Ultra-high-b-value diffusion-weighted MR imaging for the detection of prostate cancer: evaluation in 201 cases with histopathological correlation

Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models with Tuned Hyperparameters

We developed a novel machine-learning algorithm to augment the clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics in a novel application of machine-learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of 0.82, 0.81 and 0.91 in three separate pathology classifications. Tumor heterogeneity and prediction of the Gleason score were quantified using two feature selection approaches and two separate classifiers with tuned hyperparameters. There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, incorporating a support vector machine (SVM) (with randomized search) and random forest (RF) (with grid search), were utilized to differentiate between non-tumor regions and significant cancer while also predicting the Gleason score. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC images. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92.

Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging

PURPOSE

To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions.

METHODS

A controlled breathing DWI acquisition was first employed at 3 T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC.

RESULTS

Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF.

CONCLUSION

Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800 s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.

Using Ultrahigh b -Value Diffusion-Weighted Imaging to Noninvasively Assess Renal Fibrosis in a Rabbit Model of Renal Artery Stenosis

OBJECTIVE

This study aimed to investigate the feasibility of diffusion-weighted imaging with ultrahigh b values ( ub DWI) for the evaluation of renal fibrosis (RF) induced by renal artery stenosis (RAS) in a rabbit model.

METHODS

Thirty-two rabbits underwent left RAS operation, whereas 8 rabbits received sham surgery. All rabbits underwent ub DWI ( b = 0-4500 s/mm 2 ). The standard apparent diffusion coefficient (ADC st ), molecular diffusion coefficient ( D ), perfusion fraction ( f ), perfusion-related diffusion coefficient ( D *) and ultrahigh apparent diffusion coefficient (ADC uh ) were longitudinally assessed before operation and at weeks 2, 4, and 6 after operation. The degree of interstitial fibrosis and the expression of aquaporin (AQP) 1 and AQP2 were determined through pathological examination.

RESULTS

In the stenotic kidney, the ADC st , D , f , and ADC uh values of the renal parenchyma significantly decreased compared with those at baseline (all P < 0.05), whereas the D * values significantly increased after RAS induction ( P < 0.05). The ADC st , D , D *, and f were weakly to moderately correlated with interstitial fibrosis as well as with the expression of AQP1 and AQP2. Furthermore, the ADC uh negatively correlated with interstitial fibrosis ( ρ = -0.782, P < 0.001) and positively correlated with AQP1 and AQP2 expression ( ρ = 0.794, P < 0.001, and ρ = 0.789, P < 0.001, respectively).

CONCLUSIONS

Diffusion-weighted imaging with ultrahigh b values shows the potential for noninvasive assessment of the progression of RF in rabbits with unilateral RAS. The ADC uh derived from ub DWI could reflect the expression of AQPs in RF.

Multi-parametric MRI for radiotherapy simulation

Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

Acquisition Parameters Influence Diffusion Metrics Effectiveness in Probing Prostate Tumor and Age-Related Microstructure

This study aimed to investigate the Diffusion-Tensor-Imaging (DTI) potential in the detection of microstructural changes in prostate cancer (PCa) in relation to the diffusion weight (b-value) and the associated diffusion length l_D. Thirty-two patients (age range = 50-87 years) with biopsy-proven PCa underwent Diffusion-Weighted-Imaging (DWI) at 3T, using single non-zero b-value or groups of b-values up to b = 2500 s/mm². The DTI maps (mean-diffusivity, MD; fractional-anisotropy, FA; axial and radial diffusivity, D// and D┴), visual quality, and the association between DTI-metrics and Gleason Score (GS) and DTI-metrics and age were discussed in relation to diffusion compartments probed by water molecules at different b-values. DTI-metrics differentiated benign from PCa tissue (p ≤ 0.0005), with the best discriminative power versus GS at b-values ≥ 1500 s/mm², and for b-values range 0-2000 s/mm², when the l_D is comparable to the size of the epithelial compartment. The strongest linear correlations between MD, D//, D┴, and GS were found at b = 2000 s/mm² and for the range 0-2000 s/mm². A positive correlation between DTI parameters and age was found in benign tissue. In conclusion, the use of the b-value range 0-2000 s/mm² and b-value = 2000 s/mm² improves the contrast and discriminative power of DTI with respect to PCa. The sensitivity of DTI parameters to age-related microstructural changes is worth consideration.

Evaluating Biliary Malignancy with Measured and Calculated Ultra-high b-value Diffusion-weighted MR Imaging at 3T

PURPOSE

Although diffusion-weighted imaging (DWI) with ultra-high b-values is reported to be advantageous in the detection of some tumors, its applicability is not yet known in biliary malignancy. Therefore, this study aimed to evaluate the impact of measured b = 1400 s/mm² (M1400) and calculated b = 1400 s/mm² (C1400) DWI on image quality and quality of lesion discernibility using a modern 3T MR system compared to conventional b = 800 s/mm² DWI (M800).

METHODS

We evaluated 56 patients who had pathologically proven biliary malignancy. All the patients underwent preoperative or baseline 3T MRI using DWI (b = 50, 400, 800, and 1400 s/mm²). The calculated DWI was obtained using a conventional DWI set (b = 50, 400, and 800). The tumor-to-bile contrast ratio (CR) and tumor SNR were compared between the different DWI images. Likert scores were given on a 5-point scale to assess the overall image quality, overall artifacts, ghost artifacts, misregistration artifacts, margin sharpness, and lesion discernibility. Repeated-measures analysis of variance with post hoc analyses was used for statistical evaluations.

RESULTS

The CR of the tumor-to-bile was significantly higher in both M1400 and C1400 than in M800 (P^a < 0.01). SNRs were significantly higher in M800, followed by C1400 and M1400 (P^a < 0.01). Lesion discernibility was significantly improved for M1400, followed by C1400 and M800 for both readers (P^a < 0.01).

CONCLUSION

Using a 3T MRI, both measured and calculated DWI with an ultra-high b-value offer superior lesion discernibility for biliary malignancy compared to the conventional DWI.

Evaluation of apparent diffusion coefficient of two-dimensional BLADE turbo gradient- and spin-echo diffusion-weighted imaging with a breast phantom

This study aimed to evaluate the reliability of apparent diffusion coefficient (ADC) values generated with two-dimensional turbo gradient- and spin-echo with BLADE trajectory diffusion-weighted imaging (TGSE-BLADE-DWI) sequence using a breast diffusion phantom. TGSE-BLADE-DWI and single-shot spin-echo echo-planar imaging (SS-EPI-DWI) were performed using a 3.0 T magnetic resonance imaging scanner. Concordance rates of ADC values and the signal-to-noise ratio (SNR) were compared between TGSE-BLADE-DWI and SS-EPI-DWI. TGSE-BLADE-DWI provided a higher concordance rate for ADC values than SS-EPI-DWI when b-values > 2000s/mm² and a slice thickness of 1 mm were used. TGSE-BLADE-DWI showed less image distortion than SS-EPI-DWI. The SNR of TGSE-BLADE-DWI was higher than that of SS-EPI-DWI, except at a number of excitations of 7 and a slice thickness of 1 mm. In conclusion, TGSE-BLADE-DWI can offer a better SNR, less distortion, and more reliable ADC measurements than SS-EPI-DWI in a breast phantom.

A feasibility study of reduced full-of-view synthetic high-b-value diffusion-weighted imaging in uterine tumors

OBJECTIVES

This study aimed to evaluate the feasibility of reduced full-of-view synthetic high-b value diffusion-weighted images (rFOV-syDWIs) in the clinical application of cervical cancer based on image quality and diagnostic efficacy.

METHODS

We retrospectively evaluated the data of 35 patients with cervical cancer and 35 healthy volunteers from May to November 2021. All patients and volunteers underwent rFOV-DWI scans, including a 13b-protocol: b = 0, 25, 50, 75, 100, 150, 200, 400, 600, 800, 1000, 1200, and 1500 s/mm² and a 5b-protocol: b = 0, 100, 400, 800,1500 s/mm². rFOV-syDWIs with b values of 1200 (rFOV-syDWI_b=1200) and 1500 (rFOV-syDWI_b=1500) were generated from two different multiple-b-value image datasets using a mono-exponential fitting algorithm. According to homoscedasticity and normality assessed by the Levene's test and Shapiro-Wilk test, the inter-modality differences of quantitative measurements were, respectively, examined by Wilcoxon signed-rank test or paired t test and the inter-group differences of ADC values were examined by independent t test or Mann-Whitney U test.

RESULTS

A higher inter-reader agreement between SNRs and CNRs was found in 13b-protocol and 5b-protocol rFOV-syDWI_b=1200/1500 compared to 13b-protocol rFOV-sDWI_b=1200/1500 (p < 0.05). AUC of 5b-protocol syADC_{mean,b=1200/1500} and syADC_{minimum,b=1200/1500} was equal or higher than that of 13b-protocol sADC_{mean,b=1200/1500} and sADC_{minimum,b=1200/1500}.

CONCLUSIONS

rFOV-syDWIs provide better lesion clarity and higher image quality than rFOV-sDWIs. 5b-protocol rFOV-syDWIs shorten scan time, and synthetic ADCs offer reliable diagnosis value as scanned 13b-protocol DWIs.

Post-MRI transrectal micro-ultrasonography of transition zone PI-RADS > 2 lesions for biopsy guidance

OBJECTIVE

To prospectively determine the value of post-MRI micro-ultrasonography (microUS) in the diagnosis of transition zone (TZ) significant prostate cancer (sPCa).

PATIENTS AND METHODS

Eighty-four consecutive men (66 ± 6.3 years) with a mean PSA level of 10.2 ± 7.4 ng/mL and at least one TZ-PI-RADS > 2 lesion were included. All patients had MRI-directed microUS and biopsy. Sensitivity and specificity of post-MRI microUS to visualize PI-RADS > 2 TZ lesions, the cancer detection rate of TZ-sPCa, and tumor characteristics according to their visibility on microUS were evaluated. Interreader agreement for detecting microUS+ lesions was evaluated using Cohen's kappa test.

RESULTS

Of the 92 PI-RADS > 2 lesions, 71 (71/92; 77%) were visible on microUS and biopsy was performed without image fusion, which was required for the 21 invisible lesions (21/92; 22.8%). TZ-sPCa detection rate was 51.1% (47/92). Sensitivity and specificity of MRI-directed microUS were 83% (39/47; 95% CI: 69.2-92.4%) and 28.9% (13/45; 95% CI: 16.4-44.3%), on a per-lesion basis and 86.4% (38/45; 95% CI: 72.6-94.8%) and 27.5% (11/40; 95% CI: 14.6-43.9%) on a per-patient basis. Visible tumors on microUS exhibited a larger volume and a lower mean ADC value than non-visible tumors (15.8 ± 5.1 vs. 12.5 ± 3.6 mm and 0.82 ± 1.1 × 10³ vs. 0.9 ± 1.4 × 10^-3 mm²/s) (p = 0.02). Non-visible tumors showed a heterogeneous non-specific echotexture or were masked by the shadowing caused by corpora amylacea. Interreader agreement was almost perfect (kappa = 0.88; 95% CI: 0.79-0.95). The main limitation is the single-center feature of the study.

CONCLUSION

MRI-targeted transrectal microUS is effective to detect TZ-sPCa. TRUS-MRI image fusion helps overcome limitations due to TZ tissue heterogeneity.

KEY POINTS

microUS can visualize the majority of MRI-detected PI-RADS > 2 TZ lesions (sensitivity = 83%). Interreader agreement of MRI-directed microUS in the detection of TZ lesions appears excellent (kappa = 0.88). In 77% of PI-RADS > 2 TZ lesions, biopsy was performed under microUS visual control. MRI fusion system was only used to overcome limitations due to tissue heterogeneity of benign prostatic hyperplasia.

Machine learning prediction of prostate cancer from transrectal ultrasound video clips

Objective

To build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI).

Methods

We systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis.

Results

The AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05).

Conclusion

The prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

Diffusion and quantification of diffusion of prostate cancer

For assessing a cancer treatment, and for detecting and characterizing cancer, Diffusion-weighted imaging (DWI) is commonly used. The key in DWI's use extracranially has been due to the emergence of of high-gradient amplitude and multichannel coils, parallelimaging, and echo-planar imaging. The benefit has been fewer motion artefacts and high-quality prostate images.Recently, new techniques have been developed to improve the signal-to-noise ratio of DWI with fewer artefacts, allowing an increase in spatial resolution. For apparent diffusion coefficient quantification, non-Gaussian diffusion models have been proposed as additional tools for prostate cancer detection and evaluation of its aggressiveness. More recently, radiomics and machine learning for prostate magnetic resonance imaging have emerged as novel techniques for the non-invasive characterisation of prostate cancer. This review presents recent developments in prostate DWI and discusses its potential use in clinical practice.

Synthesizing High-b-Value Diffusion-weighted Imaging of the Prostate Using Generative Adversarial Networks

Purpose

To develop and evaluate a diffusion-weighted imaging (DWI) deep learning framework based on the generative adversarial network (GAN) to generate synthetic high-b-value (b =1500 sec/mm²) DWI (SYN_b1500) sets from acquired standard-b-value (b = 800 sec/mm²) DWI (ACQ_b800) and acquired standard-b-value (b = 1000 sec/mm²) DWI (ACQ_b1000) sets.

Materials and Methods

This retrospective multicenter study included 395 patients who underwent prostate multiparametric MRI. This cohort was split into internal training (96 patients) and external testing (299 patients) datasets. To create SYN_b1500 sets from ACQ_b800 and ACQ_b1000 sets, a deep learning model based on GAN (M₀) was developed by using the internal dataset. M₀ was trained and compared with a conventional model based on the cycle GAN (M_cyc). M₀ was further optimized by using denoising and edge-enhancement techniques (optimized version of the M₀ [Opt-M₀]). The SYN_b1500 sets were synthesized by using the M₀ and the Opt-M₀ were synthesized by using ACQ_b800 and ACQ_b1000 sets from the external testing dataset. For comparison, traditional calculated (b =1500 sec/mm²) DWI (CAL_b1500) sets were also obtained. Reader ratings for image quality and prostate cancer detection were performed on the acquired high-b-value (b = 1500 sec/mm²) DWI (ACQ_b1500), CAL_b1500, and SYN_b1500 sets and the SYN_b1500 set generated by the Opt-M₀ (Opt-SYN_b1500). Wilcoxon signed rank tests were used to compare the readers' scores. A multiple-reader multiple-case receiver operating characteristic curve was used to compare the diagnostic utility of each DWI set.

Results

When compared with the M_cyc, the M₀ yielded a lower mean squared difference and higher mean scores for the peak signal-to-noise ratio, structural similarity, and feature similarity (P < .001 for all). Opt-SYN_b1500 resulted in significantly better image quality (P ≤ .001 for all) and a higher mean area under the curve than ACQ_b1500 and CAL_b1500 (P ≤ .042 for all).

Conclusion

A deep learning framework based on GAN is a promising method to synthesize realistic high-b-value DWI sets with good image quality and accuracy in prostate cancer detection.Keywords: Prostate Cancer, Abdomen/GI, Diffusion-weighted Imaging, Deep Learning Framework, High b Value, Generative Adversarial Networks© RSNA, 2021 Supplemental material is available for this article.

A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI), the use of three multiple imaging sequences, typically T2-weighted, diffusion weighted (DWI) and dynamic contrast enhanced (DCE) images, has a high sensitivity and specificity for detecting significant cancer. Current guidance now recommends its use prior to biopsy. However, the impact of DCE is currently under debate regarding test accuracy. Biparametric MRI (bpMRI), using only T2 and DWI has been proposed as a viable alternative. We conducted a contemporary systematic review and meta-analysis to further examine the diagnostic performance of bpMRI in the diagnosis of any and clinically significant prostate cancer.

METHODS

A systematic review of the literature from 01/01/2017 to 06/07/2019 was performed by two independent reviewers using predefined search criteria. The index test was biparametric MRI and the reference standard whole-mount prostatectomy or prostate biopsy. Quality of included studies was assessed by the QUADAS-2 tool. Statistical analysis included pooled diagnostic performance (sensitivity; specificity; AUC), meta-regression of possible covariates and head-to-head comparisons of bpMRI and mpMRI where both were performed in the same study.

RESULTS

Forty-four articles were included in the analysis. The pooled sensitivity for any cancer detection was 0.84 (95% CI, 0.80-0.88), specificity 0.75 (95% CI, 0.68-0.81) for bpMRI. The summary ROC curve yielded a high AUC value (AUC = 0.86). The pooled sensitivity for clinically significant prostate cancer was 0.87 (95% CI, 0.78-0.93), specificity 0.72 (95% CI, 0.56-0.84) and the AUC value was 0.87. Meta-regression analysis revealed no difference in the pooled diagnostic estimates between bpMRI and mpMRI.

CONCLUSIONS

This meta-analysis on contemporary studies shows that bpMRI offers comparable test accuracies to mpMRI in detecting prostate cancer. These data are broadly supportive of the bpMRI approach but heterogeneity does not allow definitive recommendations to be made. There is a need for prospective multicentre studies of bpMRI in biopsy naïve men.

Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis in chronic kidney disease patients

BACKGROUND

Renal fibrosis is the strongest prognosis predictor of end-stage renal disease (ESRD) in chronic kidney disease (CKD). Diffusion kurtosis imaging (DKI) is a promising method of magnetic resonance imaging (MRI) successfully used to assess renal fibrosis in IgA nephropathy. This study first evaluated the long-term prognostic value of DKI in CKD patients.

METHODS

Forty-two patients with CKD were prospectively enrolled, and underwent DKI on a clinical 3 T MR scanner. We excluded patients with comorbidities that could affect the volume or the components of the kidney. DKI parameters, including mean kurtosis (K), mean diffusivity (D) and apparent diffusion coefficient (ADC) of kidney cortex were obtained by region-of-interest measurement. We followed up these patients for a median of 43 months and investigated the correlations between each DKI parameter and overall renal prognosis.

RESULTS

Both K and ADC values were correlated well with the eGFR on recruitment and the eGFR of the last visit in follow-up (p＜0.001). K and ADC values were also well associated with the eGFR slopes in CKD patients, both with the first-last time point slope (p = 0.011 and p＜0.001, respectively) and with the regression slope (p = 0.010 and p＜0.001, respectively). Cox proportional hazard regression indicated that lower eGFR and ADC values independently predicted eGFR loss of more than 30% and ESRD. The receiver operating characteristic analysis showed that K and ADC values were predictable for renal prognosis, and ADC displayed better capabilities for both ESRD (AUC 0.936, sensitivity 92.31%, specificity 82.76%) and the composite endpoint (eGFR loss>30% or ESRD) (AUC 0.881, sensitivity 66.67%, specificity 96.3%).

CONCLUSIONS

Renal ADC values obtained from DKI showed significant predictive value for the prognosis of CKD patients, which could be a promising noninvasive technique in follow-up.

Diagnostic accuracy of high b-value diffusion weighted imaging for patients with prostate cancer: a diagnostic comprehensive analysis

We performed a meta-analysis to assess the diagnostic accuracy of high b-value diffusion-weighted imaging for patients with prostate cancer. A comprehensive literature search of the PubMed, Excerpta Medica Database, Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disc, and Wanfang databases from January 1, 1995, to April 30, 2021, was conducted. The quality of the retrieved papers was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and their 95% confidence intervals (CIs) were evaluated using bivariate mixed effects models. A total of twenty-four articles matched the selection criteria and were finally included after screening the titles, abstracts, and full texts of 641 initial articles. The pooled sensitivity and specificity (95% CI) were 0.84 (0.80-0.87) and 0.87 (0.81-0.91), respectively. The pooled positive and negative likelihood ratios (95% CI) were 6.4 (4.4-9.3) and 0.19 (0.16-0.23), respectively. The diagnostic odds ratio was 34 (95% CI: 22-51). The area under the summary receiver operator characteristic curve was 0.91 (95% CI: 0.88-0.93). Subgroup analysis presents similar results. The diagnostic accuracy of high b-value diffusion-weighted imaging was similarly high in the qualitative and quantitative evaluation of prostate cancer.

Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

Image quality and diagnostic value of diffusion-weighted breast magnetic resonance imaging: Comparison of acquired and computed images

PURPOSE

To compare the image quality of acquired diffusion-weighted imaging (DWI) and computed DWI and evaluate the lesion detectability and likelihood of malignancy in these datasets.

MATERIALS AND METHODS

This prospective study was approved by our institutional review board. A total of 29 women (mean age, 43.5 years) underwent DWI between August 2018 and April 2019 for 32 breast cancers and 16 benign breast lesions. Three radiologists independently reviewed the acquired DWI with b-values of 1000 and 2000 s/mm2 (A-b1000 and A-b2000) and the computed DWI with a b-value of 2000 s/mm2 (C-b2000). Image quality was scored and compared between the three DWI datasets. Lesion detectability was recorded, and the lesion's likelihood for malignancy was scored using a five-point scale.

RESULTS

The A-b1000 images were superior to the A-b2000 and C-b2000 images in chest distinction, fat suppression, and overall image quality. The A-b2000 and C-b2000 images showed comparable scores for all image quality parameters. C-b2000 showed the highest values for lesion detection among all readers, although there was no statistical difference in sensitivity, specificity, positive predictive value, negative predictive value, and accuracy between the DWI datasets. The malignancy scores of the DWI images were not significantly different among the three readers.

CONCLUSIONS

A-b1000 DWI is suitable for breast lesion evaluations, considering its better image quality and comparable diagnostic values compared to that of A-b2000 and C-b2000 images. The additional use of computed high b-value DWI may have the potential to increase the detectability of breast masses.

Measured Multipoint Ultra-High b-Value Diffusion MRI in the Assessment of MRI-Detected Prostate Lesions

OBJECTIVES

The aim of this study was to assess quantitative ultra-high b-value (UHB) diffusion magnetic resonance imaging (MRI)-derived parameters in comparison to standard clinical apparent diffusion coefficient (SD-ADC-2b-1000, SD-ADC-2b-1500) for the prediction of clinically significant prostate cancer, defined as Gleason Grade Group greater than or equal to 2.

MATERIALS AND METHODS

Seventy-three patients who underwent 3-T prostate MRI with diffusion-weighted imaging acquired at b = 50/500/1000/1500s/mm2 and b = 100/500/1000/1500/2250/3000/4000 s/mm2 were included. Magnetic resonance lesions were segmented manually on individual sequences, then matched to targeted transrectal ultrasonography/MRI fusion biopsies. Monoexponential 2-point and multipoint fits of standard diffusion and of UHB diffusion were calculated with incremental b-values. Furthermore, a kurtosis fit with parameters Dapp and Kapp with incremental b-values was obtained. Each parameter was examined for prediction of clinically significant prostate cancer using bootstrapped receiver operating characteristics and decision curve analysis. Parameter models were compared using Vuong test.

RESULTS

Fifty of 73 men (age, 66 years [interquartile range, 61-72]; prostate-specific antigen, 6.6 ng/mL [interquartile range, 5-9.7]) had 64 MRI-detected lesions. The performance of SD-ADC-2b-1000 (area under the curve, 0.82) and SD-ADC-2b-1500 (area under the curve, 0.82) was not statistically different (P = 0.99), with SD-ADC-2b-1500 selected as reference. Compared with the reference model, none of the 19 tested logistic regression parameter models including multipoint and 2-point UHB-ADC, Dapp, and Kapp with incremental b-values of up to 4000 s/mm2 outperformed SD-ADC-2b-1500 (all P's > 0.05). Decision curve analysis confirmed these results indicating no higher net benefit for UHB parameters in comparison to SD-ADC-2b-1500 in the clinically important range from 3% to 20% of cancer threshold probability. Net reduction analysis showed no reduction of MR lesions requiring biopsy.

CONCLUSIONS

Despite evaluation of a large b-value range and inclusion of 2-point, multipoint, and kurtosis models, none of the parameters provided better predictive performance than standard 2-point ADC measurements using b-values 50/1000 or 50/1500. Our results suggest that most of the diagnostic benefits available in diffusion MRI are already represented in an ADC composed of one low and one 1000 to 1500 s/mm2 b-value.

Can mean ADC value and ADC ratio of benign prostate tissue to prostate cancer assist in the prediction of clinically significant prostate cancer within the PI-RADSv2 scoring system?

Background

The aim of this study is to investigate whether quantitative DW metrics can provide additive value to the reliable categorization of lesions within existing PI-RADSv2 guidelines. Fifty-eight patients with clinically suspicious prostate cancer who underwent PR examination, PSA serum levels, sextant TRUS-guided biopsies, and bi-parametric MR imaging were included in the study.

Results

Sixty-six lesions were detected by histopathological analysis of surgical specimens. The mean ADC values were significantly lower in tumor than non-tumor tissue. The mean ADC value inversely correlated with Gleason score of tumors with a significant p value < 0.001.Conversely, a positive relationship was found between the ADC ratio (ADC of benign prostatic tissue to prostate cancer) and the pathologic Gleason score with a significant elevation of the ADC ratio along with an increase of the pathologic Gleason score (p < 0.001). ROC curves constructed for the tumor ADC and ADC ratio helped to distinguish pathologically aggressive (Gleason score ≥ 7) from non-aggressive (Gleason score ≤ 6) tumors and to correlate it with PIRADSv2 scoring to predict the presence of clinically significant PCA (PIRADSv2 DW ≥ 4). The ability of the tumor ADC and ADC ratio to predict highly aggressive tumors (GS> 7) was high (AUC for ADC and ADC ratio, 0.946 and 0.897; p = 0.014 and 0.039, respectively). The ADC cut-off value for GS ≥ 7 was < 0.7725 and for GS ≤ 6 was > 0.8620 with sensitivity and specificity 97 and 94%. The cutoff ADC ratio for predicting (GS > 7) was 1.42 and for GS ≤ 6 was > 1.320 with sensitivity and specificity 97 and 92%. By applying this ADC ratio cut-off value the sensitivity and specificity of reader 1 for correct categorization of PIRADSv2 DW > 4 increased from 90 and 68% to 95 and 90% and that of reader 2 increased from 94 and 88% to 97 and 92%, respectively.

Conclusion

Estimation of DW metrics (ADC and ADC ratio between benign prostatic tissue and prostate cancer) allow the non-invasive assessment of biological aggressiveness of prostate cancer and allow reliable application of the PIRADSv2 scoring to determine clinically significant cancer (DW score > 4) which may contribute in planning initial treatment strategies.

Fusion US/MRI prostate biopsy using a computer aided diagnostic (CAD) system

BACKGROUND

The aim of this study was to investigate the impact of computer aided diagnostic (CAD) system on the detection rate of prostate cancer (PCa) in a series of fusion prostate biopsy (FPB).

METHODS

Two prospective transperineal FPB series (with or without CAD assistance) were analyzed and PCa detection rates compared with per-patient and per-target analyses. The χ² and Mann-Whitney test were used to compare categorical and continuous variables, respectively. Univariable and multivariable regression analyses were applied to identify predictors of any and clinically significant (cs) PCa detection. Subgroup analyses were performed after stratifying for PI-RADS Score and lesion location.

RESULTS

Out of 183 FPB, 89 were performed with CAD assistance. At per-patient analysis the detection rate of any PCa and of cs PCa were 56.3% and 30.6%, respectively; the aid of CAD was negligible for either any PCa or csPCa detection rates (P=0.45 and P=0.99, respectively). Conversely in a per-target analysis, CAD-assisted biopsy had significantly higher positive predictive value (PPV) for any PCa versus MRI-only group (58% vs. 37.8%, P=0.001). PI-RADS Score was the only independent predictor of any and csPCa, either in per-patient or per-target multivariable regression analysis (all P<0.029). In a subgroup per-patient analysis of anterior/transitional zone lesions, csPCa detection rate was significantly higher in the CAD cohort (54.5%vs.11.1%, respectively; P=0.028), and CAD assistance was the only predictor of csPCa detection (P=0.013).

CONCLUSIONS

CAD assistance for FPB seems to improve detection of csPCa located in anterior/transitional zone. Enhanced identification and improved contouring of lesions may justify higher diagnostic performance.

Radiotherapy for prostate cancer: Effect of gold fiducial markers on diffusion-weighted magnetic resonance imaging

PURPOSE

There has been an increase in the use of gold fiducial markers to ensure precise radiotherapy delivery in prostate cancer patients. However, metal artifacts may affect the quality of subsequent imaging used to assess disease status following treatment. In this study, we evaluated the effect of gold fiducial markers on magnetic resonance imaging (MRI), particularly on diffusion-weighted imaging (DWI).

MATERIAL AND METHODS

Among 57 patients with prostate cancer, 21 patients in whom two gold markers were placed in the prostate tumor with abnormal signal intensity on DWI were evaluated. The effect of the markers on DWI was evaluated on a scale of 1-5, with a high score indicating clinical usefulness. Change inapparent diffusion coefficient (ADC; 10^-3 mm² /s) from before to after marker placement was also evaluated.

RESULTS

The mean effect of the markers on DWI was 4.3 (standard deviation [SD] 1.3, range 2-5) points. The mean change in ADC was 0.045 (SD 0.041, range 0.025-0.089) × 10^-3 mm² /s.

CONCLUSIONS

The gold fiducial markers demonstrated negligible effect on DWI quality. Therefore, gold markers do not affect MRI quality, particularly DWI, and may be used during follow-up in prostate cancer patients.

Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study

OBJECTIVES

We aimed to compare the efficiency of prostate cancer (PCa) detection using a radiomics signature based on advanced zoomed diffusion-weighted imaging and conventional full-field-of-view DWI.

METHODS

A total of 136 patients, including 73 patients with PCa and 63 without PCa, underwent multi-parametric magnetic resonance imaging (mp-MRI). Radiomic features were extracted from prostate lesion areas segmented on full-field-of-view DWI with b-value = 1500 s/mm² (f-DWI_b1500), advanced zoomed DWI images with b-value = 1500 s/mm² (z-DWI_b1500), calculated zoomed DWI with b-value = 2000 s/mm² (z-calDWI_b2000), and apparent diffusion coefficient (ADC) maps derived from both sequences (f-ADC and z-ADC). Single-imaging modality radiomics signature, mp-MRI radiomics signature, and a mixed model based on mp-MRI and clinically independent risk factors were built to predict PCa probability. The diagnostic efficacy and the potential net benefits of each model were evaluated.

RESULTS

Both z-DWI_b1500 and z-calDWI_b2000 had significantly better predictive performance than f-DWIb1500 (z-DWIb1500 vs. f-DWIb1500: p = 0.048; z-calDWIb2000 vs. f-DWIb1500: p = 0.014). z-ADC had a slightly higher area under the curve (AUC) value compared with f-ADC value but was not significantly different (p = 0.127). For predicting the presence of PCa, the AUCs of clinical independent risk factors model, mp-MRI model, and mixed model were 0.81, 0.93, and 0.94 in training sets, and 0.74, 0.92, and 0.93 in validation sets, respectively.

CONCLUSION

Radiomics signatures based on the z-DWI technology had better diagnostic accuracy for PCa than that based on the f-DWI technology. The mixed model was better at diagnosing PCa and guiding clinical interventions for patients with suspected PCa compared with mp-MRI signatures and clinically independent risk factors.

KEY POINTS

• Advanced zoomed DWI technology can improve the diagnostic accuracy of radiomics signatures for PCa. • Radiomics signatures based on z-calDWI_b2000 have the best diagnostic performance among individual imaging modalities. • Compared with the independent clinical risk factors and the mp-MRI model, the mixed model has the best diagnostic efficiency.

Is dynamic contrast enhancement still necessary in multiparametric magnetic resonance for diagnosis of prostate cancer: a systematic review and meta-analysis

Background

The purpose of this study is to systematically review the literatures assessing the value of dynamic contrast enhancement (DCE) in the multiparametric magnetic resonance imaging (mpMRI) for the diagnosis of prostate cancer (PCa).

Methods

We searched Embase, PubMed and Web of science until January 2019 to extract articles exploring the possibilities whether the pre-biopsy biparametric magnetic resonance imaging (bpMRI) can replace the position of mpMRI in the diagnosis of PCa. The sensitivity and specificity of bpMRI were all included. The study quality was assessed by QUADAS-2. Bivariate random effects meta-analyses and a hierarchical summary receiver operating characteristic plot were performed for further study through Revman 5 and Stata12.

Results

After searching, we acquired 752 articles among which 45 studies with 5,217 participants were eligible for inclusion. The positive likelihood ratio for the detection of PCa was 2.40 (95% CI: 1.50–3.80) and the negative likelihood ratio was 0.31 (95% CI: 0.18–0.53). The sensitivity and specificity were 0.77 (95% CI: 0.73–0.81) and 0.81 (95% CI: 0.76–0.85) respectively. Based on our result, pooled specificity demonstrated little difference between bpMRI and mpMRI [bpMRI, 0.81 (95% CI, 0.76–0.85); mpMRI, 0.82 (95% CI, 0.72–0.88); P=0.169]. The sensitivity, however, indicated a significant difference between these two groups [bpMRI, 0.77 (95% CI, 0.73–0.81); mpMRI, 0.84 (95% CI, 0.78–0.89); P=0.001].

Conclusions

bpMRI with high b-value is a sensitive tool for diagnosing PCa. Consistent results were found in multiple subgroup analysis.

Synthetic Apparent Diffusion Coefficient for High b-Value Diffusion-Weighted MRI in Prostate

Purpose

It has been reported that diffusion-weighted imaging (DWI) with ultrahigh b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher Materials and Methods. Fifteen patients (7 malignant and 8 benign) were included in this study retrospectively with the institutional ethical committee approval. All images were acquired at a 3T MR scanner. The ADC values were calculated using a monoexponential model. Synthetic ADC (sADC) for higher b-value increases the diagnostic power of prostate cancer. DWI with higher

Results

No significant difference was observed between actual ADC and sADC for b-value increases the diagnostic power of prostate cancer. DWI with higher p=0.002, paired t-test) in sDWI as compared to DWI. Malignant lesions showed significantly lower sADC as compared to benign lesions (p=0.002, paired t-test) in sDWI as compared to DWI. Malignant lesions showed significantly lower sADC as compared to benign lesions (Discussion/

Conclusion

Our initial investigation suggests that the ADC values corresponding to higher b-value can be computed using log-linear relationship derived from lower b-values (b ≤ 1000). Our method might help clinicians to decide the optimal b-value for prostate lesion identification.b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher

Usefulness of Bi-Parametric Magnetic Resonance Imaging with b=1,800 s/mm² Diffusion-Weighted Imaging for Diagnosing Clinically Significant Prostate Cancer

Purpose

This study was conducted to compare the accuracy of bi-parametric magnetic resonance imaging (bpMRI) with high b-value (b=1,000 s/mm², b1000) diffusion-weighted imaging (DWI) to that of bpMRI with ultra-high b-value (b=1,800 s/mm², b1800) DWI to detect clinically significant prostate cancer (csPCa).

Materials and Methods

A total of 408 patients with suspected PCa were evaluated by bpMRI prior to biopsy. One reader retrospectively reviewed all images for confirmation of Prostate Imaging–Reporting and Data System (PI-RADS) score. Cognitive magnetic resonance/ultrasound fusion target biopsy was done for all visible lesions (PI-RADS 3–5). Systematic biopsy was done for all cases. The csPCa detection rates were compared according to the bpMRI protocol (with/without b1800 DWI) or PI-RADS score. The accuracy of PI-RADS score was estimated using receiver operating characteristics curve. The signal intensity (SI) ratio (visible lesion/surrounding background) was evaluated.

Results

Among 164 men confirmed having PCa, 102 had csPCa (Gleason score≥7). Proportions of PI-RADS score 1–2/3/4/5 without b1800 DWI (n=133) and with b1800 DWI (n=275) were 19.5%/57.9%/15.8%/6.8% and 21.1%/48.7%/22.2%/8.0%, respectively. csPCa detection rates with/without b1800 DWI were 27.6%/19.5% (p=0.048), respectively. Areas under the curve of PI-RADS grading with/without b1800 DWI for csPCa detection were 0.885 and 0.705, respectively. The SI ratio in b1800 DWI was higher than that in b1000 DWI (p<0.001).

Conclusions

Adding b1800 DWI to bpMRI protocol improved the diagnostic accuracy and detection rate of csPCa. The higher SI ratio (lesion/background) in b1800 DWI enabled clearer identification of lesions.

Application evaluation of DCE-MRI combined with quantitative analysis of DWI for the diagnosis of prostate cancer

The present study aimed to investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with quantitative analysis of diffusion weighted imaging (DWI) for the diagnosis of prostate cancer (PCa). A total of 81 patients with prostatic diseases, including PCa (n=44) and benign prostatic hyperplasia (BPH, n=37), were imaged with T1 weighted imaging (T1WI), T2 weighted imaging (T2WI), DWI and DCE-MRI. The blood vessel permeability parameters volume transfer rate constant (K^trans), back flow rate constant (K_ep), extravascular extracellular space volume fraction (V_e), plasma volume fraction (V_p) and apparent diffusion coefficient (ADC) were measured, and compared between the two groups. The efficiency of these tools for the diagnosis of PCa was analyzed by receiver operating characteristic curve analysis. The efficiency of ADC combined with blood vessel permeability parameters in the diagnosis of PCa was analyzed by logistic regression. The correlation between these parameters and the Gleason score was evaluated by Spearman correlation analysis in the PCa group. The results demonstrated that, compared with the BPH group, K^trans, K_ep, V_e and V_p were higher, and ADC was lower in the PCa group (P<0.05). The combination of K_ep and ADC offered the highest diagnosis efficiency [area under the curve (AUC=0.939)]. However, the combination of three parameters did not significantly improve the diagnostic efficiency. A subtle improvement in diagnostic efficiency was observed when four parameters (K^trans + K_ep + V_e + ADC) were combined (AUC=0.940), which was significantly higher than with one parameter. The ADC value of the PCa group was negatively correlated with the primary Gleason pattern, secondary Gleason pattern and the total Gleason score in PCa (r=−0.665, −0.456 and −0.714, respectively; P<0.001). The V_p in the PCa group was slightly negatively correlated with the primary Gleason pattern of PCa (r=−0.385; P<0.05); however, no significant correlation was found with secondary Gleason pattern and the total Gleason score. The present study revealed that the combination of DCE-MRI quantitative analysis and DWI was efficient for PCa diagnosis. This may be because DCE-MRI and DWI can noninvasively detect water motility in tumor tissues and alterations in permeability during tumor neovascularization. The present study demonstrated that K_ep and ADC values may be used as predictive parameters for PCa diagnosis, which may help differentiate benign from malignant prostate lesions.

The evaluation of prostate lesions with IVIM DWI and MR perfusion parameters at 3T MRI

PURPOSE

The purpose of our study was to analyze the difference between IVIM DWI and perfusion parameters of malignant lesions and benign lesions-normal prostate tissue.

METHODS

This prospective study included 31 patients who had multiparametric prostate MRI with IVIM DWI due to elevated prostate-specific antigen level and clinical suspicion between February 2015 and September 2016.

RESULTS

For peripheral zone, the mean values of K^trans, K_ep, iAUC, χ² and f were significantly higher in malignant lesions, and the mean values of Dt were significantly lower in malignant lesions (p 0.00, p 0.02, p 0.00, p 0.02 and p 0.00, respectively). For transitional zone, the mean values of K^trans, V_e, iAUC, χ² and f were significantly higher in malignant lesions, and the mean values of Dp and Dt were significantly lower in malignant lesions (p 0.00, p 0.00, p 0.00, p 0.00, p 0.00, p 0.02 and p 0.00, respectively). For whole prostate gland, the mean values of K^trans, K_ep, V_e, iAUC, χ² and f were significantly higher in malignant lesions, and the mean values of Dp and Dt were significantly lower in malignant lesions (p 0.00, p 0.03, p 0.00, p 0.00, p 0.00, p 0.01, p 0.04 and p 0.00, respectively).

CONCLUSIONS

Restricted diffusion-pseudodiffusion and increased perfusion parameters are important to differentiate prostate cancer from benign pathologies. It is also important to keep in mind that transitional zone and peripheral zone tumors may have different perfusion and diffusion parameters. Future studies are needed to confirm our findings.

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.

Diagnostic Performance of Biparametric MRI for Detection of Prostate Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of this study was to perform a systematic review and meta-analysis to estimate the diagnostic performance of biparametric MRI (bpMRI) for detection of prostate cancer (PCa).

MATERIALS AND METHODS

Two independent reviewers performed a systematic review of the literature published from January 2000 to July 2017 by using predefined search terms. The standard of pathologic reference was established at prostatectomy or prostate biopsy. The numbers of true- and false-positive and true- and false-negative results were extracted. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess the quality of the selected studies. Statistical analysis included pooling of diagnostic accuracy, meta-regression, subgroup analysis, head-to-head comparison, and identification of publication bias.

RESULTS

Thirty-three studies were used for general data pooling. The overall sensitivity was 0.81 (95% CI, 0.76-0.85), and overall specificity was 0.77 (95% CI, 0.69-0.84). As for clinically relevant PCa, bpMRI maintained high diagnostic value (AUC, 0.85; 95% CI, 0.82-0.88). There was no evidence of publication bias (p = 0.67). From head-to-head comparison for detection of PCa, multiparametric MRI (mpMRI) had significantly higher pooled sensitivity (0.85; 95% CI, 0.78-0.93) than did bpMRI (0.80; 95% CI, 0.71-0.90) (p = 0.01). However, the pooled specificity values were not significantly different (mpMRI, 0.77 [95% CI, 0.58-0.95]; bpMRI, 0.80 [95% CI, 0.64-0.96]; p = 0.82).

CONCLUSION

The results of this meta-analysis suggest that bpMRI has high diagnostic accuracy in the detection of PCa and maintains a high detection rate for clinically relevant PCa. However, owing to high heterogeneity among the included studies, caution is needed in applying the results of the meta-analysis.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Electronic supplementary material

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

Comparison between ultra-high and conventional mono b-value DWI for preoperative glioma grading

To compare the efficacy of ultra-high and conventional mono-b-value DWI for glioma grading, in 109 pathologically confirmed glioma patients, ultra-high apparent diffusion coefficient (ADC_uh)was calculated using a tri-exponential mode, distributed diffusion coefficients (DDCs) and α values were calculated using a stretched-exponential model, and conventional ADC values were calculated using a mono-exponential model. The efficacy and reliability of parameters for grading gliomas were investigated using receiver operating characteristic (ROC) curve and intra-class correlation (ICC) analyses, respectively. The ADC_uh values differed (P < 0.001) between low-grade gliomas (LGGs; 0.436 ×10⁻³ mm²/sec) and high-grade gliomas (HGGs; 0.285 × 10⁻³ mm²/sec). DDC, a and various conventional ADC values were smaller in HGGs (all P ≤ 0.001, vs. LGGs). The ADC_uh parameter achieved the highest diagnostic efficacy with an area under curve (AUC) of 0.993, 92.9% sensitivity and 98.8% specificity for glioma grading at a cutoff value of 0.362×10⁻³ mm²/sec. ADC_uh measurement appears to be an easy-to-perform technique with good reproducibility (ICC = 0.9391, P < 0.001). The ADC_uh value based in a tri-exponential model exhibited greater efficacy and reliability than other DWI parameters, making it a promising technique for glioma grading.

Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings

PURPOSE

To examine the usefulness of rich diffusion protocols with high b-values and varying diffusion time for probing microstructure in bone metastases. Analysis techniques including biophysical and mathematical models were compared with the clinical apparent diffusion coefficient (ADC).

METHODS

Four patients were scanned using 13 b-values up to 3,000 s/mm2 and diffusion times ranging 18-52 ms. Data were fitted to mono-exponential ADC, intravoxel incoherent motion (IVIM), Kurtosis and Vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) models. Parameters from the models were compared using correlation plots.

RESULTS

ADC and IVIM did not fit the data well, failing to capture the signal at high b-values. The Kurtosis model best explained the data in many voxels, but in voxels exhibiting a more time-dependent signal, the VERDICT model explained the data best. The ADC correlated significantly (p < 0.004) with the intracellular diffusion coefficient (r = 0.48), intracellular volume fraction (r = -0.21), and perfusion fraction (r = 0.46) parameters from VERDICT, suggesting that these factors all contribute to ADC contrast. The mean kurtosis correlated with the intracellular volume fraction parameter (r = 0.26) from VERDICT, consistent with the hypothesis that kurtosis relates to cellularity, but also correlated weakly with the intracellular diffusion coefficient (r = 0.18) and cell radius (r = 0.16) parameters, suggesting that it may be difficult to attribute physical meaning to kurtosis.

CONCLUSION

Both Kurtosis and VERDICT explained the diffusion signal better than ADC and IVIM, primarily due to poor fitting at high b-values in the latter two models. The Kurtosis and VERDICT models captured information at high b using parameters (Kurtosis or intracellular volume fraction and radius) that do not have a simple relationship with ADC and that may provide additional microstructural information in bone metastases.

Computed diffusion weighted imaging (cDWI) and voxelwise-computed diffusion weighted imaging (vcDWI) for oncologic liver imaging: A pilot study

Objective

Aim of the study was to evaluate the influence of the selection of measured b-values on the precision of cDWI in the upper abdomen as well as on the lesion contrast of PET-positive liver metastases in cDWI and vcDWI.

Methods

We performed a retrospective analysis of 10 patients (4 m, 63.5 ± 12.9 y/o) with PET-positive liver metastases examined in 3 T-PET/MRI with b = 100,600,800,1000 and 1500s/mm². cDWI (cb1000/cb1500) and vcDWI were computed based on following combinations: i) b = 100/600 s/mm², ii) b = 100/800 s/mm², iii) b = 100/1000s/mm², iv) b = 100/600/1000s/mm² v) all measured b-values. Mean signal intensity (SI) and standard deviation (SD) in the liver, spleen, kidney, bone marrow and in liver lesions were acquired. The coefficient of variation (CV = SD/SI), the differences of SI between measured and calculated high b-value images and the lesion contrast (SI lesion/liver) were computed.

Results

With increasing upper measured b-values, the CV in cDWI and vcDWI decreased (CV in the liver in cb1500: 0.42 with b100/600 s/mm² and 0.28 with b100/b1000s/mm²) while the differences of measured and calculated b-value images decreased (in the liver in cb1500: 30.7% with b = 100/600 s/mm², 19.7% with b100/b1000s/mm²). In diffusion-restricted lesions, lesion contrast was at least 1.6 in cb1000 and 1.4 in cb1500, respectively, with an upper measured b-value of b = 800 s/mm² and 2.1 for vcDWI with an upper measured b-value of b = 1000s/mm². Overall, the lesion contrast was superior in cb1500 and vcDWI compared to cb1000 (15% and 11%, respectively).

Conclusion

Measuring higher upper b-values seems to lead to more precise computed high b-value images and a decrease of CV. vcDWI provides a comparable lesion contrast to b = 1500s/mm² and offers additionally the reduction of T2 shine-through effects. For vcDWI, measuring b = 1000s/mm² as upper b-value seems to be necessary to guarantee good lesion visibility in the liver based on our preliminary results.

Ultra-high b-value diffusion-weighted imaging features of the prostatic leiomyoma-case report

Background

Leiomyoma of the prostate is a rare benign tumor arising from smooth muscle fibers. Most cases are incidental findings observed during pathological examinations after resection of the prostate. To the best of our knowledge, only few studies have reported the conventional magnetic resonance imaging (MRI) findings of such tumors; however, no reports have described the ultra-high b-value diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) findings of prostatic leiomyomas.

Case presentation

We report MR imaging characteristics and surgical pathologic findings of a case of prostatic leiomyoma treated by robot-assisted transperitoneal laparoscopic approach. Typical MR features showed a homogeneous lesion with slightly hypointense signal compared to the skeletal muscle on T2-weighted images, and isointense signal relative to the muscle on T1-weighted images with fat suppression, which collectively demonstrate apparent homogeneous enhancement with a non-enhanced envelope. A slightly hyperintense signal compared to the skeletal muscle was observed on ultra-high b-value DWI, and higher ADC values were observed as compared to the prostate cancer.

Conclusions

Prostatic leiomyoma is a benign tumor. This case indicates that MRI features of prostatic leiomyoma are helpful for the differential diagnosis of prostate cancer.

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.

Head-To-Head Comparison Between High- and Standard-b-Value DWI for Detecting Prostate Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of this study was to perform a head-to-head comparison between high-b-value (> 1000 s/mm²) and standard-b-value (800-1000 s/mm²) DWI regarding diagnostic performance in the detection of prostate cancer.

MATERIALS AND METHODS

The MEDLINE and EMBASE databases were searched up to April 1, 2017. The analysis included diagnostic accuracy studies in which high- and standard-b-value DWI were used for prostate cancer detection with histopathologic examination as the reference standard. Methodologic quality was assessed with the revised Quality Assessment of Diagnostic Accuracy Studies tool. Sensitivity and specificity of all studies were calculated and were pooled and plotted in a hierarchic summary ROC plot. Meta-regression and multiple-subgroup analyses were performed to compare the diagnostic performances of high- and standard-b-value DWI.

RESULTS

Eleven studies (789 patients) were included. High-b-value DWI had greater pooled sensitivity (0.80 [95% CI, 0.70-0.87]) (p = 0.03) and specificity (0.92 [95% CI, 0.87-0.95]) (p = 0.01) than standard-b-value DWI (sensitivity, 0.78 [95% CI, 0.66-0.86]); specificity, 0.87 [95% CI, 0.77-0.93] (p < 0.01). Multiple-subgroup analyses showed that specificity was consistently higher for high- than for standard-b-value DWI (p ≤ 0.05). Sensitivity was significantly higher for high- than for standard-b-value DWI only in the following subgroups: peripheral zone only, transition zone only, multiparametric protocol (DWI and T2-weighted imaging), visual assessment of DW images, and per-lesion analysis (p ≤ 0.04).

CONCLUSION

In a head-to-head comparison, high-b-value DWI had significantly better sensitivity and specificity for detection of prostate cancer than did standard-b-value DWI. Multiple-subgroup analyses showed that specificity was consistently superior for high-b-value DWI.

Prostate cancer detection among readers with different degree of experience using ultra-high b-value diffusion-weighted Imaging: Is a non-contrast protocol sufficient to detect significant cancer?

AIM

To evaluate the accuracy of a T2-weighted (T2w) - and a parallel transmit zoomed b = 2000 s/mm2 (b2000) - diffusion-weighted imaging sequence among three readers with different degrees of experience for prostate cancer (Pca) detection.

METHODS

Ninety-three patients with suspected Pca were enrolled. For b2000 a two-dimensional spatially-selective RF pulse using an echo-planar transmit trajectory was applied, and the field of view (FOV) was reduced to one-third. All three readers (Reader A: 7, B 4 and C <1 years of experience in prostate MRI) independently evaluated b2000 with regard to the presence of suspicious lesions that displayed increased signal. The results were compared to histopathology obtained by real-time MR/ultrasound fusion and systematic biopsy.

RESULTS

In 62 patients Pca was confirmed. One significant Pca (Gleason score (GS) 7b) was missed by Reader C. Overall, sensitivity/specificity/positive predictive value/negative predictive value were 90/71/86/79% for Reader A, 87/84/92/76% for Reader B and 85/74/87/72% for Reader C, respectively. Detection rates for significant Pca (GS >7a) were 100/100/94% for Readers A/B/C, respectively. Inter-reader agreement was generally good (Kappa A/B: 0.8; A/C: 0.82; B/C: 0.74).

CONCLUSION

B2000 in combination with a T2w could be useful to detect clinically significant Pca.

KEY POINTS

• Significant prostate cancer using zoomed ultra-high b-value DWI was detected. • Diagnostic performance among readers with different degrees of experience was good. • mp- MRI of the prostate using a comprehensive non-contrast protocol is clinically feasible.

Diffusion weighted imaging of the prostate-principles, application, and advances

This review article aims to provide an overview on the principles of diffusion-weighted magnetic resonance imaging (DW-MRI) and its applications in the imaging of the prostate. DW-MRI with regards to different applications for prostate cancer (PCa) detection and characterization, local staging as well as for active surveillance (AS) and tumor recurrence after radical prostatectomy (RP) will be discussed. Furthermore, advances in DW-MRI techniques like diffusion kurtosis imaging (DKI) will be presented.

Multiparametric magnetic resonance imaging of the prostate-a basic tutorial

Prostate cancer is the second most common cause of cancer related death in the United States and the most commonly diagnosed malignancy in men. In general, prostate cancer is slow growing, though there is a broad spectrum of disease that may be indolent, or aggressive and rapidly progressive. Screening for prostate is controversial and complicated by lack of specificity and over diagnosis of clinically insignificant cancer. Imaging has played a role in diagnosis of prostate cancer, primarily through systemic transrectal ultrasound (TRUS) guided biopsy. While TRUS guided biopsy radically changed prostate cancer diagnosis, it still remains limited by low resolution, poor tissue characterization, relatively low sensitivity and positive predictive value. Advances in multiparametric magnetic resonance imaging (mpMRI) have allowed more accurate detection, localization, and staging as well as aiding in the role of active surveillance (AS). The use of mpMRI for the evaluation of prostate cancer has increased dramatically and this trend is likely to continue as the technique is rapidly improving and its applications expand. The purpose of this article is to review the basic principles of mpMRI of the prostate and its clinical applications, which will be reviewed in greater detail in subsequent chapters of this issue.

[MRI of the prostate]

New clinical and technological advances in the field of magnetic resonance imaging (MRI) and targeted image-guided biopsy techniques have significantly improved the detection, localization and staging as well as active surveillance of prostate cancer in recent years. Multiparametric MRI (mpMRI) is currently the main imaging technique for the detection, characterization and diagnostics of metastasizing prostate cancer and is of high diagnostic importance for local staging within the framework of the detection of prostate cancer.

Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI

PURPOSE

To develop a prediction model to distinguish between transition zone (TZ) cancers and benign prostatic hyperplasia (BPH) on multi-parametric prostate magnetic resonance imaging (mp-MRI).

MATERIALS AND METHODS

This retrospective study enrolled 60 patients with either BPH or TZ cancer, who had undergone 3 T-MRI. We generated ten parameters for T2-weighted images (T2WI), diffusion-weighted images (DWI) and dynamic MRI. Using a t-test and multivariate logistic regression (LR) analysis to evaluate the parameters' accuracy, we developed LR models. We calculated the area under the receiver operating characteristic curve (ROC) of LR models by a leave-one-out cross-validation procedure, and the LR model's performance was compared with radiologists' performance with their opinion and with the Prostate Imaging Reporting and Data System (Pi-RADS v2) score.

RESULTS

Multivariate LR analysis showed that only standardized T2WI signal and mean apparent diffusion coefficient (ADC) maintained their independent values (P < 0.001). The validation analysis showed that the AUC of the final LR model was comparable to that of board-certified radiologists, and superior to that of Pi-RADS scores.

CONCLUSION

A standardized T2WI and mean ADC were independent factors for distinguishing between BPH and TZ cancer. The performance of the LR model was comparable to that of experienced radiologists.

KEY POINTS

• It is difficult to diagnose transition zone (TZ) cancer. • We performed quantitative image analysis in multi-parametric MRI. • Standardized-T2WI and mean-ADC were independent factors for diagnosing TZ cancer. • We developed logistic-regression analysis to diagnose TZ cancer accurately. • The performance of the logistic-regression analysis was higher than PIRADSv2.

Ultrahigh b-values MRI in normal human prostate: Initial research on reproducibility and age-related differences

PURPOSE

To investigate the reproducibility of diffusion-weighted imaging (DWI) with ultrahigh b-values, and analyze the age-related differences in normal prostates.

MATERIALS AND METHODS

In all, 67 healthy participants were divided into three age groups (group A, 15-30 years; group B, 31-50 years; group C, ≥51 years), and underwent DWI scanning twice with 15 b-factors from 0 to 3000 at 3.0T. Triexponential fits were applied to calculate the molecular diffusion coefficient (D), the pseudo-diffusion coefficient (D*), the ultrahigh apparent diffusion coefficient (ADC_uh ), and perfusion fraction (f). The interobserver and short-term interscan reproducibility were evaluated, and the change in these parameters with age were assessed.

RESULTS

The D, ADC_uh , and f values presented good to excellent reproducibility. With increasing age, a trend of increasing D values was observed, with significant difference in both peripheral zone (PZ, P = 0.01) and central gland (CG, P = 0.01) of normal prostate tissue. The f value increased in the CG beginning at 50 years of age while the ADC_uh value decreased in the PZ after 50 years of age; all of them showed significant differences between groups A and C and groups B and C (P = 0.01/0.01).

CONCLUSION

The D, ADC_uh , and f values have good to excellent reproducibility in the normal prostate, and these values change with age. The ultrahigh b-values magnetic resonance imaging (MRI) can provide additional information (ADC_uh ), which is different from the IVIM (intravoxel incoherent motion)-derived parameters.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:801-812.

Diffusion radiomics analysis of intratumoral heterogeneity in a murine prostate cancer model following radiotherapy: Pixelwise correlation with histology

PURPOSE

To investigate the biological meaning of apparent diffusion coefficient (ADC) values in tumors following radiotherapy.

MATERIALS AND METHODS

Five mice bearing TRAMP-C1 tumor were half-irradiated with a dose of 15 Gy. Diffusion-weighted images, using multiple b-values from 0 to 3000 s/mm² , were acquired at 7T on day 6. ADC values calculated by a two-point estimate and monoexponential fitting of signal decay were compared between the irradiated and nonirradiated regions of the tumor. Pixelwise ADC maps were correlated with histological metrics including nuclear counts, nuclear sizes, nuclear spaces, cytoplasmic spaces, and extracellular spaces.

RESULTS

As compared with the nonirradiated region, the irradiated region exhibited significant increases in ADC, extracellular space, and nuclear size, and a significant decrease in nuclear counts (P < 0.001 for all). Optimal ADC to differentiate the irradiated from nonirradiated regions was achieved at a b-value of 800 s/mm² by the two-point method and monoexponential curve fitting. ADC positively correlated with extracellular spaces (r = 0.74) and nuclear sizes (r = 0.72), and negatively correlated with nuclear counts (r = -0.82, P < 0.001 for all).

CONCLUSION

As a radiomic biomarker, ADC maps correlating with histological metrics pixelwise could be a means of evaluating tumor heterogeneity and responses to radiotherapy.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:483-489.

Prostate magnetic resonance imaging for brachytherapists: Diagnosis, imaging pitfalls, and post-therapy assessment

Optimal integration of multiparametric MRI (mp MRI) into prostate brachytherapy practice necessitates an understanding of imaging findings pertinent to prostate cancer detection and staging. This review will summarize prostate cancer imaging findings and tumor staging on mp MRI, including an overview of the Prostate Imaging Reporting and Data System (PIRADS)-structured reporting schema, mp MRI findings observed in the post-therapy setting including cases of post-treatment recurrence, and MRI concepts integral to successful salvage brachytherapy.

The utility of quantitative ADC values for differentiating high-risk from low-risk prostate cancer: a systematic review and meta-analysis

PURPOSE

The purpose of the study is to perform a meta-analysis of studies investigating the diagnostic performance of apparent diffusion coefficient (ADC) values in separating high-risk from low-risk prostate cancer (PCa).

METHODS

MEDLINE and EMBASE databases were searched in December 2015 for studies reporting diagnostic performance of ADC values for discriminating high-risk from low-risk PCa and providing sufficient data to construct 2 × 2 contingency tables. Diagnostic performance was quantitatively pooled using a bivariate random-effects model including subgroup analysis and assessment of study heterogeneity and methodological quality.

RESULTS

13 studies were included, providing 1107 tumor foci in 705 patients. Heterogeneity among studies was moderate (τ² = 0.222). Overall sensitivity was 76.9% (95% CI 68.6-83.6%); overall specificity was 77.0% (95% CI 69.9-82.8%); and summary AUC was 0.67. Inverse correlation between sensitivity and specificity (ρ = -0.58) indicated interstudy heterogeneity was partly due to variation in threshold for test positivity. Primary biases were readers' knowledge of Gleason score during ADC measurement, lack of prespecified ADC thresholds, and lack of prostatectomy as reference in some studies. Higher sensitivity was seen in studies published within the past 2 years and studies not using b value of at least 2000; higher specificity was associated with involvement of one, rather than two, readers measuring ADC. Field strength, coil selection, and advanced diffusion metrics did not significantly impact diagnostic performance.

CONCLUSION

ADC values show moderate accuracy in separating high-risk from low-risk PCa, although important biases may overestimate performance and unexplained sources of heterogeneity likely exist. Further studies using a standardized methodology and addressing identified weaknesses may help guide the use of ADC values for clinical decision-making.