Prostate MRI: can we do without DCE sequences in 2013?

Round table: arguments against using multiparametric prostate MRI protocols

Biparametric MRI (bpMRI), which uses only T2-weighted imaging and diffusion-weighted imaging, continues to gain support for the detection of prostate cancer, as this imaging technique offers many benefits over traditional mpMRI. However, the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 document released in 2019 emphasized that mpMRI is still preferred over bpMRI in most clinical scenarios. As one article in a series of four providing arguments for and against using mpMRI and bpMRI protocols, this paper provides arguments against using mpMRI. Within this article, we discuss recent data suggesting equivalent performance between bpMRI and mpMRI in the detection of prostate cancer. The limited utility of dynamic contrast enhancement in the evaluation of prostate cancer according to the PI-RADS v2.1 document is also reviewed. Finally, we detail the large financial and time costs, legal and logistical issues, and potential for patient harm that must be considered with the administration of contrast.

Additional Value of Dynamic Contrast-enhanced Sequences in Multiparametric Prostate Magnetic Resonance Imaging: Data from the PROMIS Study

BACKGROUND

Multiparametric magnetic resonance imaging (MP-MRI) is established in the diagnosis of prostate cancer, but the need for enhanced sequences has recently been questioned.

OBJECTIVE

To assess whether dynamic contrast-enhanced imaging (DCE) improves accuracy over T2 and diffusion sequences.

DESIGN, SETTING, AND PARTICIPANTS

PROMIS was a multicentre, multireader trial, with, in this part, 497 biopsy-naïve men undergoing standardised 1.5T MP-MRI using T2, diffusion, and DCE, followed by a detailed transperineal prostate mapping (TPM) biopsy at 5 mm intervals. Likert scores of 1-5 for the presence of a significant tumour were assigned in strict sequence, for (1) T2 + diffusion and then (2) T2 + diffusion + dynamic contrast-enhanced images.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

For the primary analysis, the primary PROMIS outcome measure (Gleason score ≥4 + 3 or ≥6 mm maximum cancer length) on TPM was used, and an MRI score of ≥3 was considered positive.

RESULTS AND LIMITATIONS

Sensitivity without and with DCE was 94% and 95%, specificity 37% and 38%, positive predictive value 51% and 51%, and negative predictive value 90% and 91%, respectively (p >  0.05 in each case). The number of patients avoiding biopsy (scoring 1-2) was similar (123/497 vs 121/497, p =  0.8). The number of equivocal scores (3/5) was slightly higher without DCE (32% vs 28% p =  0.031). The proportion of MRI equivocal (3/5) and positive (4-5) cases showing significant tumours were similar (23% and 71% vs 20% and 69%). No cases of dominant Gleason 4 or higher were missed with DCE, compared with a single case with T2 + diffusion-weighted imaging. No attempt was made to correlate lesion location on MRI and histology, which may be considered a limitation. Radiologists were aware of the patient's prostate-specific antigen.

CONCLUSIONS

Contrast adds little when MP-MRI is used to exclude significant prostate cancer.

PATIENT SUMMARY

An intravenous injection of contrast may not be necessary when magnetic resonance imaging is used as a test to rule out significant tumours in the prostate.

Comparison of double inversion recovery magnetic resonance imaging (DIR-MRI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in detection of prostate cancer: A pilot study

INTRODUCTION

DCE-MRI is established for detecting prostate cancer (PCa). However, it requires a gadolinium contrast agent, with potential risks for patients. The application of DIR-MRI is simple and may allow cancer detection without the use of an intravenous contrast agent by differentially nullifying signal from normal and abnormal prostate tissue, creating contrast between the cancer and background normal prostate. In this pilot study we gathered data from DIR-MRI and DCE-MRI of the prostate for an equivalence trial. We also looked at how the DIR-MRI appearance varies with the aggressiveness of PCa.

METHOD

DIR-MRI and DCE-MRI were acquired. The images were assessed by an experienced Consultant Radiologist and a novice reporter (Radiographer). The potential PCa lesions were quantified using a lesion to normal ratio (LNR). Radiological pathological correlation was made to identify the MRI lesions that represented significant PCa. A Wilcoxon sign rank was used to compare DCE-LNR and DIR-LNR for PCa containing lesions. Pearson's correlation was used to look at the relationship between DIR-LNR and PCa grade group (aggressiveness).

RESULTS

DCE-LNR and DIR-LNR were found to be significantly different (Z = -5.910, p < 0.001). However, a significant correlation was found between PCa grade group and DIR-LNR.

CONCLUSION

DIR and DCE sequences are not equivalent and significant cancer is more conspicuous on the DCE sequence. However, DIR-LNR does correlate with PCa aggressiveness.

IMPLICATIONS FOR PRACTICE

With the correlation of PCa grade group with DIR-LNR this may be a useful sequence in evaluation of the prostate; stratifying the risk of there being clinically significant PCa before biopsy is performed. Furthermore, given that DIR-LNR appears to predict PCa aggressiveness DIR might be used as part of a multiparametric MRI protocol designed to avoid biopsy.

The Evolution of MRI of the Prostate: The Past, the Present, and the Future

OBJECTIVE. The purpose of this article is to discuss the evolution of MRI in prostate cancer from the early 1980s to the current day, providing analysis of the key studies on this topic. CONCLUSION. The rapid diffusion of MRI technology has meant that residual variability remains between centers regarding the quality of acquisition and the quality and standardization of reporting.

Multiparametric MRI and radiomics in prostate cancer: a review

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Evolution of prostate MRI: from multiparametric standard to less-is-better and different-is better strategies

Multiparametric magnetic resonance imaging (mpMRI) has become the standard of care to achieve accurate and reproducible diagnosis of prostate cancer. However, mpMRI is quite demanding in terms of technical rigour, patient's tolerability and safety, expertise in interpretation, and costs. This paper reviews the main technical strategies proposed as less-is-better solutions for clinical practice (non-contrast biparametric MRI, reduction of acquisition time, abbreviated protocols, computer-aided diagnosis systems), discussing them in the light of the available evidence and of the concurrent evolution of Prostate Imaging Reporting and Data System (PI-RADS). We also summarised research results on those advanced techniques representing an alternative different-is-better line of the still ongoing evolution of prostate MRI (quantitative diffusion-weighted imaging, quantitative dynamic contrast enhancement, intravoxel incoherent motion, diffusion tensor imaging, diffusional kurtosis imaging, restriction spectrum imaging, radiomics analysis, hybrid positron emission tomography/MRI).

Short review of biparametric prostate MRI

Magnetic resonance imaging (MRI) of the prostate has become the gold standard for visualization of prostate cancer. Prostate MRI is usually performed as multiparametric MRI (mpMRI). Since mpMRI has several drawbacks, a biparametric MRI (bpMRI) of the prostate has been proposed. Many studies have been published on mpMRI and bpMRI in recent years. This short review offers an overview of the latest developments in this rapidly evolving field of research.

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.

Dynamic contrast-enhanced magnetic resonance imaging of prostate cancer: A review of current methods and applications

In many areas of oncology, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has proven to be a clinically useful, non-invasive functional imaging technique to quantify tumor vasculature and tumor perfusion characteristics. Tumor angiogenesis is an essential process for tumor growth, proliferation, and metastasis. Malignant lesions demonstrate rapid extravasation of contrast from the intravascular space to the capillary bed due to leaky capillaries associated with tumor neovascularity. DCE-MRI has the potential to provide information regarding blood flow, areas of hypoperfusion, and variations in endothelial permeability and microvessel density to aid treatment selection, enable frequent monitoring during treatment and assess response to targeted therapy following treatment. This review will discuss the current status of DCE-MRI in cancer imaging, with a focus on its use in imaging prostate malignancies as well as weaknesses that limit its widespread clinical use. The latest techniques for quantification of DCE-MRI parameters will be reviewed and compared.

Accuracy of dynamic contrast-enhanced magnetic resonance imaging in the diagnosis of prostate cancer: systematic review and meta-analysis

The goals of this meta-analysis were to assess the effectiveness of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with prostate carcinoma (PCa) and to explore the risk profiles with the highest benefit. Systematic electronic searched were conducted in database. We used patient-based and biopsy-based pooled weighted estimates of the sensitivity, specificity, and a summary receiver operating characteristic (SROC) curve for assessing the diagnostic performance of DCE. We performed direct and indirect comparisons of DCE and other methods of imaging. A total of 26 articles met the inclusion criteria for the analysis. DCE-MRI pooled sensitivity was 0.53 (95% CI 0.39 to 0.67), with a specificity of 0.88 (95% CI 0.83 to 0.92) on whole gland. The peripheral zone pooled sensitivity was 0.70 (95% CI 0.46 to 0.86), with a specificity of 0.88 (95% CI 0.76 to 0.94). Compared with T2-weighted imaging (T2WI), DCE was statistically superior to T2. In conclusion, DCE had relatively high specificity in detecting PCa but relatively low sensitivity as a complementary functional method. DCE-MRI might help clinicians exclude cases of normal tissue and serve as an adjunct to conventional imaging when seeking to identify tumor foci in patients with PCa.

Diagnostic value and relative weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers

Purpose

To assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa).

Materials and methods

We used a prospective database of 262 patients who underwent T2-weighted, diffusion-weighted, and dynamic contrast-enhanced (DCE) imaging before prostatectomy. For each lesion, two independent readers (R1, R2) prospectively defined nine features: shape, volume (V_Max), signal abnormality on each pulse sequence, number of pulse sequences with a marked (S_Max) and non-visible (S_Min) abnormality, likelihood of extracapsular extension (ECE) and PSA density (dPSA). Overall likelihood of malignancy was assessed using a 5-level Likert score. Features were evaluated using the area under the receiver operating characteristic curve (AUC). csPCa was defined as Gleason ≥7 cancer (csPCa-A), Gleason ≥7(4+3) cancer (csPCa-B) or Gleason ≥7 cancer with histological extraprostatic extension (csPCa-C),

Results

For csPCa-A, the Signal1 model (S_Max+S_Min) provided the best combination of signal-related variables, for both readers. The performance was improved by adding V_Max, ECE and/or dPSA, but not shape. All models performed better with DCE findings than without.

When moving from csPCa-A to csPCa-B and csPCa-C definitions, the added value of V_Max, dPSA and ECE increased as compared to signal-related variables, and the added value of DCE decreased.

For R1, the best models were Signal1+ECE+dPSA (AUC = 0,805 [95%CI:0,757–0,866]), Signal1+V_Max+dPSA (AUC = 0.823 [95%CI:0.760–0.893]) and Signal1+ECE+dPSA [AUC = 0.840 (95%CI:0.774–0.907)] for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0.844 [95%CI:0.806–0.877, p = 0.11], 0.841 [95%CI:0.799–0.876, p = 0.52]) and 0.849 [95%CI:0.811–0.884, p = 0.49], respectively.

For R2, the best models were Signal1+V_Max+dPSA (AUC = 0,790 [95%CI:0,731–0,857]), Signal1+V_Max (AUC = 0.813 [95%CI:0.746–0.882]) and Signal1+ECE+V_Max (AUC = 0.843 [95%CI: 0.781–0.907]) for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0. 829 [95%CI:0.791–0.868, p = 0.13], 0.790 [95%CI:0.742–0.841, p = 0.12]) and 0.808 [95%CI:0.764–0.845, p = 0.006]), respectively.

Conclusion

Combination of simple variables can match the Likert score’s results. The optimal combination depends on the definition of csPCa.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

KEY POINTS

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

Integration of multiparametric MRI into active surveillance of prostate cancer

Prostate cancer is the most common noncutaneous cancer in men though many men will not die of this disease and may not require definitive treatment. Active surveillance (AS) is an increasingly utilized potential solution to the issue of overtreatment of prostate cancer. Traditionally, prostate cancer patients have been stratified into risk groups based on clinical stage on digital rectal examination, prostate-specific antigen and biopsy Gleason score, though each of these variables has significant limitations. This review will discuss the potential role for prostate multiparametric MRI and targeted biopsy techniques incorporating MRI in the selection of candidates for AS, monitoring patients on AS and as triggers for definitive treatment.

Dynamic contrast-enhanced case-control analysis in 3T MRI of prostate cancer can help to characterize tumor aggressiveness

PURPOSE

The aim of this work is to establish normality and tumor tissue ranges for perfusion parameters from dynamic contrast-enhanced (DCE) MR of the peripheral prostate at 3T and to compare the diagnostic performance of quantitative and semi-quantitative parameters.

MATERIALS AND METHODS

Thirty-six patients with prostate carcinomas (18 Gleason-6, 15 Gleason-7, and 3 Gleason-8) and 33 healthy subjects were included. Image analysis workflow comprised four steps: manual segmentation of whole prostate and lesions, series registration, voxelwise T1 mapping and calculation of pharmacokinetic and semi-quantitative parameters.

RESULTS

K^trans, v_e, upslope and AUC60 showed statistically significant differences between healthy peripheral areas and tumors. Curve type showed no association with healthy/tumor peripheral areas (chi-square=0.702). Areas under the ROC curves were 0.64 (95% CI: 0.54-0.75), 0.70 (0.60-0.80), 0.62 (0.51-0.72) and 0.63 (0.52-0.74) for K^trans, v_e, upslope and AUC60, respectively. The optimal cutoff values were: K^trans=0.21min^-1 (sensitivity=0.61, specificity=0.64), v_e=0.36 (0.63, 0.71), upslope=0.59 (0.59, 0.59) and AUC60=2.4 (0.63, 0.64). Significant differences were found between Gleason scores 6 and 7 for normalized K^trans, upslope and AUC60, with good diagnostic accuracy (area under ROC curve 0.80, 95% CI: 0.60-1.00).

CONCLUSION

Quantitative (K^trans and v_e) and semi-quantitative (upslope and AUC60) perfusion parameters showed significant differences between tumors and control areas in the peripheral prostate. Normalized K^trans, upslope and AUC60 values might characterize tumor aggressiveness.

Biparametric versus multiparametric MRI in the diagnosis of prostate cancer

Background

Since multiparametric magnetic resonance imaging (mp-MRI) of the prostate exceeds 30 min, minimizing the evaluation time of significant (Gleason scores > 6) prostate cancer (PCa) would be beneficial. A reduced protocol might be sufficient for the diagnosis.

Purpose

To study whether a short unenhanced biparametric MRI (bp-MRI) matches mp-MRI in detecting significant PCa.

Material and Methods

A total of 204 men (median age, 65 years; mean ± SD, 64.1; range 45–75 years; median serum PSA level, 14 ng/mL; range, 2.2–120 ng/mL; median prostate volume, 60 mL; range, 23–263 mL) fulfilled the criteria for being enrolled. They underwent mp-MRI and prostate biopsy from January through June 2014. Of the included patients, 9.3% underwent prostatectomy, 90.7% had TRUS-bx, and 10.8 had MRI-targeted TRUS-bx. Two radiologists separately assessed the mp-MRI examination (T2-weighted [T2W] imaging, diffusion-weighted imaging [DWI], apparent diffusion coefficient map [ADC-map] and dynamic contrast-enhanced imaging [DCE]). Two months later, the bp-MRI version (T2W imaging, DWI, and ADC-map) was evaluated.

Results

Reader 1: Assessing mp-MRI: 0 false negatives, sensitivity of 1, and specificity 0.04. Assessing bp-MRI: four false negatives, sensitivity of 0.94, and specificity 0.15. Reader 2: Assessing mp-MRI: five false negatives, sensitivity of 0.93, and specificity 0.16. Assessing bp-MRI: three false negatives, sensitivity of 0.96, and specificity 0.15. Intra-reader agreement Cohen’s Kappa (κ) was 0.87 for reader 1 (95% confidence interval [CI], 0.83–0.92) and 0.84 for reader 2 (95% CI 0.78–0.89).

Conclusion

Bp-MRI is as good as mp-MRI at detecting PCa. A large prospective study seems to be strongly warranted.

Performance of PI-RADS version 1 versus version 2 regarding the relation with histopathological results

PURPOSE

Aim of this study was to compare the diagnostic performance of PI-RADS version 1 (v1) and version 2 (v2) in the detection of prostate cancer (PCa).

METHODS

Multiparametric MRIs (mpMRI) of 50 consecutive patients with biopsy proven PCa, which had originally been evaluated according to PIRADS v1, were now retrospectively re-evaluated, comparing PI-RADS v1 and v2. MpMRI data were evaluated in comparison with histopathological whole-mount step-section slides. MRI examinations included T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI.

RESULTS

Overall PI-RADS v1 showed a significantly larger discriminative ability of tumor detection: PI-RADS v1 AUC 0.96 (95 % CI 0.94-0.98) and v2 AUC 0.90 (95 % CI 0.86-0.94). For peripheral zone lesions, PI-RADS v1 showed a significantly larger ability of PCa discrimination: v1 AUC 0.97 (95 % CI 0.95-0.99) and v2 AUC 0.92 (95 % CI 0.88-0.96). For transition zone lesions, PI-RADS v1 showed more discrimination: v1 AUC 0.96 (95 % CI 0.92-1.00) and v2 0.90 (95 % CI 0.83-0.97), but the difference was not significant. PI-RADS v2 resulted in significantly more false negative results (3 % in v1, 14 % in v2) and a comparable number of true positive results (82 % in v1, 80 % in v2).

CONCLUSION

PI-RADS v2 uses a simplified approach, but shows a lower diagnostic accuracy. This could lead to a higher rate of false negative results with the risk of missing tumors within low PI-RADS score levels. Therefore, its use cannot be recommended unconditionally, and further improvement should be considered.

Multiparametric prostate magnetic resonance imaging in the evaluation of prostate cancer

Imaging has traditionally played a minor role in the diagnosis and staging of prostate cancer. However, recent controversies generated by the use of prostate-specific antigen (PSA) screening followed by random biopsy have encouraged the development of new imaging methods for prostate cancer. Multiparametric magnetic resonance imaging (mpMRI) has emerged as the imaging method best able to detect clinically significant prostate cancers and to guide biopsies. Here, the authors explain what mpMRI is and how it is used clinically, especially with regard to high-risk populations, and we discuss the impact of mpMRI on treatment decisions for men with prostate cancer. CA Cancer J Clin 2016;66:326-336. © 2015 American Cancer Society.

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.

How accurate is multiparametric MR imaging in evaluation of prostate cancer volume?

PURPOSE

To assess the factors influencing multiparametric (MP) magnetic resonance (MR) imaging accuracy in estimating prostate cancer histologic volume (Vh).

MATERIALS AND METHODS

A prospective database of 202 patients who underwent MP MR imaging before radical prostatectomy was retrospectively used. Institutional review board approval and informed consent were obtained. Two independent radiologists delineated areas suspicious for cancer on images (T2-weighted, diffusion-weighted, dynamic contrast material-enhanced [DCE] pulse sequences) and scored their degree of suspicion of malignancy by using a five-level Likert score. One pathologist delineated cancers on whole-mount prostatectomy sections and calculated their volume by using digitized planimetry. Volumes of MR true-positive lesions were measured on T2-weighted images (VT2), on ADC maps (VADC), and on DCE images [VDCE]). VT2, VADC, VDCE and the greatest volume determined on images from any of the individual MR pulse sequences (Vmax) were compared with Vh (Bland-Altman analysis). Factors influencing MP MR imaging accuracy, or A, calculated as A = Vmax/Vh, were evaluated using generalized linear mixed models.

RESULTS

For both readers, Vh was significantly underestimated with VT2 (P < .0001, both), VADC (P < .0001, both), and VDCE (P = .02 and P = .003, readers 1 and 2, respectively), but not with Vmax (P = .13 and P = .21, readers 1 and 2, respectively). Mean, 25th percentile, and 75th percentile, respectively, for Vmax accuracy were 0.92, 0.54, and 1.85 for reader 1 and 0.95, 0.57, and 1.77 for reader 2. At generalized linear mixed (multivariate) analysis, tumor Likert score (P < .0001), Gleason score (P = .009), and Vh (P < .0001) significantly influenced Vmax accuracy (both readers). This accuracy was good in tumors with a Gleason score of 7 or higher or a Likert score of 5, with a tendency toward underestimation of Vh; accuracy was poor in small (<0.5 cc) or low-grade (Gleason score ≤6) tumors, with a tendency toward overestimation of Vh.

CONCLUSION

Vh can be estimated by using Vmax in aggressive tumors or in tumors with high Likert scores.

Predictive power of the ESUR scoring system for prostate cancer diagnosis verified with targeted MR-guided in-bore biopsy

PURPOSE

This study evaluates the diagnostic value of the ESUR scoring system (PI-RADS) regarding prostate cancer detection using MR-guided in-bore biopsies (IB-GB) as the reference standard.

METHODS

566 lesions in 235 consecutive patients (65.7 ± 7.9 years, PSA 9.9 ± 8.5 ng/ml) with a multiparametric (mp)-MRI (T2WI, DWI, DCE) of the prostate at 3T were scored using the PI-RADS scoring system. PI-RADS single (PSsingle), summed (PSsum), and overall (PSoverall) scores were determined. All lesions were histologically verified by IB-GB.

RESULTS

Lesions with a PSsum below 9 contained no prostate cancer (PCa) with Gleason score (GS) ≥ 4+3=7. A PSsum of 13-15 (PSoverall V) resulted in 87.8% (n=108) in PCa and in 42.3% (n=52) in GS ≥ 4+3=7. Transition zone (TZ) lesions with a PSsum of 13-15 (PSoverall V) resulted in 76.3% (n=36) in PCa and in 26.3% (n=10) in GS ≥ 4+3=7, whereas for peripheral zone (PZ) lesions cancer detection rate at this score was 92.9% (n=79) and 49.4% (n=42) for GS ≥ 4+3=7. Using a threshold of PSsum ≥ 10, sensitivity was 86.0%, and negative predictive value (NPV) was 86.2%. For higher grade PCa sensitivity was 98.6%, and NPV was 99.5%.

CONCLUSION

A PSsum below 9 excluded a higher grade PCa, whereas lesions with a PSsum ≥ 13 (PSoverall V) represented in 88% PCa, and in 42% higher grade PCa. The PSsum or PSoverall demonstrated a better diagnostic value for PZ lesions with higher detection rates for higher grade PCa compared to TZ lesions.

MR-sequences for prostate cancer diagnostics: validation based on the PI-RADS scoring system and targeted MR-guided in-bore biopsy

PURPOSE

This study evaluated the accuracy of MR sequences [T2-, diffusion-weighted, and dynamic contrast-enhanced (T2WI, DWI, and DCE) imaging] at 3T, based on the European Society of Urogenital Radiology (ESUR) scoring system [Prostate Imaging Reporting and Data System (PI-RADS)] using MR-guided in-bore prostate biopsies as reference standard.

METHODS

In 235 consecutive patients [aged 65.7 ± 7.9 years; median prostate-specific antigen (PSA) 8 ng/ml] with multiparametric prostate MRI (mp-MRI), 566 lesions were scored according to PI-RADS. Histology of all lesions was obtained by targeted MR-guided in-bore biopsy.

RESULTS

In 200 lesions, biopsy revealed prostate cancer (PCa). The area under the curve (AUC) for cancer detection was 0.70 (T2WI), 0.80 (DWI), and 0.74 (DCE). A combination of T2WI + DWI, T2WI + DCE, and DWI + DCE achieved an AUC of 0.81, 0.78, and 0.79. A summed PI-RADS score of T2WI + DWI + DCE achieved an AUC of 0.81. For higher grade PCa (primary Gleason pattern ≥ 4), the AUC was 0.85 for T2WI + DWI, 0.84 for T2WI + DCE, 0.86 for DWI + DCE, and 0.87 for T2WI + DWI + DCE. The AUC for T2WI + DWI + DCE for transitional-zone PCa was 0.73, and for the peripheral zone 0.88. Regarding higher-grade PCa, AUC for transitional-zone PCa was 0.88, and for peripheral zone 0.96.

CONCLUSION

The combination of T2WI + DWI + DCE achieved the highest test accuracy, especially in patients with higher-grade PCa. The use of ≤2 MR sequences led to lower AUC in higher-grade and peripheral-zone cancers.

KEY POINTS

• T2WI + DWI + DCE achieved the highest accuracy in patients with higher grade PCa • T2WI + DWI + DCE was more accurate for peripheral- than for transitional-zone PCa • DCE increased PCa detection accuracy in the peripheral zone • DWI was the leading sequence in the transitional zone.