Value of Diffusion-Weighted Imaging for the Prediction of Prostate Cancer Location at 3T Using a Phased-Array Coil: Preliminary Results

Multispecies comparative prostate anatomy by imaging: Implications for experimental models of prostatic disease

BACKGROUND

There is an increasing interest in using preclinical models for development and assessment of medical devices and imaging techniques for prostatic disease care. Still, a comprehensive assessment of the prostate's radiological anatomy in primary preclinical models such as dogs, rabbits, and mice utilizing human anatomy as a reference point remains necessary with no optimal model for each purpose being clearly defined in the literature. Therefore, this study compares the anatomical characteristics of different animal models to the human prostatic gland from the imaging perspective.

METHODS

We imaged five Beagle laboratory dogs, five New Zealand White rabbits, and five mice, all sexually mature males, under Institutional Animal Care and Use Committee (IACUC) approval. Ultrasonography (US) was performed using the Vevo® F2 for mice (57 MHz probe). Rabbits and dogs were imaged using the Siemens® Acuson S3000 (17 MHz probe) and endocavitary (8 MHz) probes, respectively. Magnetic resonance imaging (MRI) was also conducted with a 7T scanner in mice and 3T scanner in rabbits and dogs.

RESULTS

Canine transrectal US emerged as the optimal method for US imaging, depicting a morphologically similar gland to humans but lacking echoic zonal differentiation. MRI findings in canines indicated a homogeneously structured gland similar to the human peripheral zone on T2-weighted images (T2W) and apparent diffusion coefficient (ADC). In rabbits, US imaging faced challenges due to the pubic symphysis, whereas MRI effectively visualized all structures with the prostate presenting a similar aspect to the human peripheral gland on T2W and ADC maps. Murine prostate assessment revealed poor visualization of the prostate glands in ultrasound due to its small size, while 7T MRI delineated the distinct prostates and its lobes, with the lateral and dorsal prostate resembling the peripheral zone and the anterior prostate the central zone of the human gland.

CONCLUSION

Dogs stand out as superior models for advanced preclinical studies in prostatic disease research. However, mice present as a good model for early stage studies and rabbits are a cost-effective alternative and serve as valuable tools in specific research domains when canine research is not feasible.

Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients' demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

Cross-Modality Image Registration Using a Training-Time Privileged Third Modality

In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusion-weighted scans with high b-value (DWI [Formula: see text]). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI [Formula: see text]) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWI [Formula: see text], to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI [Formula: see text] and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI [Formula: see text] and T2w in this challenging application.

Head-to-head comparison of biparametric versus multiparametric MRI of the prostate before robot-assisted transperineal fusion prostate biopsy

Purpose

Prostate biparametric magnetic resonance imaging (bpMRI) including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) might be an alternative to multiparametric MRI (mpMRI, including dynamic contrast imaging, DCE) to detect and guide targeted biopsy in patients with suspected prostate cancer (PCa). However, there is no upgrading peripheral zone PI-RADS 3 to PI-RADS 4 without DCE in bpMRI. The aim of this study was to evaluate bpMRI against mpMRI in biopsy-naïve men with elevated prostate-specific antigen (PSA) scheduled for robot-assisted-transperineal fusion-prostate biopsy (RA-TB).

Methods

Retrospective single-center-study of 563 biopsy-naïve men (from 01/2015 to 09/2018, mean PSA 9.7 ± 6.5 ng/mL) with PI-RADSv2.1 conform mpMRI at 3 T before RA-TB. Clinically significant prostate cancer (csPCa) was defined as ISUP grade ≥ 2 in any core. Two experienced readers independently evaluated images according to PI-RADSv2.1 criteria (separate readings for bpMRI and mpMRI sequences, 6-month interval). Reference standard was histology from RA-TB.

Results

PI-RADS 2 was scored in 5.1% of cases (3.4% cancer/3.4% csPCa), PI-RADS 3 in 16.9% (32.6%/3.2%), PI-RADS 4 in 57.6% (66.1%/58.3%) and PI-RADS 5 in 20.4% of cases (79.1%/74.8%). For mpMRI/bpMRI test comparison, sensitivity was 99.0%/97.1% (p < 0.001), specificity 47.5%/61.2% (p < 0.001), PPV 69.5%/75.1% (p < 0.001) and NPV 97.6%/94.6% (n.s.). csPCa was considered gold standard. 35 cases without cancer were upgraded to PI-RADS 4 (mpMRI) and six PI-RADS 3 cases with csPCa were not upgraded (bpMRI).

Conclusion

In patients planned for RA-TB with elevated PSA and clinical suspicion for PCa, specificity was higher in bpMRI vs. mpMRI, which could solve constrains regarding time and contrast agent.

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

Background

Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.

Methods

This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.

Results

According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.

Conclusions

This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models

False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer—true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)—false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular−extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases.

Relationship between Apparent Diffusion Coefficient Distribution and Cancer Grade in Prostate Cancer and Benign Prostatic Hyperplasia

The aim of this paper was to assess the associations between prostate cancer aggressiveness and histogram-derived apparent diffusion coefficient (ADC) parameters and determine which ADC parameters may help distinguish among stromal hyperplasia (SH), glandular hyperplasia (GH), and low-grade, intermediate-grade, and high-grade prostate cancers. The mean, median, minimum, maximum, and 10th and 25th percentile ADC values were determined from the ADC histogram and compared among two benign prostate hyperplasia (BPH) groups and three Gleason score (GS) groups. Seventy lesions were identified in 58 patients who had undergone proctectomy. Thirty-nine lesions were prostate cancers (GS 6 = 7 lesions, GS 7 = 19 lesions, GS 8 = 11 lesions, GS 9 = 2 lesions), and thirty-one lesions were BPH (SH = 15 lesions, GH = 16 lesions). There were statistically significant differences in 10th percentile and 25th percentile ADC values when comparing GS 6 to GS 7 (p < 0.05). The 10th percentile ADC values yielded the highest area under the curve (AUC). Tenth and 25th percentile ADCs can be used to more accurately differentiate lesions with GS 6 from those with GS 7 than other ADC parameters. Our data indicate that the major challenge with ADC mapping is to differentiate between SH and GS 6, and SH and GS 7.

The Histogram Analysis of Intravoxel Incoherent Motion-Kurtosis Model in the Diagnosis and Grading of Prostate Cancer-A Preliminary Study

Objectives

This study was conducted in order to explore the value of histogram analysis of the intravoxel incoherent motion-kurtosis (IVIM-kurtosis) model in the diagnosis and grading of prostate cancer (PCa), compared with monoexponential model (MEM).

Materials and Methods

Thirty patients were included in this study. Single-shot echo-planar imaging (SS-EPI) diffusion-weighted images (b-values of 0, 20, 50, 100, 200, 500, 1,000, 1,500, 2,000 s/mm²) were acquired. The pathologies were confirmed by in-bore MR-guided biopsy. The postprocessing and measurements were processed using the software tool Matlab R2015b for the IVIM-kurtosis model and MEM. Regions of interest (ROIs) were drawn manually. Mean values of D, D*, f, K, ADC, and their histogram parameters were acquired. The values of these parameters in PCa and benign prostatic hyperplasia (BPH)/prostatitis were compared. Receiver operating characteristic (ROC) curves were used to investigate the diagnostic efficiency. The Spearman test was used to evaluate the correlation of these parameters and Gleason scores (GS) of PCa.

Results

For the IVIM-kurtosis model, D (mean, 10th, 25th, 50th, 75th, 90th), D* (90th), and f (10th) were significantly lower in PCa than in BPH/prostatitis, while D (skewness), D* (kurtosis), and K (mean, 75th, 90th) were significantly higher in PCa than in BPH/prostatitis. For MEM, ADC (mean, 10th, 25th, 50th, 75th, 90th) was significantly lower in PCa than in BPH/prostatitis. The area under the ROC curve (AUC) of the IVIM-kurtosis model was higher than MEM, without significant differences (z = 1.761, P = 0.0783). D (mean, 50th, 75th, 90th), D* (mean, 10th, 25th, 50th, 75th), and f (skewness, kurtosis) correlated negatively with GS, while D (kurtosis), D* (skewness, kurtosis), f (mean, 75th, 90th), and K (mean, 75th, 90th) correlated positively with GS. The histogram parameters of ADC did not show correlations with GS.

Conclusion

The IVIM-kurtosis model has potential value in the differential diagnosis of PCa and BPH/prostatitis. IVIM-kurtosis histogram analysis may provide more information in the grading of PCa than MEM.

A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network

PURPOSE

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. In this paper, an efficient convolutional neural network (CNN) was developed and the model interpretation at various convolutional layers was systematically analyzed to improve the understanding of how CNN interprets multimodality medical images and the predictive powers of features at each layer. The problem of small sample size was addressed by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine (wELM), with imbalanced distribution among output categories taken into consideration.

METHODS

The training data collection used a retrospective set of prostate MR studies, from SPIE-AAPM-NCI PROSTATEx Challenges held in 2017. Three hundred twenty biopsy samples of lesions from 201 prostate cancer patients were diagnosed and identified as clinically significant (malignant) or not significant (benign). All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE) and diffusion-weighted (DW) imaging. After registration and lesion-based normalization, a CNN with four convolutional layers were developed and trained on tenfold cross validation. The features from intermediate layers were then extracted as input to wELM to test the discriminative power of each individual layer. The best performing model from the tenfolds was chosen to be tested on the holdout cohort from two sources. Feature maps after each convolutional layer were then visualized to monitor the trend, as the layer propagated. Scatter plotting was used to visualize the transformation of data distribution. Finally, a class activation map was generated to highlight the region of interest based on the model perspective.

RESULTS

Experimental trials indicated that the best input for CNN was a modality combination of T2W, apparent diffusion coefficient (ADC) and DWI_b50 . The convolutional features from CNN paired with a weighted extreme learning classifier showed substantial performance compared to a CNN end-to-end training model. The feature map visualization reveals similar findings on natural images where lower layers tend to learn lower level features such as edges, intensity changes, etc, while higher layers learn more abstract and task-related concept such as the lesion region. The generated saliency map revealed that the model was able to focus on the region of interest where the lesion resided and filter out background information, including prostate boundary, rectum, etc. CONCLUSIONS: This work designs a customized workflow for the small and imbalanced dataset of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patient stratification.

Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer

PURPOSE

To compare the diagnostic accuracy of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) and to explore the application value of dynamic contrast-enhanced (DCE) MRI in prostate imaging.

METHODS AND MATERIALS

This study retrospectively enrolled 235 patients with suspected PCa in our hospital from January 2016 to December 2017, and all lesions were histopathologically confirmed. The lesions were scored according to the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2). The bpMRI (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC]) and mpMRI (T2WI, DWI/ADC and DCE) scores were recorded to plot the receiver operating characteristic (ROC) curves. The area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for each method were calculated and compared. The patients were further stratified according to bpMRI scores (bpMRI ≥3, and bpMRI = 3, 4, 5) to analyse the difference in DCE MRI between PCa and non-PCa lesions (as well as between csPCa and non-csPCa).

RESULTS

The AUC values for the bpMRI and mpMRI protocols for PCa were comparable (0.790 [0.732-0.840] and 0.791 [0.733-0.841], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for PCa were 76.2, 79.5, 72.6, 75.8, and 76.6%, respectively, and the values for mpMRI were 77.4, 84.4, 69.9, 75.2, and 80.6%, respectively. The AUC values for the bpMRI and mpMRI protocols for the diagnosis of csPCa were similar (0.781 [0.722-0.832] and 0.779 [0.721-0.831], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for csPCa were 74.0, 83.8, 66.9, 64.8, and 85.0%, respectively; and 73.6, 87.9, 63.2, 63.2, and 87.8%, respectively, for mpMRI. For patients with bpMRI scores ≥3, positive DCE results were more common in PCa and csPCa lesions (both P = 0.001). Further stratification analysis showed that for patients with a bpMRI score = 4, PCa and csPCa lesions were more likely to have positive DCE results (P = 0.003 and P < 0.001, respectively).

CONCLUSION

The diagnostic accuracy of bpMRI is comparable with that of mpMRI in the detection of PCa and the identification of csPCa. DCE MRI is helpful in further identifying PCa and csPCa lesions in patients with bpMRI ≥3, especially bpMRI = 4, which may be conducive to achieving a more accurate PCa risk stratification. Rather than omitting DCE, we think further comprehensive studies are required for prostate MRI.

Revisiting quantitative multi-parametric MRI of benign prostatic hyperplasia and its differentiation from transition zone cancer

PURPOSE

This study investigates the multiparametric MRI (mpMRI) appearance of different types of benign prostatic hyperplasia (BPH) and whether quantitative mpMRI is effective in differentiating between prostate cancer (PCa) and BPH.

MATERIALS AND METHODS

Patients (n = 60) with confirmed PCa underwent preoperative 3T MRI. T2-weighted, multi-echo T2-weighted, diffusion weighted and dynamic contrast enhanced images (DCE) were obtained prior to undergoing prostatectomy. PCa and BPH (cystic, glandular or stromal) were identified in the transition zone and matched with MRI. Quantitative mpMRI metrics: T2, ADC and DCE-MRI parameters using an empirical mathematical model were measured.

RESULTS

ADC values were significantly lower (p < 0.001) in PCa compared to all BPH types and can differentiate between PCa and BPH with high accuracy (AUC = 0.87, p < 0.001). T2 values were significantly lower (p < 0.001) in PCa compared to cystic BPH only, while glandular (p = 0.27) and stromal BPH (p = 0.99) showed no significant difference from PCa. BPH mimics PCa in the transition zone on DCE-MRI evidenced by no significant difference between them. mpMRI values of glandular (ADC = 1.31 ± 0.22 µm²/ms, T2 = 115.7 ± 37.3 ms) and cystic BPH (ADC = 1.92 ± 0.43 µm²/ms, T2 = 242.8 ± 117.9 ms) are significantly different. There was no significant difference in ADC (p = 0.72) and T2 (p = 0.46) between glandular and stromal BPH.

CONCLUSIONS

Multiparametric MRI and specifically quantitative ADC values can be used for differentiating PCa and BPH, improving PCa diagnosis in the transition zone. However, DCE-MRI metrics are not effective in distinguishing PCa and BPH. Glandular BPH are not hyperintense on ADC and T2 as previously thought and have similar quantitative mpMRI measurements to stromal BPH. Glandular and cystic BPH appear differently on mpMRI and are histologically different.

Prostate imaging features that indicate benign or malignant pathology on biopsy

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

Comparison of multiparametric and biparametric MRI of the prostate: are gadolinium-based contrast agents needed for routine examinations?

PURPOSE

To investigate, if and how omitting gadolinium-based contrast agents (GBCA) and dynamic contrast-enhanced imaging (DCE) influences diagnostic accuracy and tumor detection rates of prostate MRI.

METHODS

In this retrospective study, 236 patients were included. The results of biparametric (bpMRI) and multiparametric magnetic resonance imaging (mpMRI) were compared using the PI-RADS version 2 scoring system. The distribution of lesions to PIRADS score levels, tumor detection rates, diagnostic accuracy and RoC analysis were calculated and compared to the results of histopathological analysis or 5-year follow-up for benign findings.

RESULTS

Omitting DCE changed PI-RADS scores in 9.75% of patients, increasing the number of PI-RADS 3 scores by 8.89% when compared to mpMRI. No change of more than one score level was observed. BpMRI did not show significant differences in diagnostic accuracy or tumor detection rates. (AuC of 0.914 vs 0.917 in ROC analysis). Of 135 prostate carcinomas (PCa), 94.07% were scored identically, and 5.93% were downgraded only from PI-RADS 4 to PI-RADS 3 by bpMRI. All of them were low-grade PCa with Gleason Score 6 or 7a. No changes were observed for PCa ≥ 7b.

CONCLUSION

Omitting DCE did not lead to significant differences in diagnostic accuracy or tumor detection rates when using the PI-RADS 2 scoring system. According to these data, it seems reasonable to use a biparametric approach for initial routine prostate MRI. This could decrease examination time and reduce costs without significantly lowering the diagnostic accuracy.

Correlation of apparent diffusion coefficient ratio on 3.0 T MRI with prostate cancer Gleason score

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ADC_ratio is a reliable and reproducible tool in quantification of diffusion restriction for clinically significant PCa foci.

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Comparing an experienced and a new MRI reader, Inter-reader reliability in the calculation of ADC_ratio was excellent.

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ADC_ratio has potential to replace the current practice of visual analysis of ADC_tumour reduction, and provide an objective tool.

Introduction

The purpose was to investigate the usefulness of ADC_ratio on Diffusion MRI to discriminate between benign and malignant lesions of Prostate.

Methods

Images of patients who underwent in-gantry MRI guided prostate lesion biopsy were retrospectively analyzed. Prostate Cancers with 20% or more Gleason score (GS) pattern 3 + 3 = 6 in each core or any volume of higher Gleason score pattern were included. ADC_ratio was calculated by two reviewers for each lesion. The ADC_ratio was calculated for each lesion by dividing the lowest ADC value in a lesion and highest ADC value in normal prostate in peripheral zone (PZ). ADC_ratio values were compared with the biopsy result. Data was analysed using independent samples T-test, Spearman correlation, intra-class correlation coefficient (ICC) and Receiver operating characteristic (ROC) curve.

Results

45 lesions in 33 patients were analyzed. 12 lesions were in transitional zone (TZ) and 33 in perpheral zone PZ. All lesions demonstrated an ADC_ratio of 0.45 or lower. GS demonstrated a negative correlation with both the ADC value and ADC_ratio. However, ADC_ratio (p < 0.001) demonstrated a stronger correlation compared to ADC value alone (p = 0.014). There was no significant statistical difference between GS 3 + 4 and GS 4 + 3 mean ADC_tumour value (p = 0.167). However when using ADC_ratio, there was a significant difference (p = 0.032). ROC curve analysis demonstrated an area under the curve of 0.83 using ADC_ratio and 0.76 when using ADC_tumour value when discriminating Gleason 6 from Gleason ≥7 tumours. Inter-observer reliability in the calculation of ADC ratios was excellent, with ICC of 0.964.

Conclusion

ADC_ratio is a reliable and reproducible tool in quantification of diffusion restriction for clinically significant prostate cancer foci.

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.

Model selection for high b-value diffusion-weighted MRI of the prostate

PURPOSE

To assess the abilities of the standard mono-exponential (ME), bi-exponential (BE), diffusion kurtosis (DK) and stretched exponential (SE) models to characterize diffusion signal in malignant and prostatic tissues and determine which of the four models best characterizes these tissues on a per-voxel basis.

MATERIALS AND METHODS

This institutional-review-board-approved, HIPAA-compliant, retrospective study included 55 patients (median age, 61years; range, 42-77years) with untreated, biopsy-proven PCa who underwent endorectal coil MRI at 3-Tesla, diffusion-weighted MRI acquired at eight b-values from 0 to 2000s/mm². Estimated parameters were apparent diffusion coefficent (ME model); diffusion coefficients for the fast (D_fast) and slow (D_slow) components and fraction of fast component, f_fast (BE model); diffusion coefficient D, and kurtosis K (DK model); distributed diffusion coefficient DDC and α for (SE model). For one region-of-interest (ROI) in PZ and another in PCa in each patient, the corrected Akaike information criterion (AICc) and the Akaike weight (w) were calculated for each voxel.

RESULTS

Based on AICc and w, all non-monoexponential models outperformed the ME model in PZ and PCa. The DK model in PZ and SE model in PCa ROIs best fit the greatest average percentages of voxels (39% and 43%, respectively) and had the highest mean w (35±16×10^-2 and 41±22×10^-2, respectively).

CONCLUSION

DK and SE models best fit DWI data in PZ and PCa, and non-ME models consistently outperformed the ME model. Voxel-wise mapping of the preferential model demonstrated that the vast majority of voxels in either tissue type were best fit with one of the non-monoexponential models. At the given SNR levels, the maximum b-value of 2000s/mm² is not sufficiently high to identify the preferred non-monoexponential model.

The utility of CT and MRI in detecting male urethral recurrence after radical cystectomy

PURPOSE

To evaluate the utility of computed tomography (CT) and magnetic resonance imaging (MRI) in detecting male urethral recurrence (UR).

MATERIALS AND METHODS

Between December 2008 and March 2016, 12 men (age range 61-85 years; median, 74 years) with urethral bloody discharge or pain were histologically confirmed as UR after radical cystectomy due to urothelial carcinoma. Of these patients, eight underwent both CT and MRI. The remaining four patients underwent CT only. CT and MRI were compared regarding UR detection rate. CT and MRI were also evaluated to determine which modality was more accurate for depicting UR. UR detection rate of each MRI sequence were recorded. Standard reference was biopsy or urethrectomy in 11 patients and size change in one patient after treatment.

RESULTS

UR detection rate with CT was 41.7% (5/12), while that with MRI was 100% (8/8) (p = 0.0147). Of the eight patients who were diagnosed UR with MRI, six were detected with MRI alone and two with both MRI and CT (p = 0.0313). UR detection rates of T2-weighted, T1-weighted, diffusion-weighted, and contrast-enhanced MRI were 87.5% (7/8), 62.5% (5/8), 100% (5/5), and 87.5% (7/8), respectively.

CONCLUSION

MRI is superior to CT in detecting male URs in symptomatic patients after radical cystectomy. T2-weighted, diffusion-weighted, and contrast-enhanced MRI sequences are useful for detecting male UR.

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.

Geometric distortion correction in prostate diffusion-weighted MRI and its effect on quantitative apparent diffusion coefficient analysis

PURPOSE

To evaluate the effect of correction for B₀ inhomogeneity-induced geometric distortion in echo-planar diffusion-weighted imaging on quantitative apparent diffusion coefficient (ADC) analysis in multiparametric prostate MRI.

METHODS

Geometric distortion correction was performed in echo-planar diffusion-weighted images (b = 0, 50, 400, 800 s/mm² ) of 28 patients, using two b₀ scans with opposing phase-encoding polarities. Histology-matched tumor and healthy tissue volumes of interest delineated on T₂ -weighted images were mapped to the nondistortion-corrected and distortion-corrected data sets by resampling with and without spatial coregistration. The ADC values were calculated on the volume and voxel level. The effect of distortion correction on ADC quantification and tissue classification was evaluated using linear-mixed models and logistic regression, respectively.

RESULTS

Without coregistration, the absolute differences in tumor ADC (range: 0.0002-0.189 mm² /s×10^-3 (volume level); 0.014-0.493 mm² /s×10^-3 (voxel level)) between the nondistortion-corrected and distortion-corrected were significantly associated (P < 0.05) with distortion distance (mean: 1.4 ± 1.3 mm; range: 0.3-5.3 mm). No significant associations were found upon coregistration; however, in patients with high rectal gas residue, distortion correction resulted in improved spatial representation and significantly better classification of healthy versus tumor voxels (P < 0.05).

CONCLUSIONS

Geometric distortion correction in DWI could improve quantitative ADC analysis in multiparametric prostate MRI. Magn Reson Med 79:2524-2532, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention

Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland. Segmentation of the prostate in TRUS imaging is itself a challenging task and prone to high variability. These segmentation errors can lead to poor registration and subsequently poor localization of biopsy targets, which may result in false-negative cancer detection. In this paper, we present a non-rigid surface registration approach to MR-TRUS fusion based on a statistical deformation model (SDM) of intra-procedural deformations derived from clinical training data. Synthetic validation experiments quantifying registration volume of interest overlaps of the PI-RADS parcellation standard and tests using clinical landmark data demonstrate that our use of an SDM for registration, with median target registration error of 2.98 mm, is significantly more accurate than the current clinical method. Furthermore, we show that the low-dimensional SDM registration results are robust to segmentation errors that are not uncommon in clinical TRUS data.

Multiparametric MR Imaging for Detection and Locoregional Staging of Prostate Cancer

Detection and staging of prostate cancer (PCa) based on digital rectal examination, prostate-specific antigen levels, and systematic transrectal ultrasound-guided biopsies show notorious limitations in light of the current needs of PCa management. Multiparametric magnetic resonance imaging (mpMRI) has emerged as a useful noninvasive imaging technique for detection, staging, assessment of aggressiveness, and treatment monitoring of PCa, combining anatomic high-resolution T2-weighted images with functional techniques, such as diffusion-weighted imaging and dynamic contrast enhancement evaluation. In this article, the authors review the technical aspects and the current clinical role of mpMRI for detection and locoregional staging of PCa.

Diagnostic value of integrated PET/MRI for detection and localization of prostate cancer: Comparative study of multiparametric MRI and PET/CT

PURPOSE

To evaluate the diagnostic value of integrated positron emission tomography/magnetic resonance imaging (PET/MRI) compared with conventional multiparametric MRI and PET/computed tomography (CT) for the detailed and accurate segmental detection/localization of prostate cancer.

MATERIALS AND METHODS

Thirty-one patients who underwent integrated PET/MRI using ¹⁸ F-choline and ¹⁸ F-FDG with an integrated PET/MRI scanner followed by radical prostatectomy were included. The prostate was divided into six segments (sextants) according to anatomical landmarks. Three radiologists noted the presence and location of cancer in each sextant on four different image interpretation modalities in consensus (1, multiparametric MRI; 2, integrated ¹⁸ F-FDG PET/MRI; 3, integrated ¹⁸ F-choline PET/MRI; and 4, combined interpretation of 1 and ¹⁸ F-FDG PET/CT). Sensitivity, specificity, accuracy, positive and negative predictive values, likelihood ratios, and diagnostic performance based on the DOR (diagnostic odds ratio) and NNM (number needed to misdiagnose) were evaluated for each interpretation modality, using the pathologic result as the reference standard. Detection rates of seminal vesicle invasion and extracapsular invasion were also evaluated.

RESULTS

Integrated ¹⁸ F-choline PET/MRI showed significantly higher sensitivity than did multiparametric MRI alone in high Gleason score patients (77.0% and 66.2%, P = 0.011), low Gleason score patients (66.7% and 47.4%, P = 0.007), and total patients (72.5% and 58.0%, P = 0.008) groups. Integrated ¹⁸ F-choline PET/MRI and ¹⁸ F-FDG PET/MRI showed similar sensitivity and specificity to combined interpretation of multiparametric MRI and ¹⁸ F-FDG PET/CT (for sensitivity, 58.0%, 63.4%, 72.5%, and 68.7%, respectively, and for specificity, 87.3%, 80.0%, 81.8%, 72.7%, respectively, in total patient group). However, integrated ¹⁸ F-choline PET/MRI showed the best diagnostic performance (as DOR, 11.875 in total patients, 27.941 in high Gleason score patients, 5.714 in low Gleason score groups) among the imaging modalities, regardless of Gleason score. Integrated ¹⁸ F-choline PET/MRI showed higher sensitivity and diagnostic performance than did integrated ¹⁸ F-FDG PET/MRI (as DOR, 6.917 in total patients, 15.143 in high Gleason score patients, 3.175 in low Gleason score groups) in all three patient groups.

CONCLUSION

Integrated PET/MRI carried out using a dedicated integrated PET/MRI scanner provides better sensitivity, accuracy, and diagnostic value for detection/localization of prostate cancer compared to multiparametric MRI. Generally, integrated ¹⁸ F-choline PET/MRI shows better sensitivity, accuracy, and diagnostic performance than does integrated ¹⁸ F-FDG PET/MRI as well as combined interpretation of multiparametric MRI with ¹⁸ F-FDG PET/CT.

LEVEL OF EVIDENCE

2 J. Magn. Reson. Imaging 2017;45:597-609.

Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation

The use of multiparametric MRI scans for the evaluation of men with prostate cancer has increased dramatically and is likely to continue expanding as new developments come to practice. However, it has not yet gained the same level of acceptance of other imaging tests. Partly, this is because of the use of suboptimal protocols, lack of standardization, and inadequate patient preparation. In this manuscript, we describe several practical aspects of prostate MRI that may facilitate the implementation of new prostate imaging programs or the expansion of existing ones.

Magnetic resonance-ultrasound fusion prostate biopsy in the diagnosis of prostate cancer

The advent of multiparametric magnetic resonance imaging (MRI) has ushered in a new era for urologists who perform prostate needle biopsies. The fusion of MRI with transrectal ultrasound (US) allows the direct targeting of suspicious lesions, which has been shown to improve the performance of conventional random biopsy techniques by increasing detection of clinically relevant disease while also decreasing detection of low-risk cancer. However, as with any new technology, many questions regarding effectiveness, reproducibility, and generalizability still remain. In this review, we (1) provide a summary of the various sequences that comprise a MRI of the prostate; (2) evaluate the 3 different ways of incorporating MRI into targeted biopsies of the prostate including in-bore MRI-guided biopsy, cognitive fusion, and device-mediated fusion; (3) review the sensitivity of MR-US fusion in the detection of clinically significant and clinically insignificant disease; and (4) review the barriers to the widespread implementation of MR-US fusion into everyday practice. Whereas other articles in this issue of Urologic Oncology Seminars will discuss other aspects of MRI in the management of prostate cancer, the purpose of this article is to provide an overview of MR-US fusion biopsies in the diagnosis of prostate cancer.

Stratification of the aggressiveness of prostate cancer using pre-biopsy multiparametric MRI (mpMRI)

Risk stratification, based on the Gleason score (GS) of a prostate biopsy, is an important decision-making tool in prostate cancer management. As low-grade disease may not need active intervention, the ability to identify aggressive cancers on imaging could limit the need for prostate biopsies. We assessed the ability of multiparametric MRI (mpMRI) in pre-biopsy risk stratification of men with prostate cancer. One hundred and twenty men suspected to have prostate cancer underwent mpMRI (diffusion MRI and MR spectroscopic imaging) prior to biopsy. Twenty-six had cancer and were stratified into three groups based on GS: low grade (GS ≤ 6), intermediate grade (GS = 7) and high grade (GS ≥ 8). A total of 910 regions of interest (ROIs) from the peripheral zone (PZ, range 25-45) were analyzed from these 26 patients. The metabolite ratio [citrate/(choline + creatine)] and apparent diffusion coefficient (ADC) of voxels were calculated for the PZ regions corresponding to the biopsy cores and compared with histology. The median metabolite ratios for low-grade, intermediate-grade and high-grade cancer were 0.29 (range: 0.16, 0.61), 0.17 (range: 0.13, 0.32) and 0.13 (range: 0.05, 0.23), respectively (p = 0.004). The corresponding mean ADCs (×10(-3) mm(2) /s) for low-grade, intermediate-grade and high-grade cancer were 0.99 ± 0.08, 0.86 ± 0.11 and 0.69 ± 0.12, respectively (p < 0.0001). The combined ADC and metabolite ratio model showed strong discriminatory ability to differentiate subjects with GS ≤ 6 from subjects with GS ≥ 7 with an area under the curve of 94%. These data indicate that pre-biopsy mpMRI may stratify PCa aggressiveness noninvasively. As the recent literature data suggest that men with GS ≤ 6 cancer may not need radical therapy, our data may help limit the need for biopsy and allow informed decision making for clinical intervention. Copyright © 2015 John Wiley & Sons, Ltd.

Body Diffusion-weighted MR Imaging in Oncology: Imaging at 3 T

Advances in hardware and software enable high-quality body diffusion-weighted images to be acquired for oncologic assessment. 3.0 T affords improved signal/noise for higher spatial resolution and smaller field-of-view diffusion-weighted imaging (DWI). DWI at 3.0 T can be applied as at 1.5 T to improve tumor detection, disease characterization, and the assessment of treatment response. DWI at 3.0 T can be acquired on a hybrid PET-MR imaging system, to allow functional MR information to be combined with molecular imaging.

T2* relaxation time in the detection and assessment of aggressiveness of peripheral zone cancer in comparison with diffusion-weighted imaging

AIM

To investigate the feasibility of T2* relaxation time for distinguishing benign from malignant regions, as well as tumour aggressiveness, within the peripheral zone (PZ) of the prostate in comparison with diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Fifty-eight patients with prostate cancer underwent 3 T magnetic resonance imaging using multi-echo T2* and DWI (maximum b-value, 2000 s/mm(2)). Parametric maps were obtained for apparent diffusion coefficient (ADC) and T2* values. Two radiologists reviewed these maps and measured ADC and T2* values in sextants positive for cancer at biopsy. Data were analysed using mixed-model analysis of variance and receiver operating characteristic curves.

RESULTS

Ninety-three sextants exhibited a Gleason score of 6; 59 exhibited a Gleason score of 7 or 8. The T2* value was significantly lower in cancerous sextants than in the benign PZ (48.69+0.60 versus 74.14+0.56, p<0.001), as well as in cancerous sextants with higher rather than lower Gleason scores (43.18+0.89 versus 52.18+0.55, p<0.001). The T2* value showed significantly greater specificity for differentiating cancerous sextants from benign PZ than ADC (93.1% versus 89.7%, p<0.001), with equal sensitivity (82.8% versus 81%, p>0.05). The T2* value exhibited significantly greater sensitivity and specificity for differentiating sextants with low- and high-grade cancer than ADC (79.6% versus 64.5% and 81.4% versus 72.9%, respectively; p<0.05). The T2* value had a significantly greater area under the receiver operating characteristic curve for differentiating sextants with low- and high-grade cancer than ADC (0.77 versus 0.71, p<0.01).

CONCLUSION

Preliminary findings suggest that the T2* relaxation time has increased diagnostic value compared with DWI in prostate PZ cancer assessment.

Pitfalls in Interpreting mp-MRI of the Prostate: A Pictorial Review with Pathologic Correlation

Objectives

The purpose of this pictorial review is to present a wide spectrum of prostate multiparametric MRI (mp-MRI) pitfalls that may occur in clinical practice, with radiological and pathological correlation.

Methods

All examinations were performed according to ESUR Guidelines protocols.

Results and Conclusion

mp-MRI imaging of the prostate often leads to interpreting doubts and misdiagnosis due to the many interpretative pitfalls that a tissue, whether healthy or treated, may cause. These “false-positive” findings may occur in each stage of the disease history, from the primary diagnosis and staging, to the post-treatment stage, and whether they are caused by the tissue itself or are iatrogenic, their recognition is critical for proper treatment and management. Knowledge of these known pitfalls and their interpretation in the anatomical-radiological context can help radiologists avoid misdiagnosis and consequently mistreatment.

Main Messages

• Some physiological changes in the peripheral and central zone may simulate prostate cancer.

• Technical errors, such as mispositioned endorectal coils, can affect the mp-MRI interpretation.

• Physiological changes post-treatment can simulate recurrence

Prostate diffusion-weighted imaging at 3T: effect of intravenous gadobutrol administration

OBJECTIVE

To investigate whether gadolinium-based contrast agent (GBCA) administration significantly affects diffusion-weighted imaging (DWI) at 3 T in the evaluation of prostate cancer and benign tissue.

METHOD

Thirty-four consecutive patients with surgically proven prostate cancer underwent preoperative DWI at 3 T before and after GBCA administration. Exponential apparent diffusion coefficient (EADC) and ADC maps were developed from DWI data. The ADC and EADC values pre- and post-contrast were measured in the cancer and benign tissue, respectively. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were evaluated on pre- and post-contrast DWI.

RESULTS

The ADC and EADC values of the cancer and benign transition zone were not significantly different between pre- and post-contrast, respectively (P > 0.05), while those in the benign peripheral zone were significantly different (P = 0.030 and 0.037, respectively). In all tissues, the SNRs and CNRs of the DWI, ADC map and EADC map were not significantly different between pre- and post-contrast (P > 0.05). Between pre- and post-contrast, ADC and EADC values showed excellent agreement (intraclass correlation coefficient ≥ 0.894) and variability of ≤3.2 %.

CONCLUSION

Prostate 3 T-DWI after GBCA administration may be used without a significant difference in SNR or CNR, with minimal variability of the cancer ADC and EADC values.

KEY POINTS

• ADCs and EADCs have excellent agreement before and after gadobutrol administration. • SNRs of prostate DWI are similar before and after gadobutrol administration. • CNRs of cancers are similar between pre- and post-contrast DWI.

High and ultra-high b-value diffusion-weighted imaging in prostate cancer: a quantitative analysis

BACKGROUND

Diffusion-weighted imaging (DWI) is routinely used in magnetic resonance imaging (MRI) of prostate cancer. However, the routine use of b values higher than 1000 s/mm(2) is not clear up to present. Moreover, the complex diffusion behavior of malignant and benign prostate tissues hampers precise predictions of contrast in DWI images and apparent diffusion coefficient (ADC) maps.

PURPOSE

To quantitatively analyze DWI with different b values in prostate cancer and to identify b values best suitable for cancer detection.

MATERIAL AND METHODS

Forty-one patients with histologically proven prostate cancer were examined with high resolution T2-weighted imaging and DWI at 3 Tesla. Five different b values (0, 800, 1000, 1500, 2000 s/mm(2)) were applied. ADC values of tumors and reference areas were measured on ADC maps derived from different pairs of b values. Furthermore, signal intensities of DW images of tumors and reference areas were measured. For analysis, contrast ratios of ADC values and signal intensities of DW images were calculated and compared.

RESULTS

No significant differences were found between contrast ratios measured on ADC maps of all analyzed b value pairs (P = 0.43). Contrast ratios calculated from signal intensities of DW images were highest at b values of 1500 and 2000 s/mm(2) and differed significantly from contrast ratios at b values of 800 and 1000 s/mm(2) (P < 0.01).

CONCLUSION

Whereas contrast in ADC maps does not significantly change with different b values, contrast ratios of DW images are significantly higher at b-values of 1500 and 2000 s/mm(2) in comparison to b values of 800 and 1000 s/mm(2). Therefore, diagnostic performance of DWI in prostate cancer might be increased by application of b values higher than 1000 s/mm(2).

Transrectal Ultrasound (US), Contrast-enhanced US, Real-time Elastography, HistoScanning, Magnetic Resonance Imaging (MRI), and MRI-US Fusion Biopsy in the Diagnosis of Prostate Cancer

CONTEXT

Debates on overdiagnosis and overtreatment of prostate cancer (PCa) are ongoing and there is still huge uncertainty regarding misclassification of prostate biopsy results. Several imaging techniques that have emerged in recent years could overcome over- and underdiagnosis in PCa.

OBJECTIVE

To review the literature on transrectal ultrasound (TRUS)-based techniques (contrast enhancement, HistoScanning, elastography) and magnetic resonance imaging (MRI)-based techniques for a nonsystematic overview of their benefits and limitations.

EVIDENCE ACQUISITION

A comprehensive search of the PubMed database between August 2004 and August 2014 was performed. Studies assessing grayscale TRUS, contrast-enhanced (CE)-TRUS, elastography, HistoScanning, multiparametric MRI (mpMRI), and MRI-TRUS fusion biopsy were included. Publications before 2004 were included if they reported the principle or the first clinical results for these techniques.

EVIDENCE SYNTHESIS

Grayscale TRUS alone cannot detect PCa foci (detection rate 23-29%). TRUS-based (elastography) and MRI-based techniques (MRI-TRUS fusion biopsy) have significantly improved PCa diagnostics, with sensitivity of 53-74% and specificity of 72-95%. HistoScanning does not provide convincing or homogeneous results (specificity 19-82%). CE-TRUS seems to be user dependent; it is used in a low number of high-volume centers and has wide ranges for sensitivity (54-79%) and specificity (42-95%). For all the techniques reviewed, prospective multicenter studies with consistent definitions are lacking.

CONCLUSIONS

Standard grayscale TRUS is unreliable for PCa detection. Among the techniques reviewed, mpMRI and MRI-TRUS fusion biopsy seem to be suitable for enhancing PCa diagnostics. Elastography shows promising results according to the literature. CE-TRUS yields very inhomogeneous results and might not be the ideal technique for clinical practice. The value of HistoScanning must be questioned according to the literature.

PATIENT SUMMARY

New imaging modalities such as elastography and magnetic resonance imaging/transrectal ultrasound fusion biopsies have improved the detection of prostate cancer. This may lower the burden of overtreatment as a result of more precise diagnosis.

Apparent diffusion coefficient value and ratio as noninvasive potential biomarkers to predict prostate cancer grading: comparison with prostate biopsy and radical prostatectomy specimen

OBJECTIVE. The purpose of this study is to test the association between diffusion-weighted MRI and prostate cancer Gleason score at both biopsy and final pathologic analysis after radical prostatectomy. SUBJECTS AND METHODS. Patients with prostate cancer (n = 72) underwent diffusion-weighted MRI (b values, 0, 800, and 1600 s/mm(2)) with an endorectal coil. Apparent diffusion coefficient (ADC) and ADC ratio were obtained in normal and pathologic tissue and were correlated with transrectal ultrasound-guided biopsy (n = 72) and histopathologic (n = 39) Gleason scores using the ANOVA test. ADC accuracy was estimated using ROC curves. RESULTS. Lesions suspicious for prostate cancer were detected in 65 patients. The mean ADC was 1.47 and 0.87 × 10(-3) mm(2)/s for normal and pathologic tissue, respectively (p < 0.001). When we divided the population into four groups (normal tissue and biopsy Gleason scores of 6, 7, and 8-10), then the mean ADC value was 1.47, 0.96, 0.80, and 0.78 × 10(-3) mm(2)/s, respectively (p < 0.001). The ADC ratio decreased along with an increase in biopsy Gleason score (66.9%, 56.7%, and 51.5% for Gleason scores of 6, 7 and 8-10, respectively) (ANOVA, p = 0.003) and pathologic Gleason score (ANOVA, p < 0.001). ROC curves had an AUC of 0.94 and 0.86 for ADC and ADC ratio, respectively (p = 0.012 and 0.042, respectively). CONCLUSION. Decreasing ADC values may represent a strong risk factor of harboring a poorly differentiated prostate cancer, independently of biopsy characteristics.

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

BACKGROUND

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

PURPOSE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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

Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging

OBJECTIVE

The aim of this study was to demonstrate the feasibility of the recently introduced Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours (VERDICT) framework for imaging prostate cancer with diffusion-weighted magnetic resonance imaging (DW-MRI) within a clinical setting.

MATERIALS AND METHODS

The VERDICT framework is a noninvasive microstructure imaging technique that combines an in-depth diffusion MRI acquisition with a mathematical model to estimate and map microstructural tissue parameters such as cell size and density and vascular perfusion. In total, 8 patients underwent 3-T MRI using 9 different b values (100-3000 s/mm). All patients were imaged before undergoing biopsy. Experiments with VERDICT analyzed DW-MRI data from patients with histologically confirmed prostate cancer in areas of cancerous and benign peripheral zone tissue. For comparison, we also fitted commonly used diffusion models such as the apparent diffusion coefficient (ADC), the intravoxel incoherent motion (IVIM), and the kurtosis model. We also investigated correlations of ADC and kurtosis with VERDICT parameters to gain some biophysical insight into the various parameter values.

RESULTS

Eight patients had prostate cancer in the peripheral zone, with Gleason score 3 + 3 (n = 1), 3 + 4 (n = 6), and 4 + 3 (n = 1). The VERDICT model identified a significant increase in the intracellular and vascular volume fraction estimates in cancerous compared with benign peripheral zone, as well as a significant decrease in the volume of the extracellular-extravascular space (EES) (P = 0.05). This is in agreement with manual segmentation of the biopsies for prostate tissue component analysis, which found proliferation of epithelium, loss of surrounding stroma, and an increase in vasculature. The standard ADC and kurtosis parameters were also significantly different (P = 0.05) between tissue types. There was no significant difference in any of the IVIM parameters (P = 0.11 to 0.29). The VERDICT parametric maps from voxel-by-voxel fitting clearly differentiated cancer from benign regions. Kurtosis and ADC parameters correlated most strongly with VERDICT's intracellular volume fraction but also moderately with the EES and vascular fractions.

CONCLUSIONS

The VERDICT model distinguished tumor from benign areas, while revealing differences in microstructure descriptors such as cellular, vascular, and EES fractions. The parameters of ADC and kurtosis models also discriminated between cancer and benign regions. However, VERDICT provides more specific information that disentangles the various microstructural features underlying the changes in ADC and kurtosis. These results highlight the clinical potential of the VERDICT framework and motivate the construction of a shorter, clinically viable imaging protocol to enable larger trials leading to widespread translation of the method.

Multiparametric magnetic resonance imaging of the prostate: current concepts

Multiparametric MR (mpMR) imaging is rapidly evolving into the mainstay in prostate cancer (PCa) imaging. Generally, the examination consists of T2-weighted sequences, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) evaluation, and less often proton MR spectroscopy imaging (MRSI). Those functional techniques are related to biological properties of the tumor, so that DWI correlates to cellularity and Gleason scores, DCE correlates to angiogenesis, and MRSI correlates to cell membrane turnover. The combined use of those techniques enhances the diagnostic confidence and allows for better characterization of PCa. The present article reviews and illustrates the technical aspects and clinical applications of each component of mpMR imaging, in a practical approach from the urological standpoint.

Diffusion-weighted imaging to evaluate for changes from androgen deprivation therapy in prostate cancer

OBJECTIVE

The objective of our study was to investigate the usefulness of apparent diffusion coefficient (ADC) values in evaluating for therapeutic changes from androgen deprivation therapy (ADT) in prostate cancer patients.

MATERIALS AND METHODS

Forty-eight patients with prostate cancer treated with ADT were enrolled in this retrospective study. Diffusion-weighted imaging (DWI) at 3 T was performed before and after ADT. Before and after treatment, ADC values were measured in the tumors and in the benign tissues of the prostate, and serum prostate-specific antigen (PSA) levels and prostate volumes were also assessed. Statistical analysis was performed using a paired Student t test, Wilcoxon signed rank test, and Spearman rank correlation.

RESULTS

In 48 patients, 55 tumors were identified. After treatment, the mean ADC value of the tumors (1.06×10(-3) mm2/s) was significantly increased as compared with the pretreatment value (0.78×10(-3) mm2/s) (p<0.001), whereas the ADC values of the benign tissues after treatment were significantly decreased compared with the pretreatment values (p<0.001). The mean prostate volume and mean PSA level were significantly reduced from 42.8 cm3 and 153.60 ng/mL before treatment to 21.4 cm3 and 9.51 ng/mL, respectively, after treatment (p<0.001). Changes in tumor ADC values showed a weak negative correlation with changes in PSA levels after treatment (correlation coefficient, ρ=-0.320; p=0.028).

CONCLUSION

DWI may have potential as a noninvasive tool for monitoring changes in response to ADT in prostate cancer patients.

Comparison of stretched-Exponential and monoexponential model diffusion-Weighted imaging in prostate cancer and normal tissues

BACKGROUND

To compare stretched-exponential and monoexponential model diffusion-weighted imaging (DWI) in prostate cancer and normal tissues.

METHODS

Twenty-seven patients with prostate cancer underwent DWI exam using b-values of 0, 500, 1000, and 2000 s/mm(2) . The distributed diffusion coefficients (DDC) and α values of prostate cancer and normal tissues were obtained with stretched-exponential model and apparent diffusion coefficient (ADC) values using monoexponential model. The ADC, DDC (both in 10(-3) mm(2)/s), and α values (range, 0-1) were compared among different prostate tissues. The ADC and DDC were also compared and correlated in each tissue, and the standardized differences between DDC and ADC were compared among different tissues.

RESULTS

Data were obtained for 31 cancers, 36 normal peripheral zone (PZ) and 26 normal central gland (CG) tissues. The ADC (0.71 ± 0.12), DDC (0.60 ± 0.18), and α value (0.64 ± 0.05) of tumor were all significantly lower than those of the normal PZ (1.41 ± 0.22, 1.47 ± 0.20, and 0.85 ± 0.09) and CG (1.25 ± 0.14, 1.32 ± 0.13, and 0.82 ± 0.06) (all P < 0.05). ADC was significantly higher than DDC in cancer, but lower than DDC in the PZ and CG (all P < 0.05). The ADC and DDC were strongly correlated (R(2)  = 0.99, 0.98, 0.99, respectively, all P < 0.05) in all the tissue, and standardized difference between ADC and DDC of cancer was slight but significantly higher than that in normal tissue.

CONCLUSION

The stretched-exponential model DWI provides more parameters for distinguishing prostate cancer and normal tissue and reveals slight differences between DDC and ADC values.

Feasibility of multiparametric prostate magnetic resonance imaging in the detection of cancer distribution: histopathological correlation with prostatectomy specimens

Purpose

To prevent overtreatment, it is very important to diagnose the precise distribution and characteristics of all cancer lesions, including small daughter tumors. The purpose of this study was to evaluate the efficacy of T2-weighted magnetic resonance imaging (T2W), diffusion-weighted magnetic resonance imaging (DWI), magnetic resonance spectroscopy (¹H-MRS), and prostate biopsy (PBx) in the detection of intraprostatic cancer distribution.

Methods

All patients underwent T2W, DWI, ¹H-MRS, and PBx followed by radical prostatectomy (RP). Individual prostates were divided into 12 segmental regions, each of which was examined for the presence or absence of malignancy on the basis of T2W, DWI, ¹H-MRS, and PBx, respectively. These results were compared with the histopathological findings for RP specimens.

Results

We included 54 consecutive patients with biopsy-proven prostate cancer (mean age, 62.7 years; median prostate-specific antigen level, 5.7 ng/mL) in this study. We could detect cancer in 247 of 540 evaluable lesions. The area under the receiver operator characteristic curve analysis yielded a higher value for DWI (0.68) than for T2W (0.65), ¹H-MRS (0.54), or PBx (0.56). In 180 cancerous regions of RP specimens with false-negative PBx results, T2W+DWI had the highest positive rate (53.3%) compared with that of each sequence alone, including T2W (45.6%), DWI (41.1%), and ¹H-MRS (30.0%).

Conclusions

Multiparametric magnetic resonance imaging (T2W, ¹H-MRS, DWI) enables the detection of prostate cancer distribution with reasonable sensitivity and specificity. T2W+DWI was particularly effective in detecting cancer distribution with false-negative PBx results.

Reduced-FOV excitation decreases susceptibility artifact in diffusion-weighted MRI with endorectal coil for prostate cancer detection

The purposes of this study were to determine if image distortion is less in prostate MR apparent diffusion coefficient (ADC) maps generated from a reduced-field-of-view (rFOV) diffusion-weighted-imaging (DWI) technique than from a conventional DWI sequence (CONV), and to determine if the rFOV ADC tumor contrast is as high as or better than that of the CONV sequence. Fifty patients underwent a 3T MRI exam. CONV and rFOV (utilizing a 2D, echo-planar, rectangularly-selective RF pulse) sequences were acquired using b=600, 0s/mm(2). Distortion was visually scored 0-4 by three independent observers and quantitatively measured using the difference in rectal wall curvature between the ADC maps and T2-weighted images. Distortion scores were lower with the rFOV sequence (p<0.012, Wilcoxon Signed-Rank Test, n=50), and difference in distortion scores did not differ significantly among observers (p=0.99, Kruskal-Wallis Rank Sum Test). The difference in rectal curvature was less with rFOV ADC maps (26%±10%) than CONV ADC maps (34%±13%) (p<0.011, Student's t-test). In seventeen patients with untreated, biopsy confirmed prostate cancer, the rFOV sequence afforded significantly higher ADC tumor contrast (44.0%) than the CONV sequence (35.9%), (p<0.0012, Student's t-test). The rFOV sequence yielded significantly decreased susceptibility artifact and significantly higher contrast between tumor and healthy tissue.