The value of ADC, T2 signal intensity, and a combination of both parameters to assess Gleason score and primary Gleason grades in patients with known prostate cancer

Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review

The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.

The grade of individual prostate cancer lesions predicted by magnetic resonance imaging and positron emission tomography

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) and positron emission tomography (PET) are widely used for the management of prostate cancer (PCa). However, how these modalities complement each other in PCa risk stratification is still largely unknown. We aim to provide insights into the potential of mpMRI and PET for PCa risk stratification.

METHODS

We analyzed data from 55 consecutive patients with elevated prostate-specific antigen and biopsy-proven PCa enrolled in a prospective study between December 2016 and December 2019. [68Ga]PSMA-11 PET (PSMA-PET), [11C]Acetate PET (Acetate-PET) and mpMRI were co-registered with whole-mount histopathology. Lower- and higher-grade lesions were defined by International Society of Urological Pathology (ISUP) grade groups (IGG). We used PET and mpMRI data to differentiate between grades in two cases: IGG 3 vs. IGG 2 (case 1) and IGG ≥ 3 vs. IGG ≤ 2 (case 2). The performance was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

We find that the maximum standardized uptake value (SUVmax) for PSMA-PET achieves the highest area under the ROC curve (AUC), with AUCs of 0.72 (case 1) and 0.79 (case 2). Combining the volume transfer constant, apparent diffusion coefficient and T2-weighted images (each normalized to non-malignant prostatic tissue) results in AUCs of 0.70 (case 1) and 0.70 (case 2). Adding PSMA-SUVmax increases the AUCs by 0.09 (p < 0.01) and 0.12 (p < 0.01), respectively.

CONCLUSIONS

By co-registering whole-mount histopathology and in-vivo imaging we show that mpMRI and PET can distinguish between lower- and higher-grade prostate cancer, using partially discriminative cut-off values.

Dynamic Contrast Enhanced Study in Multiparametric Examination of the Prostate—Can We Make Better Use of It?

We sought to investigate whether quantitative parameters from a dynamic contrast-enhanced study can be used to differentiate cancer from normal tissue and to determine a cut-off value of specific parameters that can predict malignancy more accurately, compared to the obturator internus muscle as a reference tissue. This retrospective study included 56 patients with biopsy proven prostate cancer (PCa) after multiparametric magnetic resonance imaging (mpMRI), with a total of 70 lesions; 39 were located in the peripheral zone, and 31 in the transition zone. The quantitative parameters for all patients were calculated in the detected lesion, morphologically normal prostate tissue and the obturator internus muscle. Increase in the K_trans value was determined in lesion-to-muscle ratio by 3.974368, which is a cut-off value to differentiate between prostate cancer and normal prostate tissue, with specificity of 72.86% and sensitivity of 91.43%. We introduced a model to detect prostate cancer that combines K_trans lesion-to-muscle ratio value and iAUC lesion-to-muscle ratio value, which is of higher accuracy compared to individual variables. Based on this model, we identified the optimal cut-off value with 100% sensitivity and 64.28% specificity. The use of quantitative DCE pharmacokinetic parameters compared to the obturator internus muscle as reference tissue leads to higher diagnostic accuracy for prostate cancer detection.

MRI grading for the prediction of prostate cancer aggressiveness

OBJECTIVES

T o evaluate the value of multiparametric MRI (mpMRI) for the prediction of prostate cancer (PCA) aggressiveness.

METHODS

In this single center cohort study, consecutive patients with histologically confirmed PCA were retrospectively enrolled. Four different ISUP grade groups (1, 2, 3, 4-5) were defined and fifty patients per group were included. Several clinical (age, PSA, PSAD, percentage of PCA infiltration) and mpMRI parameters (ADC value, signal increase on high b-value images, diameter, extraprostatic extension [EPE], cross-zonal growth) were evaluated and correlated within the four groups. Based on combined descriptors, MRI grading groups (mG1-mG3) were defined to predict PCA aggressiveness.

RESULTS

In total, 200 patients (mean age 68 years, median PSA value 8.1 ng/ml) were analyzed. Between the four groups, statistically significant differences could be shown for age, PSA, PSAD, and for MRI parameters cross-zonal growth, high b-value signal increase, EPE, and ADC (p < 0.01). All examined parameters revealed a significant correlation with the histopathologic biopsy ISUP grade groups (p < 0.01), except PCA diameter (p = 0.09). A mixed linear model demonstrated the strongest prediction of the respective ISUP grade group for the MRI grading system (p < 0.01) compared to single parameters.

CONCLUSIONS

MpMRI yields relevant pre-biopsy information about PCA aggressiveness. A combination of quantitative and qualitative parameters (MRI grading groups) provided the best prediction of the biopsy ISUP grade group and may improve clinical pathway and treatment planning, adding useful information beyond PI-RADS assessment category. Due to the high prevalence of higher grade PCA in patients within mG3, an early re-biopsy seems indicated in cases of negative or post-biopsy low-grade PCA.

KEY POINTS

• MpMRI yields relevant pre-biopsy information about prostate cancer aggressiveness. • MRI grading in addition to PI-RADS classification seems to be helpful for a size independent early prediction of clinically significant PCA. • MRI grading groups may help urologists in clinical pathway and treatment planning, especially when to consider an early re-biopsy.

Acta Radiologica 2003-2017: a 15-year overview

This review article is written as a contribution to the special issue presented in conjunction with the 100th anniversary of Acta Radiologica.An overview is given of what has happened with and in the journal during the 15 years from 2003 to 2017 and a resume is provided concerning the handling and flow of manuscripts, manuscript publication, scientific prizes awarded by the journal, and finally the process leading up to establishing the new open-access journal Acta Radiologica Short Reports/Acta Radiologica Open.

[Financing perspectives for multiparametric magnetic resonance prostatography]

BACKGROUND

Not only is the evidence for multiparametric magnetic resonance prostatography clearly proven based on current research, the S3 guideline for prostate cancer recommends its use prior to invasive biopsy. Remuneration through the GKV does not occur.

OBJECTIVES

The negotiations concerning the inclusion in the EBM (German Uniform Evaluation Standard) Catalogue of statutory health insurance funds take place in a highly politicized environment and under economic priorities. The routes that are possible in the complex registration procedure are described.

MATERIALS AND METHODS

Radiology associations (Berufsverband der Deutschen Radiologen [BDR] und Deutsche Röntgengesellschaft [DRG]) have supported their methods with evidence and quality assurance. Special contracts with health insurance funds, coordinated at the level of the federal states, pave the way and accelerate accreditation.

RESULTS

The definition of the service according to the EBM, the recommendation concerning remuneration as well as supporting documents and a functional quality assurance system have been made available to the Joint Valuation Committee of physicians & health insurance funds as part of the application for approval.

CONCLUSIONS

Due to the nature of the system, the presented evidence and quality assurance, as well as the development of special contracts, have inevitably been transferred to radiology and the unified work of their associations. The imaging modality prostatography shows the advancement of radiological methods for dedicated multiparametric organ diagnostics.

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

Objective

To evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2).

Materials and methods

Fifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann−Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed.

Results

Six texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750−0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732−0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort.

Conclusion

A combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.

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.

Biomarkers That Differentiate Benign Prostatic Hyperplasia from Prostate Cancer: A Literature Review

Prediction of prostate cancer in primary care is typically based upon serum total prostate-specific antigen (tPSA) and digital rectal examination results. However, these tests lack sensitivity and specificity, leading to over-diagnosis of disease and unnecessary, invasive biopsies. Therefore, there is a clinical need for diagnostic tests that can differentiate between benign conditions and early-stage malignant disease in the prostate. In this review, we evaluate research papers published from 2009 to 2019 reporting biomarkers that identified or differentiated benign prostatic hyperplasia (BPH) from prostate cancer. Our review identifies hundreds of potential biomarkers in urine, serum, tissue, and semen proposed as useful targets for differentiating between prostate cancer and BPH patients. However, it is still not apparent which of these candidate biomarkers are most useful, and many will not progress beyond the discovery stage unless they are properly validated for clinical practice. We conclude that this validation will come through the use of multivariate panels which can assess the value of biomarker candidates in combination with clinical parameters as part of a risk prediction calculator. Implementation of such a model will help clinicians stratify patients with prostate cancer symptoms in primary care, with tangible benefits for both the patient and the health service.

Quantitative Prostate MRI

Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T₁ and T₂ relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores

Background

To investigate if supervised machine learning (ML) classifiers would be able to predict clinically significant cancer (sPC) from a set of quantitative image-features and to compare these results with established PI-RADS v2 assessment scores.

Methods

We retrospectively included 201, histopathologically-proven, peripheral zone (PZ) prostate cancer lesions. Gleason scores ≤3+3 were considered as clinically insignificant (inPC) and Gleason scores ≥3+4 as sPC and were encoded in a binary fashion, serving as ground-truth. MRI was performed at 3T with high spatiotemporal resolution DCE using Golden-angle RAdial SParse (GRASP) MRI. Perfusion maps (Ktrans, Kep, Ve), apparent diffusion coefficient (ADC), and absolute T2-signal intensities (SI) were determined in all lesions and served as input parameters for four supervised ML models: Gradient Boosting Machines (GBM), Neural Networks (NNet), Random Forest (RF) and Support Vector Machines (SVM). ML results and PI-RADS scores were compared with the ground-truth. Next ROC-curves and AUC values were calculated.

Results

All ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC (RF, GBM, NNet and SVM vs. PI-RADS: AUC 0.899, 0.864, 0.884 and 0.874 vs. 0.595, all P<0.001).

Conclusions

Using quantitative imaging parameters as input, supervised ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC. These results indicate that quantitative imagining parameters contain relevant information for the prediction of sPC from image features.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer

PURPOSE

To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard.

METHODS

We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADCT), ADC of adjacent normal tissue (ADCN), and ADCratio (ADCT/ADCN) were calculated. Stepwise regression analysis using tumor location, ADCT and ADCratio, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3.

RESULTS

119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADCratio was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADCT (p = 0.03) and ADCratio (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively.

CONCLUSIONS

ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

The value of MR textural analysis in prostate cancer

Current diagnosis and treatment stratification of patients with suspected prostate cancer relies on a combination of histological and magnetic resonance imaging (MRI) findings. The aim of this article is to provide a brief overview of prostate pathological grading as well as the relevant aspects of multiparametric (MRI) mpMRI, before indicating the potential that magnetic resonance textural analysis (MRTA) offers within prostate cancer. A review of the evidence base on MRTA in prostate cancer will enable discussion of the utility of this field while also indicating recommendations to future research. Radiomic textural analysis allows the assessment of spatial inter-relationships between pixels within an image by use of mathematical methods. First-order textural analysis is better understood and may have more clinical validity than higher-order textural features. Textural features extracted from apparent diffusion coefficient maps have shown the most potential for clinical utility in MRTA of prostate cancers. Future studies should aim to integrate machine learning techniques to better represent the role of MRTA in prostate cancer clinical practice. Nomenclature should be used to reduce misidentification between first-order and second-order energy and entropy. Automated methods of segmentation should be encouraged in order to reduce problems associated with inclusion of normal tissue within regions of interest. The retrospective and small-scale nature of most published studies, make it difficult to draw meaningful conclusions. Future larger prospective studies are required to validate the textural features indicated to have potential in characterisation and/or diagnosis of prostate cancer before translation into routine clinical practice.

Comparison of the accuracy of multiparametric magnetic resonance imaging (mpMRI) results with the final pathology findings for radical prostatectomy specimens in the detection of prostate cancer

AIMS

To assess the accuracy of multiparametric magnetic resonance imaging (mpMRI), used in conjunction with the Prostrate Imaging Reporting and Data System (PI-RADS), version 2, in the detection of prostate cancer (PCa), and to determine the extent of the efficacy of mpMRI as a screening test in biopsy-naïve patients.

METHODS

Retrospective analysis was conducted in 107 patients who underwent mpMRI prior to radical prostatectomy (RP) at a single institution. The mpMRI findings were reassessed using PI-RADS, version 2. A comparison was made between the histological findings for the RP specimens and the mpMRI results.

RESULTS

Unique histologically confirmed PCa foci (237) were identified in 107 patients. Overall, mpMRI sensitivity of 46% was found for PCa detection (110/237). The sensitivity, specificity and negative predictive value of mpMRI was 75.5%, 77.0% and 79.8%, respectively, for clinically significant cancer, and 75.7%, 77.7% and 79.5%, for pathological index tumors. A moderate and significant correlation was observed between a high PI-RADS score and a high pathological grade, tumor volume, index tumor status and clinically significant cancer status (all, P < 0.001, respectively). Pathological tumor volume was a significant predictor of PCa detection using mpMRI according to multivariate analysis. Using a cut-off value of 0.89 cc, the sensitivity and specificity of mpMRI for PCa detection were 0.87 and 0.65, respectively.

CONCLUSION

The mpMRI, used in conjunction with PI-RADS, was useful in detecting PCa and in predicting tumor aggressiveness. However, the detection of 20% of clinically significant cancer was missed using mpMRI. Thus, its inclusion in a triage test should be limited to selected biopsy-naïve patients.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Prostate cancer detection using quantitative T2 and T2 -weighted imaging: The effects of 5-alpha-reductase inhibitors in men on active surveillance

BACKGROUND

T₂ -weighted imaging (T₂ -WI) information has been used in a qualitative manner in the assessment of prostate cancer. Quantitative derivatives (T₂ relaxation time) can be generated from T₂ -WI. These outputs may be useful in helping to discriminate clinically significant prostate cancer from background signal.

PURPOSE/HYPOTHESIS

To investigate changes in quantitative T₂ parameters in lesions and noncancerous tissue of men on active surveillance for prostate cancer taking dutasteride 0.5 mg or placebo daily for 6 months.

STUDY TYPE

Retrospective.

POPULATION/SUBJECTS

Forty men randomized to 6 months of daily dutasteride (n = 20) or placebo (n = 20).

FIELD STRENGTH/SEQUENCE

Multiparametric 3T MRI at baseline and 6 months. This included a multiecho MR sequence for quantification of the T₂ relaxation times, in three regions of interest (index lesion, noncancerous peripheral [PZ] and transitional [TZ] zones). A synthetic signal contrast (T₂ Q contrast) between lesion and noncancerous tissue was assessed using quantitative T₂ values. Signal contrast was calculated using the T₂ -weighted sequence (T₂ W contrast).

ASSESSMENT

Two radiologists reviewed the scans in consensus according to Prostate Imaging Reporting and Data System (PI-RADS v. 2) guidelines.

STATISTICAL TESTS

Wilcoxon and Mann-Whitney U-tests, Spearman's correlation.

RESULTS

When compared to noncancerous tissue, shorter T₂ values were observed within lesions at baseline (83.5 and 80.5 msec) and 6 months (81.5 and 81.9 msec) in the placebo and dutasteride arm, respectively. No significant differences for T₂ W contrast at baseline and after 6 months were observed, both in the placebo (0.40 [0.29-0.49] vs. 0.43 [0.25-0.49]; P = 0.881) and dutasteride arm (0.35 [0.24-0.47] vs. 0.37 [0.22-0.44]; P = 0.668). There was a significant, positive correlation between the T₂ Q contrast and the T₂ W contrast values (r = 0.786; P < 0.001).

DATA CONCLUSION

The exposure to antiandrogen therapy did not significantly influence the T₂ contrast or the T₂ relaxation values in men on active surveillance for prostate cancer.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1646-1653.

[Imaging of locally advanced prostate cancer : Importance of ultrasound and especially MRI]

Prostate cancer is the most common male malignant tumor in Germany, which thus places growing demands on differentiated imaging and risk-adapted therapeutic approaches. Multiparametric MRI (mpMRI) of the prostate enables reliable detection of clinically significant cancers and is currently the leading imaging modality for the detection, characterization, and local staging of prostate cancer. According to the German S3 guideline, mpMRI of the prostate is currently primarily recommended in patients with previous negative TRUS biopsies and persisting tumor suspicion. The serial use of mpMRI in the pretherapeutic setting can support individual therapy planning of patients with locally advanced prostate cancer in the near future.

Role of MRI in the Risk Assessment of Primary Prostate Cancer

A successful paradigm shift toward personalized management strategies for patients with prostate cancer (PCa) is heavily dependent on the availability of noninvasive diagnostic tools capable of accurately establishing the true extent of disease at the time of diagnosis and estimating the risk of subsequent disease progression and related mortality. Although there is still considerable scope for improvement in its diagnostic, predictive, and prognostic capabilities, multiparametric prostate magnetic resonance imaging (MRI) is currently regarded as the imaging modality of choice for local staging of PCa. A negative MRI, that is, the absence of any MRI-visible intraprostatic lesion, has a high negative predictive value for the presence of clinically significant PCa and can substantiate the consideration of active surveillance as a preferred initial management approach. MRI-derived quantitative and semi-quantitative parameters can be utilized to noninvasively characterize MRI-visible prostate lesions and identify those patients who are most likely to benefit from radical treatment, and differentiate them from patients with benign or indolent prostate pathology that may also be visible on MRI. This literature review summarizes current strategies how MRI can be used to determine a tailored management strategy for an individual patient.

Apparent diffusion coefficient values are superior to transrectal ultrasound-guided prostate biopsy for the assessment of prostate cancer aggressiveness

Background Few studies have focused on comparing the utility of diffusion-weighted imaging (DWI) and transrectal ultrasound (TRUS)-guided biopsy in predicting prostate cancer aggressiveness. Whether apparent diffusion coefficient (ADC) values can provide more information than TRUS-guided biopsy should be confirmed. Purpose To retrospectively assess the utility of ADC values in predicting prostate cancer aggressiveness, compared to the TRUS-guided prostate biopsy Gleason score (GS). Material and Methods The DW images of 54 patients with biopsy-proven prostate cancer were obtained using 1.5-T magnetic resonance (MR). The mean ADC values of cancerous areas and biopsy GS were correlated with prostatectomy GS and D'Amico clinical risk scores, respectively. Meanwhile, the utility of ADC values in identifying high-grade prostate cancer (with Gleason 4 and/or 5 components in prostatectomy) in patients with a biopsy GS ≤ 3 + 3 = 6 was also evaluated. Results A significant negative correlation was found between mean ADC values of cancerous areas and the prostatectomy GS ( P < 0.001) and D'Amico clinical risk scores ( P < 0.001). No significant correlation was found between biopsy GS and prostatectomy GS ( P = 0.140) and D'Amico clinical risk scores ( P = 0.342). Patients harboring Gleason 4 and/or 5 components in prostatectomy had significantly lower ADC values than those harboring no Gleason 4 and/or 5 components ( P = 0.004). Conclusion The ADC values of cancerous areas in the prostate are a better indicator than the biopsy GS in predicting prostate cancer aggressiveness. Moreover, the use of ADC values can help identify the presence of high-grade tumor in patients with a Gleason score ≤ 3 + 3 = 6 during biopsy.

Prostate MR Imaging: An Update

Improvements in prostate MR imaging techniques and the introduction of MR imaging-targeted biopsies have had central roles in prostate cancer (PCa) management. The role of MR imaging has progressed from largely staging patients with biopsy-proven PCa to detecting, characterizing, and guiding the biopsy of suspected PCa. These diagnostic advances, combined with improved therapeutic interventions, have led to a more sophisticated and individually tailored approach to patients' unique PCa profile. This review discusses the MR imaging, a standardized reporting scheme, and the role of fusion-targeted prostate biopsy.

Role of multi-parametric MRI of the prostate for screening and staging: Experience with over 1500 cases

Objective

Contemporary prostate cancer (PCa) screening modalities such as prostate specific antigen (PSA) and digital rectal examination (DRE) are limited in their ability to predict the detection of clinically significant disease. Multi-parametric magnetic resonance imaging (mpMRI) of the prostate has been explored as a staging modality for PCa. Less is known regarding its utility as a primary screening modality. We examined our experience with mpMRI as both a screening and staging instrument.

Methods

mpMRI studies performed between 2012 and 2014 in patients without PCa were cross-referenced with transrectal ultrasonography (TRUS) biopsy findings. Statistical analyses were performed to determine association of mpMRI findings with overall cancer diagnoses and clinically significant (Gleason score ≥7) disease. Subgroup analyses were then performed on patients with a history of prior negative biopsy and those without a history of TRUS biopsy. mpMRI studies were also cross-referenced with RP specimens. Statistical analyses determined predictive ability of extracapsular extension (ECE), seminal vesicle involvement (SVI), and pathologic evidence of clinically significant disease (Gleason score ≥7).

Results

Four hundred biopsy naïve or prior negative biopsy patients had positive mpMRI studies. Overall sensitivity, specificity, positive and negative predictive values were 94%, 37%, 58%, and 87%, respectively and 95%, 31%, 42%, and 93%, respectively for overall cancer detection and Gleason score ≥7 disease. In patients with no prior biopsy history, mpMRI sensitivity, specificity, positive and negative predictive values were 94%, 36%, 65%, and 82%, for all cancers, and 95%, 30%, 50%, and 89% for Gleason score≥7 lesions, respectively. In those with prior negative biopsy sensitivity, specificity, positive and negative predictive values were 94%, 37%, 52%, and 90% for all cancers, and 96%, 32%, 36%, and 96% for Gleason score ≥7 lesions, respectively. Seventy-four patients underwent radical prostatectomy (RP) after mpMRI. Lesion size on mpMRI correlated with the presence of Gleason score ≥7 cancers (p = 0.005). mpMRI sensitivity, specificity, positive and negative predictive values were 84%, 39%, 81%, and 44% respectively, for Gleason ≥7 cancer. For ECE and SVI, sensitivity and specificity were 58% and 98% and 44% and 97%, respectively.

Conclusion

mpMRI is an accurate predictor of TRUS biopsy and RP outcomes. mpMRI has significant potential to change PCa management, particularly in the screening population, in whom a significant proportion may avoid TRUS biopsy. Further studies are necessary to determine how mpMRI should be incorporated into the current PCa screening and staging paradigms.

[MR imaging of the prostate]

CLINICAL ISSUE

Prostate cancer is the most common form of cancer in men in Germany; however, there is a distinct difference between incidence and mortality.

STANDARD TREATMENT

The detection of prostate cancer is based on clinical and laboratory testing using serum prostate-specific antigen (PSA) levels and transrectal ultrasound with randomized biopsy.

DIAGNOSTIC WORK-UP

Multiparametric MR imaging of the prostate can provide valuable diagnostic information for detection of prostate cancer, especially after negative results of a biopsy prior to repeat biopsy.

PERFORMANCE

In addition the use of MR ultrasound fusion-guided biopsy has gained in diagnostic importance and has increased the prostate cancer detection rate.

ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS

The prostate imaging reporting and data system (PI-RADS) classification has standardized the reporting of prostate MRI which has positively influenced the acceptance by urologists.

Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications

One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Comparison of optimised endovaginal vs external array coil T2-weighted and diffusion-weighted imaging techniques for detecting suspected early stage (IA/IB1) uterine cervical cancer

OBJECTIVE

To compare sensitivity and specificity of endovaginal versus external-array coil T2-W and T2-W + DWI for detecting and staging small cervical tumours.

METHODS

Optimised endovaginal and external array coil MRI at 3.0-T was done prospectively in 48 consecutive patients with stage Ia/Ib1 cervical cancer. Sensitivity/specificity for detecting tumour and parametrial extension against histopathology for a reading radiologist were determined on coronal T2-W and T2W + DW images. An independent radiologist also scored T2-W images without and with addition of DWI for the external-array and endovaginal coils on separate occasions >2 weeks apart. Cohen's kappa assessed inter- and intra-observer agreement.

RESULTS

Median tumour volume in 19/38 cases positive on subsequent histology was 1.75 cm(3). Sensitivity, specificity, PPV, NPV were: reading radiologist 91.3 %, 89.5 %, 91.3 %, 89.5 %, respectively; independent radiologist T2-W 82.6 %, 73.7 %, 79.1 %, 77.8 % for endovaginal, 73.9 %, 89.5 %, 89.5 %, 73.9 % for external-array coil. Adding DWI improved sensitivity and specificity of endovaginal imaging (78.2 %, 89.5 %); adding DWI to external-array imaging improved specificity (94.7 %) but reduced sensitivity (66.7 %). Inter- and intra-observer agreement on T2-W + DWI was good (kappa = 0.67 and 0.62, respectively).

CONCLUSION

Endovaginal coil T2-W MRI is more sensitive than external-array coil for detecting tumours <2 cm(3); adding DWI improves specificity of endovaginal imaging but reduces sensitivity of external-array imaging.

KEY POINTS

• Endovaginal more accurate than external-array T2-W MRI for detecting small cervical cancers. • Addition of DWI improves sensitivity and specificity of endovaginal T2-W imaging. • Addition of DWI substantially reduces sensitivity of external-array T2-W imaging.

Prostate cancer risk stratification with magnetic resonance imaging

In recent years, multiparametric magnetic resonance imaging (mpMRI) has shown promise for prostate cancer (PCa) risk stratification. mpMRI, often followed by targeted biopsy, can be used to confirm low-grade disease before enrollment in active surveillance. In patients with intermediate or high-risk PCa, mpMRI can be used to inform surgical management. mpMRI has sensitivity of 44% to 87% for detection of clinically significant PCa and negative predictive value of 63% to 98% for exclusion of significant disease. In addition to tumor identification, mpMRI has also been shown to contribute significant incremental value to currently used clinical nomograms for predicting extraprostatic extension. In combination with conventional clinical criteria, accuracy of mpMRI for prediction of extraprostatic extension ranges from 92% to 94%, significantly higher than that achieved with clinical criteria alone. Supplemental sequences, such as diffusion-weighted imaging and dynamic contrast-enhanced imaging, allow quantitative evaluation of cancer-suspicious regions. Apparent diffusion coefficient appears to be an independent predictor of PCa aggressiveness. Addition of apparent diffusion coefficient to Epstein criteria may improve sensitivity for detection of significant PCa by as much as 16%. Limitations of mpMRI include variability in reporting, underestimation of PCa volume and failure to detect clinically significant disease in a small but significant number of cases.

Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the Peripheral Zone: A Multiple Imager Study

Purpose To assess the intermanufacturer variability of quantitative models in discriminating cancers with a Gleason score of at least 7 among peripheral zone (PZ) lesions seen at 3-T multiparametric magnetic resonance (MR) imaging. Materials and Methods An institutional review board-approved prospective database of 257 patients who gave written consent and underwent T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging before prostatectomy was retrospectively reviewed. It contained outlined lesions found to be suspicious for malignancy by two independent radiologists and classified as malignant or benign after correlation with prostatectomy whole-mount specimens. One hundred six patients who underwent imaging with 3-T MR systems from two manufacturers were selected (data set A, n = 72; data set B, n = 34). Eleven parameters were calculated in PZ lesions: normalized T2-weighted signal intensity, skewness and kurtosis of T2-weighted signal intensity, T2 value, wash-in rate, washout rate, time to peak (TTP), mean apparent diffusion coefficient (ADC), 10th percentile of the ADC, and skewness and kurtosis of the histogram of the ADC values. Parameters were selected on the basis of their specificity for a sensitivity of 0.95 in diagnosing cancers with a Gleason score of at least 7, and the area under the receiver operating characteristic curve (AUC) for the models was calculated. Results The model of the 10th percentile of the ADC with TTP yielded the highest AUC in both data sets. In data set A, the AUC was 0.90 (95% confidence interval [CI]: 0.85, 0.95) or 0.89 (95% CI: 0.82, 0.94) when it was trained in data set A or B, respectively. In data set B, the AUC was 0.84 (95% CI: 0.74, 0.94) or 0.86 (95% CI: 0.76, 0.95) when it was trained in data set A or B, respectively. No third variable added significantly independent information in any data set. Conclusion The model of the 10th percentile of the ADC with TTP yielded accurate results in discriminating cancers with a Gleason score of at least 7 among PZ lesions at 3 T in data from two manufacturers. (©) RSNA, 2016 Online supplemental material is available for this article.

Multiparametric-MRI in diagnosis of prostate cancer

Multiparametric-magnetic resonance imaging (mp-MRI) has shown promising results in diagnosis, localization, risk stratification and staging of clinically significant prostate cancer. It has also opened up opportunities for focal treatment of prostate cancer. Combinations of T2-weighted imaging, diffusion imaging, perfusion (dynamic contrast-enhanced imaging) and spectroscopic imaging have been used in mp-MRI assessment of prostate cancer, but T2 morphologic assessment and functional assessment by diffusion imaging remains the mainstay for prostate cancer diagnosis on mp-MRI. Because assessment on mp-MRI can be subjective, use of the newly developed standardized reporting Prostate Imaging and Reporting Archiving Data System scoring system and education of specialist radiologists are essential for accurate interpretation. This review focuses on the present status of mp-MRI in prostate cancer and its evolving role in the management of prostate cancer.