer, and detection of recurrence after radical prostatectomy.

High b value (2,000 s/mm2) diffusion-weighted magnetic resonance imaging in prostate cancer at 3 Tesla: comparison with 1,000 s/mm2 for tumor conspicuity and discrimination of aggressiveness

Objective

The objective of our study was to investigate tumor conspicuity and the discrimination potential for tumor aggressiveness on diffusion-weighted magnetic resonance imaging (DW-MRI) with high b value at 3-T.

Materials and Methods

The institutional review board approved this study and waived the requirement for informed consent. A total of 50 patients with prostate cancer (69 cancer foci; 48 in the PZ, 20 in the TZ, and one in whole prostate) who underwent multiparametric prostate MRI including DW-MRI (b values: 0, 1000 s/mm² and 0, 2000 s/mm²) on a 3-T system were included. Lesion conspicuity score (LCS) using visual assessment (1 = invisible for surrounding normal site; 2 = slightly high intensity; 3 = moderately high; and 4 = very high) and tumor-normal signal intensity ratio (TNR) were assessed, and apparent diffusion coefficient (ADC, ×10⁻³ mm²/s) of the tumor regions and normal regions were measured.

Results

Mean LCS and TNR at 0, 2000 s/mm² was significantly higher than those at 0, 1000 s/mm² (p<0.001 for both). In addition, ADC at both 0, 1000 and 0, 2000 s/mm² was found to distinguish intermediate or high risk cancer with Gleason score ≥7 from low risk cancer with Gleason score ≤6 (p<0.001 for both). Furthermore, ADC of tumor regions correlated with Gleason score at both 0, 1000 s/mm² (ρ = −0.602; p<0.001) and 0, 2000 s/mm² (ρ = −0.645; p<0.001).

Conclusions

For tumor conspicuity and characterization of prostate cancer on DW-MRI of 3-T MRI, b = 0, 2000 s/mm² is more useful than b = 0, 1000 s/mm².