Head-to-head comparison of prostate-specific membrane antigen PET and multiparametric MRI in the diagnosis of pretreatment patients with prostate cancer: a meta-analysis

OBJECTIVES

To compare prostate-specific membrane antigen (PSMA) PET with multiparametric MRI (mpMRI) in the diagnosis of pretreatment prostate cancer (PCa).

METHODS

Pubmed, Embase, Medline, Web of Science, and Cochrane Library were searched for eligible studies published before June 22, 2022. We assessed risk of bias and applicability by using QUADAS-2 tool. Data synthesis was performed with Stata 17.0 software, using the "midas" and "meqrlogit" packages.

RESULTS

We included 29 articles focusing on primary cancer detection, 18 articles about primary staging, and two articles containing them both. For PSMA PET versus mpMRI in primary PCa detection, sensitivities and specificities in the per-patient analysis were 0.90 and 0.84 (p<0.0001), and 0.66 and 0.60 (p <0.0001), and in the per-lesion analysis they were 0.79 and 0.78 (p <0.0001), and 0.84 and 0.82 (p <0.0001). For the per-patient analysis of PSMA PET versus mpMRI in primary staging, sensitivities and specificities in extracapsular extension detection were 0.59 and 0.66 (p =0.005), and 0.79 and 0.76 (p =0.0074), and in seminal vesicle infiltration (SVI) detection they were 0.51 and 0.60 (p =0.0008), and 0.93 and 0.96 (p =0.0092). For PSMA PET versus mpMRI in lymph node metastasis (LNM) detection, sensitivities and specificities in the per-patient analysis were 0.68 and 0.46 (p <0.0001), and 0.91 and 0.90 (p =0.81), and in the per-lesion analysis they were 0.67 and 0.36 (p <0.0001), and 0.99 and 0.99 (p =0.18).

CONCLUSION

PSMA PET has higher diagnostic value than mpMRI in the detection of primary PCa. Regarding the primary staging, mpMRI has potential advantages in SVI detection, while PSMA PET has relative advantages in LNM detection.

CLINICAL RELEVANCE STATEMENT

The integration of prostate-specific membrane antigen (PSMA) PET into the diagnostic pathway may be helpful for improving the accuracy of prostate cancer detection. However, further studies are needed to address the cost implications and evaluate its utility in specific patient populations or clinical scenarios. Moreover, we recommend the combination of PSMA PET and mpMRI for cancer staging.

KEY POINTS

• Prostate-specific membrane antigen PET has higher sensitivity and specificity for primary tumor detection in prostate cancer compared to multiparametric MRI. • Prostate-specific membrane antigen PET also has significantly better sensitivity and specificity for lymph node metastases of prostate cancer compared to multiparametric MRI. • Multiparametric MRI has better accuracy for extracapsular extension and seminal vesicle infiltration compared to ate-specific membrane antigen PET.

A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice

Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.

Pitfalls in Prostate MR Imaging Interpretation

Multiparametric MR imaging of the prostate is an essential diagnostic study in the evaluation of prostate cancer. Several entities including normal anatomic structures, benign lesions, and posttreatment changes can mimic prostate cancer. An in depth understanding of the pitfalls is important for accurate interpretation of prostate MR imaging.

Prostate zones and tumor morphological parameters on magnetic resonance imaging for predicting the tumor-stage diagnosis of prostate cancer

PURPOSE

To determine whether the morphological parameters of prostate zones and tumors on magnetic resonance imaging (MRI) can predict the tumor-stage (T-stage) of prostate cancer (PCa) and establish an optimal T-stage diagnosis protocol based on three-dimensional reconstruction and quantization after image segmentation.

METHODS

A dataset of the prostate MRI scans and clinical data of 175 patients who underwent biopsy and had pathologically proven PCa from January 2018 to November 2020 was retrospectively analyzed. The authors manually segmented and measured the volume, major axis, and cross-sectional area of the peripheral zone (PZ), transition zone, central zone (CZ), anterior fibromuscular stroma, and tumor. The differences were evaluated by the One-Way analysis of variance, Pearson's chi-squared test, or independent samples t-test. Spearman's correlation coefficient and receiver operating characteristic curve analyses were also performed. The cut-off values of the T-stage diagnosis were generated using Youden's J index.

RESULTS

The prostate volume (PV), PZ volume (PZV), CZ volume, tumor's major axis (TA), tumor volume (TV), and volume ratio of the TV and PV were significantly different among stages T1 to T4. The cut-off values of the PV, PZV, CZV, TA, TV, and the ratio of TV/PV for the discrimination of the T1 and T2 stages were 53.63 cm3, 11.60 cm3, 1.97 cm3, 2.30 mm, 0.90 cm3, and 0.03 [area under the curves (AUCs): 0.628, 0.658, 0.610, 0.689, 0.724, and 0.764], respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T2 and T3 stages were 2.80 mm, 8.29 cm3, and 0.12 (AUCs: 0.769, 0.702, and 0.688), respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T3 and T4 stages were 4.17 mm, 18.71 cm3, and 0.22 (AUCs: 0.674, 0.709, and 0.729), respectively.

CONCLUSION

The morphological parameters of the prostate zones and tumors on the MRIs are simple and valuable diagnostic factors for predicting the T-stage of patients with PCa, which can help make accurate diagnoses and lateral treatment decisions.

Morphologic perfusion patterns and PI-RADSv2.1 in transition zone prostate cancer

PURPOSE

To evaluate morphologic perfusion patterns in transition zone prostate cancer in multiparametric MRI controlled by in-bore MRI-guided prostate biopsy.

METHODS

Two experienced radiologists evaluated MRI perfusion patterns in consensus from 321 biopsy cores from the transition zone in 141 patients. Transition zone cancer was present in 77 cores in 36 patients. Single early-phase perfusion images were evaluated separately for the presence of a transition zone prostate cancer (consensus tumor early perfusion). The proposed criteria for the perfusion pattern (asymmetry, signal strength, and homogeneity) were rated in consensus for each biopsy position in the presence of the T2w images including the markers of the biopsy trace. We analyzed receiver operating characteristic curves for the PI-RADSv2.1 score and the proposed perfusion pattern.

RESULTS

A logistic regression model with PI-RADSv2.1 and perfusion patterns in early perfusion imaging improved the model fit significantly compared to a model containing only PI-RADSv2.1 (Likelihood Ratio Test, LR = 14.5, p < .001). The AUC was 0.96 for the multiple regression model compared to 0.92 for the PI-RADSv2.1 alone. The evaluation of homogeneity in single early-enhancement images is not inferior compared to the conventional DCE parameter of PI-RADSv2.1 (AUC 0.84 versus 0.83).

CONCLUSION

Morphologic perfusion patterns significantly improve the diagnostic performance of PI-RADSv2.1 in TZ prostate cancer.

Transitional zone prostate cancer: Performance of texture-based machine learning and image-based deep learning

This study is aimed to explore the performance of texture-based machine learning and image-based deep-learning for enhancing detection of Transitional-zone prostate cancer (TZPCa) in the background of benign prostatic hyperplasia (BPH), using a one-to-one correlation between prostatectomy-based pathologically proven lesion and MRI. Seventy patients confirmed as TZPCa and twenty-nine patients confirmed as BPH without TZPCa by radical prostatectomy. For texture analysis, a radiologist drew the region of interest (ROI) for the pathologically correlated TZPCa and the surrounding BPH on T2WI. Significant features were selected using Least Absolute Shrinkage and Selection Operator (LASSO), trained by 3 types of machine learning algorithms (logistic regression [LR], support vector machine [SVM], and random forest [RF]) and validated by the leave-one-out method. For image-based machine learning, both TZPCa and BPH without TZPCa images were trained using convolutional neural network (CNN) and underwent 10-fold cross validation. Sensitivity, specificity, positive and negative predictive values were presented for each method. The diagnostic performances presented and compared using an ROC curve and AUC value. All the 3 Texture-based machine learning algorithms showed similar AUC (0.854-0.861)among them with generally high specificity (0.710-0.775). The Image-based deep learning showed high sensitivity (0.946) with good AUC (0.802) and moderate specificity (0.643). Texture -based machine learning can be expected to serve as a support tool for diagnosis of human-suspected TZ lesions with high AUC values. Image-based deep learning could serve as a screening tool for detecting suspicious TZ lesions in the context of clinically suspected TZPCa, on the basis of the high sensitivity.

The Impact of Prostate Volume on the Prostate Imaging and Reporting Data System (PI-RADS) in a Real-World Setting

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a new cornerstone in the diagnostic pathway of prostate cancer. However, mpMRI is not devoid of factors influencing its detection rate of clinically significant prostate cancer (csPCa). Amongst others, prostate volume has been demonstrated to influence the detection rates of csPCa. Particularly, increasing volume has been linked to a reduced cancer detection rate. However, information about the linkage between PI-RADS, prostate volume and detection rate is relatively sparse. Therefore, the current study aims to assess the association between prostate volume, PI-RADS score and detection rate of csP-Ca, representing daily practice and contemporary mpMRI expertise. Thus, 1039 consecutive patients with 1151 PI-RADS targets, who underwent mpMRI-guided prostate biopsy at our tertiary referral center, were included. Prior mpMRI had been assessed by a plethora of 111 radiology offices, including academic centers and private practices. mpMRI was not secondarily reviewed in house before biopsy. mpMRI-targeted biopsy was performed by a small group of a total of ten urologists, who had performed at least 100 previous biopsies. Using ROC analysis, we defined cut-off values of prostate volume for each PI-RADS score, where the detection rate drops significantly. For PI-RADS 4 lesions, we found a volume > 61.5 ccm significantly reduced the cancer detection rate (OR 0.24; 95% CI 0.16-0.38; p < 0.001). For PI-RADS 5 lesions, we found a volume > 51.5 ccm to significantly reduce the cancer detection rate (OR 0.39; 95% CI 0.25-0.62; p < 0.001). For PI-RADS 3 lesions, none of the evaluated clinical parameters had a significant impact on the detection rate of csPCa. In conclusion, we show that enlarged prostate volume represents a major limitation in the daily practice of mpMRI-targeted biopsy. This study is the first to define exact cut-off values of prostate volume to significantly impair the validity of PI-RADS assessed in a real-world setting. Therefore, the results of mpMRI-targeted biopsy should be interpreted carefully, especially in patients with prostate volumes above our defined thresholds.

Prevalence and grade of malignancy differences with respect to the area of involvement in multiparametric resonance imaging of the prostate in the diagnosis of prostate cancer using the PI-RADS version 2 classification

PURPOSE

The peripheral zone is histologically different from the transitional zone. The aim of this study is to analyze the differences between the prevalence and grade of malignancy of mpMRI-targeted biopsies that involve the TZ with respect to the PZ.

METHODS

A cross-sectional study of 597 men evaluated for PC screening between February 2016 and October 2022 was conducted. Exclusion criteria were prior BPH surgery, radiotherapy, 5-alpha-reductase inhibitors treatment, UTI, mixed involvement of PZ-TZ or doubts, and central-zone involvement. Hypothesis contrast test was used to study differences proportions of malignancy (ISUP > 0) and significant (ISUP > 1) and high-grade tumor (ISUP > 3) in PI-RADSv2 > 2-targeted biopsies in PZ with respect to TZ, and logistic regression and hypothesis contrast tests were used to study the influence of the area of exposure as an effect-modifying factor in the diagnosis of malignancy with respect to the PI-RADSv2 classification.

RESULTS

473 patients were selected and 573 lesions biopsied (127 PI-RADS3, 346 PI-RADS4 and 100 PI-RADS5). A significant increase was described in the proportion of malignancy and significant and high-grade tumor in PZ compared to TZ (22.6%, 21.3%, and 8.7%, respectively). Significant increase in proportions and malignancy were described in cores targeted to PZ with respect to TZ, highlight the differences between PZ and TZ for ST (37.3%vs23.7% for PI-RADS4, 69.2%vs27.3% for PI-RADS5, respectively). Statistically significant linear trend was described increasing for malignancy, significant and high-grade tumors with respect to the PI-RADSv2 scores (change > 10%).

CONCLUSION

Although the prevalence and grade of malignancy in the TZ is lower than in the PZ, PI-RADS4 and 5-targeted biopsies should not be omitted in this location, but PI-RADS3 could be.

Differences in the pathogenetic characteristics of prostate cancer in the transitional and peripheral zones and the possible molecular biological mechanisms

Since the theory of modern anatomical partitioning of the prostate was proposed, the differences in the incidence and pathological parameters of prostate cancer between the peripheral zone and transition zone have been gradually revealed. It suggests that there are differences in the pathogenic pathways and molecular biology of prostate cancer between different regions of origin. Over the past decade, advances in sequencing technologies have revealed more about molecules, genomes, and cell types specific to the peripheral and transitional zones. In recent years, the innovation of spatial imaging and multiple-parameter magnetic resonance imaging has provided new technical support for the zonal study of prostate cancer. In this work, we reviewed all the research results and the latest research progress in the study of prostate cancer in the past two decades. We summarized and proposed several vital issues and focused directions for understanding the differences between peripheral and transitional zones in prostate cancer.

Correlation between ADC, ADC ratio, and Gleason Grade group in prostate cancer patients undergoing radical prostatectomy: Retrospective multicenter study with different MRI scanners

Background

MRI is an important tool in the prostate cancer work-up, with special emphasis on the ADC sequence. This study aimed to investigate the correlation between ADC and ADC ratio compared to tumor aggressiveness determined by a histopathological examination after radical prostatectomy.

Methods

Ninety-eight patients with prostate cancer underwent MRI at five different hospitals prior to radical prostatectomy. Images were retrospectively analyzed individually by two radiologists. The ADC of the index lesion and reference tissues (contralateral normal prostatic, normal peripheral zone, and urine) was recorded. Absolute ADC and different ADC ratios were compared to tumor aggressivity according to the ISUP Gleason Grade Groups extracted from the pathology report using Spearman's rank correlation coefficient (ρ). ROC curves were used to evaluate the ability to discriminate between ISUP 1-2 and ISUP 3-5 and intra class correlation and Bland-Altman plots for interrater reliability.

Results

All patients had prostate cancer classified as ISUP grade ≥ 2. No correlation was found between ADC and ISUP grade. We found no benefit of using the ADC ratio over absolute ADC. The AUC for all metrics was close to 0.5, and no threshold could be extracted for prediction of tumor aggressivity. The interrater reliability was substantial to almost perfect for all variables analyzed.

Conclusions

ADC and ADC ratio did not correlate with tumor aggressiveness defined by ISUP grade in this multicenter MRI study. The result of this study is opposite to previous research in the field.

Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate

BACKGROUND

There is an overlap comparing transition zone prostate cancer (TZ PCa) and benign prostatic hyperplasia (BPH) on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), creating additional challenges for assessment of TZ tumors on MRI.

PURPOSE

To evaluate whether amide proton transfer-weighted (APTw) imaging provides new diagnostic ideas for TZ PCa.

STUDY TYPE

Prospective.

POPULATION

A total of 51 TZ PCa patients (age, 49-89), 44 stromal BPH (age, 57-92), and 45 glandular BPH patients (age, 56-92).

FIELD STRENGTH/SEQUENCE

A 3 T; T2WI turbo spin echo (TSE), quantitative T2*-weighted imaging, DWI echo planar imaging, 3D APTw TSE.

ASSESSMENT

Differences in APTw, apparent diffusion coefficient (ADC), and T2* among three lesions were compared by one-way analysis of variance (ANOVA). Regions of interest were drawn by two radiologists (X.Q.Z. and X.Y.Q., with 21 and 15 years of experience, respectively).

STATISTICAL TESTS

Multivariable logistic regression analyses; ANOVA with post hoc testing; receiver operator characteristic curve analysis; Delong test. Significance level: P < 0.05.

RESULTS

APTw among TZ PCa, stromal BPH, and glandular BPH (3.48% ± 0.83% vs. 2.76% ± 0.49% vs. 2.72% ± 0.45%, respectively) were significantly different except between stromal BPH and glandular BPH (P > 0.99). Significant differences were found in ADC (TZ PCa 0.76 ± 0.16 × 10^-3  mm² /sec vs. stromal BPH 0.91 ± 0.14 × 10^-3  mm² /sec vs. glandular BPH 1.08 ± 0.18 × 10^-3  mm² /sec) among three lesions. APTw (OR = 12.18, 11.80, respectively) and 1/ADC (OR = 703.87, 181.11, respectively) were independent predictors of TZ PCa from BPH and stromal BPH. The combination of APTw and ADC had better diagnostic performance in the identification of TZ PCa from BPH and stromal BPH.

DATA CONCLUSION

APTw imaging has the potential to be of added value to ADC in differentiating TZ PCa from BPH and stromal BPH.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

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.

PI-RADS version 2.1 for the evaluation of transition zone lesions: a practical guide for radiologists

Multiparametric MRI has been established as the most accurate non-invasive diagnostic imaging tool for detecting prostate cancer (PCa) in both the peripheral zone and the transition zone (TZ) using the PI-RADS (Prostate Imaging Reporting and Data System) v. 2.1 released in 2019 as a guideline to reporting. TZ PCa remains the most difficult to diagnose due to a markedly heterogeneous background and a wide variety of atypical imaging presentations as well as other anatomical and pathological processes mimicking PCa. The aim of this paper was to present a spectrum of PCa in the TZ, as a guide for radiologists.

Prostate zones and cancer: lost in transition?

Localized prostate cancer shows great clinical, genetic and environmental heterogeneity; however, prostate cancer treatment is currently guided solely by clinical staging, serum PSA levels and histology. Increasingly, the roles of differential genomics, multifocality and spatial distribution in tumorigenesis are being considered to further personalize treatment. The human prostate is divided into three zones based on its histological features: the peripheral zone (PZ), the transition zone (TZ) and the central zone (CZ). Each zone has variable prostate cancer incidence, prognosis and outcomes, with TZ prostate tumours having better clinical outcomes than PZ and CZ tumours. Molecular and cell biological studies can improve understanding of the unique molecular, genomic and zonal cell type features that underlie the differences in tumour progression and aggression between the zones. The unique biology of each zonal tumour type could help to guide individualized treatment and patient risk stratification.

Relationship of prostate cancer topography and tumour conspicuity on multiparametric magnetic resonance imaging: a protocol for a systematic review and meta-analysis

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI) has improved the triage of men with suspected prostate cancer, through precision prebiopsy identification of clinically significant disease. While multiple important characteristics, including tumour grade and size have been shown to affect conspicuity on mpMRI, tumour location and association with mpMRI visibility is an underexplored facet of this field. Therefore, the objective of this systematic review and meta-analysis is to collate the extant evidence comparing MRI performance between different locations within the prostate in men with existing or suspected prostate cancer. This review will help clarify mechanisms that underpin whether a tumour is visible, and the prognostic implications of our findings.

METHODS AND ANALYSIS

The databases MEDLINE, PubMed, Embase and Cochrane will be systematically searched for relevant studies. Eligible studies will be full-text English-language articles that examine the effect of zonal location on mpMRI conspicuity. Two reviewers will perform study selection, data extraction and quality assessment. A third reviewer will be involved if consensus is not achieved. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines will inform the methodology and reporting of the review. Study bias will be assessed using a modified Newcastle-Ottawa scale. A thematic approach will be used to synthesise key location-based factors associated with mpMRI conspicuity. A meta-analysis will be conducted to form a pooled value of the sensitivity and specificity of mpMRI at different tumour locations.

ETHICS AND DISSEMINATION

Ethical approval is not required as it is a protocol for a systematic review. Findings will be disseminated through peer-reviewed publications and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42021228087.

Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole mount histopathology

In this study, the objective was to characterize the MR signatures of the various benign prostate tissues and to differentiate them from cancer. Data was from seventy prostate cancer patients who underwent multiparametric MRI (mpMRI) and subsequent prostatectomy. The scans included T2-weighted imaging (T2W), diffusion weighted imaging, dynamic contrast-enhanced MRI (DCE MRI), and MR spectroscopic imaging. Histopathology tissue information was translated to MRI images. The mpMRI parameters were characterized separately per zone and by tissue type. The tissues were ordered according to trends in tissue parameter means. The peripheral zone tissue order was cystic atrophy, high grade prostatic intraepithelial neoplasia (HGPIN), normal, atrophy, inflammation, and cancer. Decreasing values for tissue order were exhibited by ADC (1.8 10^-3 mm²/s to 1.2 10^-3 mm²/s) and T2W intensity (3447 to 2576). Increasing values occurred for DCE MRI peak (143% to 157%), DCE MRI slope (101%/min to 169%/min), fractional anisotropy (FA) (0.16 to 0.19), choline (7.2 to 12.2), and choline / citrate (0.3 to 0.9). The transition zone tissue order was cystic atrophy, mixed benign prostatic hyperplasia (BPH), normal, atrophy, inflammation, stroma, anterior fibromuscular stroma, and cancer. Decreasing values occurred for ADC (1.6 10^-3 mm²/s to 1.1 10^-3 mm²/s) and T2W intensity (2863 to 2001). Increasing values occurred for DCE MRI peak (143% to 150%), DCE MRI slope (101%/min to 137%/min), FA (0.18 to 0.25), choline (7.9 to 11.7), and choline / citrate (0.3 to 0.7). Logistic regression was used to create parameter model fits to differentiate cancer from benign prostate tissues. The fits achieved AUCs ≥0.91. This study quantified the mpMRI characteristics of benign prostate tissues and demonstrated the capability of mpMRI to discriminate among benign as well as cancer tissues, potentially aiding future discrimination of cancer from benign confounders.

Comparison between 18 F-DCFPyL PET and MRI for the detection of transition zone prostate cancer

BACKGROUND

We aimed to compare the diagnostic performance of ¹⁸ F-DCFPyL positron emission tomography (PET) and multiparameter magnetic resonance imaging (mp-MRI) in detecting transition zone (TZ) prostate cancer (PCa).

METHODS

This retrospective study included 20 patients who underwent ¹⁸ F-DCFPyL PET/MRI and 32 patients who underwent ¹⁸ F-DCFPyL PET/CT and MRI from January 2019 to June 2020. All patients had TZ lesions and underwent prostate biopsies. One senior (reader 1) and one junior (reader 2) nuclear medicine physician evaluated each TZ lesion independently, according to the molecular imaging prostate-specific membrane antigen scoring system and the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1). The histologic diagnosis of prostate biopsy was used as the reference standard. The diagnostic performance of the two methods was compared in terms of inter-reader agreement and area under the receiver operating characteristic (AUC-ROC) curve.

RESULTS

Of the 52 patients, 43 had TZ PCa. For inter-reader agreement, the kappa value was 0.883 for ¹⁸ F-DCFPyL PET and 0.393 for mp-MRI. For PET, both readers had the same diagnostic sensitivity, specificity, and accuracy of 93.0%, 77.8%, and 90.4%, respectively. For mp-MRI, the diagnostic sensitivity, specificity, and accuracy was 67.4%, 33.3%, and 61.5% for reader 1, and 51.2%, 44.4%, and 51.9% for reader 2, respectively. PET outperformed mp-MRI for both readers with an AUC of 0.872 for PET versus 0.584 for mp-MRI, p = .0209 for reader 1, and an AUC of 0.860 for PET versus 0.505 for mp-MRI, p = .0213 for reader 2. Among the 43 patients with TZ PCa, ¹⁸ F-DCFPyL PET detected a distant bone metastasis missed by the CT in one case and two small lymph node metastases missed by the CT and MRI in another case.

CONCLUSIONS

These results suggest that ¹⁸ F-DCFPyL PET, which was almost independent of the experience of the readers, was more objective in the evaluation of TZ lesions, and had higher diagnostic value than mp-MRI.

Diagnostic accuracy of 68 Ga-PSMA PET/MRI and multiparametric MRI in detecting index tumours in radical prostatectomy specimen

OBJECTIVE

To evaluate the diagnostic accuracy of the ⁶⁸ gallium (⁶⁸ Ga) prostate-specific membrane antigen (PSMA) positron emission tomography/magnetic resonance imaging (PET/MRI) and multiparametric MRI (mpMRI) by region-based comparison of index tumour localisations using histopathological tumour maps of patients who underwent radical prostatectomy because of clinically significant prostate cancer.

PATIENTS AND METHODS

The study included 64 patients who underwent radical prostatectomy after primary staging with mpMRI and ⁶⁸ Ga-PSMA PET/MRI. Diagnostic analysis was performed by dividing the prostate into four anatomic regions as left/right anterior and left/right posterior. The extension of the lesions in mpMRI and the pathological uptake in ⁶⁸ Ga-PSMA PET/MRI were matched separately for each region with the extension of the index tumour into each region.

RESULTS

The sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and the accuracy of mpMRI and ⁶⁸ Ga-PSMA PET/MRI are shown as 55.7%, 91.8%, 80.6%, 77.2%, 78.1%, and 60.8%, 94.3%, 86.8% 79.8%, 83.5%, respectively. ⁶⁸ Ga-PSMA PET/MRI has higher sensitivity and specificity compared with mpMRI. However, no statistically significant difference was found (P = .464). Combined imaging had significantly higher diagnostic accuracy compared with mpMRI and ⁶⁸ Ga-PSMA PET/MRI (change in AUC: 0.084 and 0.046, P < .001 and P = .028, respectively), while no statistically significant difference was found between mpMRI and ⁶⁸ Ga-PSMA PET/MRI (change in AUC: 0.038, P = .246).

CONCLUSION

⁶⁸ Ga-PSMA PET/MRI had higher clinical diagnostic accuracy in prostate cancer compared with mpMRI. Diagnostic accuracy was significantly increased in the combined use of both imaging modalities.

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

Background

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

Methods

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

Results

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

Conclusion

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

Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate

Background

Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment.

Purpose

To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI).

Material and methods

62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm²) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SF_slow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models.

Results

All three models showed a significant difference (p<0.0001) between peripheral zone (PZ) tumor and normal tissue ROIs, but not between non-PZ tumor and BPH ROIs. The area under the receiver operating characteristics curve distinguishing tumor from prostate voxels was 0.956, 0.949 and 0.949 for the two-component, bi-exponential and mono-exponential models, respectively. The corresponding Spearman correlation coefficients between tumor values and Gleason Grade Group were fair (0.370, 0.499 and -0.490), but not significant.

Conclusion

Signal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models.

Diagnostic Performance of PI-RADS v2, Proposed Adjusted PI-RADS v2 and Biparametric Magnetic Resonance Imaging for Prostate Cancer Detection: A Preliminary Study

Purpose: To evaluate the diagnostic performance of PI-RADS v2, proposed adjustments to PI-RADS v2 (PA PI-RADS v2) and biparametric magnetic resonance imaging (MRI) for prostate cancer detection. Methods: A retrospective cohort of 224 patients with suspected prostate cancer was included from January 2016 to November 2018. All the patients underwent a multi-parametric MR scan before biopsy. Two radiologists independently evaluated the MR examinations using PI-RADS v2, PA PI-RADS v2, and a biparametric MRI protocol, respectively. Receiver operating characteristic (ROC) curves for the three different protocols were drawn. Results: In total, 90 out of 224 cases (40.18%) were pathologically diagnosed as prostate cancer. The area under the ROC curves (AUC) for diagnosing prostate cancers by biparametric MRI, PI-RADS v2, and PA PI-RADS v2 were 0.938, 0.935, and 0.934, respectively. For cancers in the peripheral zone (PZ), the diagnostic sensitivity was 97.1% for PI-RADS v2/PA PI-RADS v2 and 96.2% for biparametric MRI. Moreover, the specificity was 84.0% for biparametric MRI and 58.0% for PI-RADS v2/PA PI-RADS v2. For cancers in the transition zone (TZ), the diagnostic sensitivity was 93.4% for PA PI-RADS v2 and 88.2% for biparametric MRI/PI-RADS v2. Furthermore, the specificity was 95.4% for biparametric MRI/PI-RADS v2 and 78.0% for PA PI-RADS v2. Conclusions: The overall diagnostic performance of the three protocols showed minimal differences. For lesions assessed as being category 3 using the biparametric MRI protocol, PI-RADS v2, or PA PI-RADS v2, it was thought prostate cancer detection could be improved. Attention should be paid to false positive results when PI-RADS v2 or PA PI-RADS v2 are used.

The utility of ADC parameters in the diagnosis of clinically significant prostate cancer by 3.0-Tesla diffusion-weighted magnetic resonance imaging

Purpose

This study has focused on investigating the relationship between the exponential apparent diffusion coefficient (exp-ADC), selective apparent diffusion coefficient (sel-ADC) values, the ADC ratio (ADCr), and prostate cancer aggressiveness with transrectal ultrasound-guided prostate biopsy in patients with prostate cancer.

Material and methods

All patients underwent a multiparametric magnetic resonance imaging (mpMRI) including tri-planar T2-weighted (T2W), dynamic contrast-enhanced (DCE), diffusion-weighted sequences using a 3.0-Tesla MR scanner (Skyra, Siemens Medical Systems, Germany) with a dedicated 18-channel body coil and a spine coil underneath the pelvis, with the patient in the supine position. Exp-ADC, sel-ADC, and ADCr of defined lesions were evaluated using region-of-interest-based measurements. Exp-ADC, sel-ADC, and ADCr were correlated with the Gleason score obtained through transrectal ultrasound-guided biopsy.

Results

Patients were divided into 2 groups. Group I is Gleason score ≥ 3 + 4, group II is Gleason score = 6. Sel-ADC and exp-ADC were statistically significant between 2 groups (0.014 and 0.012, respectively). However, the ADCr difference between nonclinical significant prostate cancer from clinically significant prostate cancer was not significant (p = 0.09).

Conclusions

This study is the first to evaluate exp-ADC and sel-ADC values of prostate carcinoma with ADCr. One limitation of this study might be the limited number of patients. Exp-ADC and sel-ADC values in prostate MRI imaging improved the specificity, accuracy, and area under the curve (AUC) for detecting clinically relevant prostate carcinoma. Adding exp-ADC and sel-ADC values to ADCr can be used to increase the diagnostic accuracy of DWI.

Detection of clinically significant prostate cancer with 18F-DCFPyL PET/multiparametric MR

PURPOSE

To assess whether ¹⁸F-DCFPyL PET/multiparametric (mp)MR contributes to the diagnosis of clinically significant (cs) prostate cancer (PCa) compared to mpMR in patients with suspicion of PCa, or patients being considered for focal ablative therapies (FT).

PATIENTS AND METHODS

This ethics review board-approved, prospective study included 55 men with suspicion of PCa and negative systematic biopsies or clinically discordant low-risk PCa (n = 21) or those being considered for FT (n = 34) who received ¹⁸F-DCFPyL PET/mpMR. Each modality, PET, mpMR, and PET/MR (using the PROMISE classification), was assessed independently. All suspicious lesions underwent PET/MR-ultrasound fusion biopsies.

RESULTS

There were 45/55 patients (81.8%) that had histologically proven PCa and 41/55 (74.5%) were diagnosed with csPCa. Overall, 61/114 lesions (53.5%) identified on any modality were malignant; 49/61 lesions (80.3%) were csPCa. On lesion-level analysis, for detection of csPCa, the sensitivity of PET was higher than that of mpMR and PET/MR (86% vs 67% and 69% [p = 0.027 and 0.041, respectively]), but at a lower specificity (32% vs 85% and 86%, respectively [p < 0.001]). The performance of MR and PET/MR was comparable. For identification of csPCa in PI-RADS ≥ 3 lesions, the AUC (95% CI) for PET, mpMR, and PET/MR was 0.75 (0.65-0.86), 0.69 (0.56-0.82), and 0.78 (0.67-0.89), respectively. The AUC for PET/MR was significantly larger than that of mpMR (p = 0.04).

CONCLUSION

PSMA PET detects more csPCa than mpMR, but at low specificity. The performance PET/MR is better than mpMR for detection of csPCa in PI-RADS ≥ 3 lesions.

CLINICAL REGISTRATION

NCT03149861.

Classification of Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning From Disease-Related Images

Purpose

The diagnosis of prostate transition zone cancer (PTZC) remains a clinical challenge due to their similarity to benign prostatic hyperplasia (BPH) on MRI. The Deep Convolutional Neural Networks (DCNNs) showed high efficacy in diagnosing PTZC on medical imaging but was limited by the small data size. A transfer learning (TL) method was combined with deep learning to overcome this challenge.

Materials and methods

A retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (nine patients). Using T2-weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps, DCNN models were trained and compared between different TL databases (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning, or transductive transferring).

Results

PTZC and BPH can be classified through traditional DCNN. The efficacy of TL from natural images was limited but improved by transferring knowledge from the disease-related images. Furthermore, transductive TL from disease-related images had comparable efficacy to the fine-tuning method. Limitations include retrospective design and a relatively small sample size.

Conclusion

Deep TL from disease-related images is a powerful tool for an automated PTZC diagnostic system. In developing regions where only conventional MR scans are available, the accurate diagnosis of PTZC can be achieved via transductive deep TL from disease-related images.

Prostate Multiparametric MRI: Common Pitfalls in Primary Diagnosis and How to Avoid Them

Purpose of Review

To provide the radiologist with basic knowledge about normal and abnormal findings in the prostatic mp-MRI, taking a look at the possible diagnostic pitfalls commonly seen in daily clinical practice, allowing him to recognize and consequently avoid them.

Recent Findings

Prostate mp-MRI has now become commonly used in most diagnostic imaging centers, as a precise, accurate and above all non-invasive tool, useful in the diagnosis, staging and follow-up of prostate diseases, first of all prostatic carcinoma. For this reason, it is important to take into account the existence of numerous possible anatomic and pathologic processes which can mimick or masquerade as prostate cancer.

Summary

Through the combination of anatomical (T2WI) and functional sequences (DWI/ADC and DCE), the mp-MRI of the prostate provides all the information necessary for a correct classification of patients with prostate disease, cancer in particular. It is not uncommon, however, for the radiologist to make errors in the interpretation of imaging due to conditions, pathological or otherwise, that mimic prostate cancer and that, consequently, affect the diagnostic/therapeutic process of patients. The strategy, and what this pictorial review aims at, is to learn to recognize the potential pitfalls of the prostatic mp-MRI and avoid them.

Detection and localisation of primary prostate cancer using 68 gallium prostate-specific membrane antigen positron emission tomography/computed tomography compared with multiparametric magnetic resonance imaging and radical prostatectomy specimen pathology

OBJECTIVE

To compare the accuracy of ⁶⁸ gallium prostate-specific membrane antigen positron emission tomography/computed tomography (⁶⁸ Ga-PSMA PET/CT) with multiparametric MRI (mpMRI) in detecting and localising primary prostate cancer when compared with radical prostatectomy (RP) specimen pathology.

PATIENTS AND METHODS

Retrospective review of men who underwent ⁶⁸ Ga-PSMA PET/CT and mpMRI for primary prostate cancer before RP across four centres between 2015 and 2018. Patients undergoing imaging for recurrent disease or before non-surgical treatment were excluded. We defined pathological index tumour as the lesion with highest International Society of Urological Pathology Grade Group (GG) on RP specimen pathology. Our primary outcomes were rates of accurate detection and localisation of RP specimen pathology index tumour using ⁶⁸ Ga-PSMA PET/CT or mpMRI. We defined tumour detection as imaging lesion corresponding with RP specimen tumour on any imaging plane, and localisation as imaging lesion matching RP specimen index tumour in all sagittal, axial, and coronal planes. Secondary outcomes included localisation of clinically significant and transition zone (TZ) index tumours. We defined clinically significant disease as GG 3-5. We used descriptive statistics and the Mann-Whitney U-test to define and compare demographic and pathological characteristics between detected, missed and localised tumours using either imaging modality. We used the McNemar test to compare detection and localisation rates using ⁶⁸ Ga-PSMA PET/CT and mpMRI.

RESULTS

In all, 205 men were included in our analysis, including 133 with clinically significant disease. There was no significant difference between ⁶⁸ Ga-PSMA PET/CT and mpMRI in the detection of any tumour (94% vs 95%, P > 0.9). There was also no significant difference between localisation of all index tumours (91% vs 89%, P = 0.47), clinically significant index tumours (96% vs 91%, P = 0.15) or TZ tumours (85% vs 80%, P > 0.9) using ⁶⁸ Ga-PSMA PET/CT and mpMRI. Limitations include retrospective study design and non-central review of imaging and pathology.

CONCLUSION

We found no significant difference in the detection or localisation of primary prostate cancer between ⁶⁸ Ga-PSMA PET/CT and mpMRI. Further prospective studies are required to evaluate a combined PET/MRI model in minimising tumours missed by either modality.

Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept

Background: Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and diffusion weighted imaging (DWI) sequences. Datasets were analyzed both by human readers and by a fully automated artificial intelligence (AI) software using deep learning (DL). Agreement between the algorithm and the reports—serving as the ground truth—was compared on a per-case and per-lesion level using metrics of diagnostic accuracy and k statistics; Results: The DL method yielded an 87% sensitivity (33/38) and 50% specificity (5/10) with a k of 0.42. 12/28 (43%) Prostate Imaging Reporting and Data System (PI-RADS) 3, 16/22 (73%) PI-RADS 4, and 5/5 (100%) PI-RADS 5 lesions were detected compared to the ground truth. Targeted biopsy revealed PCa in six participants, all correctly diagnosed by both the human readers and AI. Conclusions: The results of our study show that in our AI-assisted, image-guided prostate cancer screening the software solution was able to identify highly suspicious lesions and has the potential to effectively guide the targeted-biopsy workflow.

Differentiation of prostate cancer and benign prostatic hyperplasia: comparisons of the histogram analysis of intravoxel incoherent motion and monoexponential model with in-bore MR-guided biopsy as pathological reference

PURPOSE

To evaluate the diagnostic performance of histogram analysis of intravoxel incoherent motion (IVIM) parameters for differentiating prostate cancer (PCa) from benign prostatic hyperplasia (BPH), and compare with the monoexponential model, with in-bore MR-guided biopsy as pathological reference.

METHODS

Thirty patients were included in this study. DWI images were processed with Matlab R2015b software by IVIM and monoexponential model for quantitation of diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient (ADC). The multiparametric data were compared between PCa and BPH group. Correlations between parameters and Gleason scores of PCa were assessed with Spearman rank test. ROC analysis was used to evaluate and compare the diagnostic ability of each parameter for discriminating PCa from BPH. Logistic regression model was used to evaluate the diagnostic performance of combination of different histogram parameters.

RESULTS

Sixteen PCa lesions and 20 BPH nodules were analyzed in this study. For IVIM-derived D, the histogram mean, 75th, 90th, and max of PCa were significantly lower than BPH. PCa had significantly lower min and 10th D* than BPH. For f, histogram mean, min, 10th, 25th, 50th, 75th, 90th, max and skew showed significant differences between PCa and BPH. For ADC, PCa were significantly lower than BPH in terms of histogram mean, min, 10th, 25th, 50th, 75th, 90th, max and kurtosis. Histogram mean D and min, 25th D* show significantly negative correlation with Gleason score (r = - 0.582, - 0.534, - 0.554, respectively). Histogram max D and mean f and min ADC showed higher diagnostic performance than other parameters (AUC = 0.925, 0.881, 0.969, respectively). The IVIM model (combined with max D, min D* and mean f) (AUC = 0.950 [0.821, 0.995]) didn't show significant difference from the monoexponential model (AUC = 0.969 [0.849, 0.999], p = 0.23). Besides, combination of the IVIM and monoexponential model didn't improve diagnostic performance compared with the single model (p = 0.362 and 0.763, respectively).

CONCLUSIONS

Histogram analyses of IVIM and monoexponential model were both useful methods for discriminating PCa from BPH. The diagnostic performance of IVIM model seemed to be not superior to that of monoexponential model. Combination of IVIM and monoexponential model did not add significant information to the single model alone.

Prostate MR: pitfalls and benign lesions

Multiparametric MRI (mpMRI) of the prostate has evolved to be an integral component for the diagnosis, risk stratification, staging, and targeting of prostate cancer. However, anatomic and histologic mimics of prostate cancer on mpMRI exist. Anatomic feature that mimic prostate cancer on mpMRI include anterior fibromuscular stroma, normal central zone, periprostatic venous plexus, and thickened surgical capsule (transition zone pseudocapsule). Benign conditions such as post-biopsy hemorrhage, prostatitis or inflammation, focal prostate atrophy, benign prostatic hyperplasia nodules, and prostatic calcifications can also mimic prostate cancer on mpMRI. Technical challenges and other pitfalls such as image distortion, motion artifacts, and endorectal coil placements can also limit the efficacy of mpMRI. Knowledge of prostate anatomy, location of the lesion and its imaging features on different sequences, and being familiar with the common pitfalls are critical for the radiologists who interpret mpMRI. Therefore, this article reviews the pitfalls (anatomic structures and technical challenges) and benign lesions or abnormalities that may mimic prostate cancer on mpMRI and how to interpret them.

What You Need to Know Before Reading Multiparametric MRI for Prostate Cancer

OBJECTIVE. Multiparametric MRI (mpMRI) has become the main imaging modality for the detection, localization, and local staging of prostate cancer over the past decade. For radiologists to achieve consistent and reproducible reporting of prostate mpMRI, a comprehensive evaluation of the gland including detailed knowledge of anatomy, pathology, and clinical data is required. This article familiarizes radiologists with common pitfalls and conditions that affect mpMRI performance during readouts. CONCLUSION. Consistent, accurate, and reproducible reporting of prostate mpMRI is vital. Additionally, radiologists should be aware of common diagnostic pitfalls that can hinder mpMRI performance.

Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer

OBJECTIVES

To assess the discriminatory power of lexicon terms used in PI-RADS version 2 to describe MRI features of prostate lesions.

METHODS

Four hundred fifty-four patients were included in this retrospective, institutional review board-approved study. Patients received multiparametric (mp) MRI and subsequent prostate biopsy including MRI/transrectal ultrasound fusion biopsy and 10-core systematic biopsy. PI-RADS lexicon terms describing lesion characteristics on mpMRI were assigned to lesions by experienced readers. Positive and negative predictive values (PPV, NPV) of each lexicon term were assessed using biopsy results as a reference standard.

RESULTS

From a total of 501 lesions, clinically significant prostate cancer (csPCa) was present in 175 lesions (34.9%). Terms related to findings of restricted diffusion showed PPVs of up to 52.0%/43.9% and NPV of up to 91.8%/89.7% (peripheral zone or PZ/transition zone or TZ). T2-weighted imaging (T2W)-related terms showed a wide range of predictive values. For PZ lesions, high PPVs were found for "markedly hypointense," "lenticular," "lobulated," and "spiculated" (PPVs between 67.2 and 56.7%). For TZ lesions, high PPVs were found for "water-drop-shaped" and "erased charcoal sign" (78.6% and 61.0%). The terms "encapsulated," "organized chaos," and "linear" showed to be good predictors for benignity with distinctively low PPVs between 5.4 and 6.9%. Most T2WI-related terms showed improved predictive values for TZ lesions when combined with DWI-related findings.

CONCLUSIONS

Lexicon terms with high discriminatory power were identified (e.g., "markedly hypointense," "water-drop-shaped," "organized chaos"). DWI-related terms can be useful for excluding TZ cancer. Combining T2WI- with DWI findings in TZ lesions markedly improved predictive values.

KEY POINTS

• Lexicon terms describing morphological and functional features of prostate lesions on MRI show a wide range of predictive values for prostate cancer. • Some T2-related terms have favorable PPVs, e.g., "water-drop-shaped" and "organized chaos" while others show less distinctive predictive values. DWI-related terms have noticeable negative predictive values in TZ lesions making DWI feature a useful tool for exclusion of TZ cancer. • Combining DWI- and T2-related lexicon terms for assessment of TZ lesions markedly improves PPVs. Most T2-related lexicon terms showed a significant decrease in PPV when combined with negative findings for "DW hyperintensity."

Biparametric versus multiparametric magnetic resonance imaging of the prostate: detection of clinically significant cancer in a perfect match group

Background

Biparametric (bp) magnetic resonance imaging (MRI) could be an alternative MRI for the detection of the clinically significant prostate cancer (csPCa).

Purpose

To compare the accuracies of prostate cancer detection and localization between prebiopsy bpMRI and postbiopsy multiparametric MRI (mpMRI) taken on different days, using radical prostatectomy specimens as the reference standards.

Material and methods

Data of 41 total consecutive patients who underwent the following examinations and procedures between September 2015 and March 2017 were collected: (1) magnetic resonance- and/or ultrasonography-guided biopsy after bpMRI; (2) postbiopsy mpMRI; and (3) radical prostatectomy with csPCa. Two radiologists scored suspected lesions on bpMRI and mpMRI independently using Prostate Imaging Reporting and Data System version 2. The diagnostic accuracy of detecting csPCa and the Dice similarity coefficient were obtained. Apparent diffusion coefficient (ADC) ratios were also obtained for quantitative comparison between bpMRI and mpMRI.

Results

Diagnostic accuracies on bpMRI and mpMRI were 0.83 and 0.82 for reader 1; 0.80 and 0.82 for reader 2. There are no significantly different values of diagnostic sensitivities or specificities between the readers or between MRI protocols. Intra-observer Dice similarity coefficient was significantly lower in reader 2, compared to that in reader 1 between the two MRI protocols. The range of mean ADC ratio was 0.281-0.635. There was no statistically significant difference in the ADC ratio between bpMRI and mpMRI.

Conclusions

Diagnostic performance of bpMRI without dynamic contrast enhancement MRI is not significantly different from mpMRI with dynamic contrast enhancement MRI in the detection of csPCa.

Direct Comparison of PI-RADS Version 2 and 2.1 in Transition Zone Lesions for Detection of Prostate Cancer: Preliminary Experience

BACKGROUND

There appears to be less agreement in the identification of cancers in the transition zone (TZ), which is not as reliable as those in peripheral zone when using the Prostate Imaging Reporting and Data System (PI-RADS) version 2 (v2). In response to such shortcomings, the updated version 2.1 was introduced, which incorporated diffusion-weighted imaging (DWI) into category 2 and clarified lexicons.

PURPOSE

To compare the diagnostic performance for the detection of clinically significant TZ prostate cancers (csPCa) and interreader agreement between PI-RADS v2.1 and v2.

STUDY TYPE

Retrospective study.

POPULATION

In all, 142 patients, 201 TZ lesions.

FIELD STRENGTH/SEQUENCE

3.0T; T₂ -weighted image and DWI.

ASSESSMENT

Lesions were scored by three independent readers using PI-RADS v2 and v2.1.

STATISTICAL TESTS

The sensitivity and specificity at category ≥3 were compared between v2 and v2.1 using the generalized estimating equation model. Detection rates for csPCa of upgraded and downgraded lesions in the use of PI-RADS v2.1 from v2 were assessed. Interreader agreement was assessed using κ statistics.

RESULTS

PI-RADS v2.1 showed a higher sensitivity and specificity (94.5% and 60.9%) than v2 (91.8% and 56.3%) for category ≥3 lesions in the detection of csPCa, although not significantly. Of eight upgraded lesions from category 2 to 3 (2 + 1) with an incorporated DWI, 50% (4/8) were csPCa. This was significantly higher than category 2 lesions (4.4%; P = 0.003). No csPCa was detected among the 22.8% (46/201) downgraded lesions. There was a moderate interreader agreement for scores ≥3 (κ = 0.565) in v2.1, which was slightly higher than that for v2 (κ = 0.534), although not significantly.

DATA CONCLUSION

PI-RADS v2.1 provides moderate and comparable interreader agreement at category ≥3 than v2 in the TZ lesions. Upgraded lesions from category 2 to 3 demonstrated a higher detection rate of csPCa than category 2 lesions in v2.1.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:577-586.

Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer

Purpose: To design and validate a preprocessing procedure dedicated to T2-weighted MR images of lung cancers so as to improve the ability of radiomic features to distinguish between adenocarcinoma and other histological types.

Materials and Methods: A discovery set of 52 patients with advanced lung cancer who underwent T2-weighted MR imaging at 3 Tesla in a single center study from August 2017 to May 2019 was used. Findings were then validated using a validation set of 19 additional patients included from May to October 2019. Tumor type was obtained from the pathology report after trans-thoracic needle biopsy, metastatic lymph node or metastasis samples, or surgical excisions. MR images were preprocessed using N4ITK bias field correction and by normalizing voxel intensities with fat as a reference region. Segmentation and extraction of radiomic features were performed with LIFEx software on the raw images, on the N4ITK-corrected images and on the fully preprocessed images. Two analyses were conducted where radiomic features were extracted: (1) from the whole tumor volume (3D analysis); (2) from all slices encompassing the tumor (2D analysis). Receiver operating characteristic (ROC) analysis was used to identify features that could distinguish between adenocarcinoma and other histological types. Sham experiments were also designed to control the number of false positive findings.

Results: There were 31 (12) adenocarcinomas and 21 (7) other histological types in the discovery (validation) set. In 2D, preprocessing increased the number of discriminant radiomic features from 8 without preprocessing to 22 with preprocessing. 2D analysis yielded more features able to identify adenocarcinoma than 3D analysis (12 discriminant radiomic features after preprocessing in 3D). Preprocessing did not increase false positive findings as no discriminant features were identified in any of the sham experiments. The greatest sensitivity of the 2D analysis applied to preprocessed data was confirmed in the validation set.

Conclusion: Correction for magnetic field inhomogeneities and normalization of voxel values are essential to reveal the full potential of radiomic features to identify the tumor histological type from MR T2-weighted images, with classification performance similar to those reported in PET/CT and in multiphase CT in lung cancers.

Advances of Zinc Signaling Studies in Prostate Cancer

Prostate cancer (PCa) is one of the most common cancers and the second leading cause of cancer-related death among men worldwide. Despite progresses in early diagnosis and therapeutic strategies, prognosis for patients with advanced PCa remains poor. Noteworthily, a unique feature of healthy prostate is its highest level of zinc content among all soft tissues in the human body, which dramatically decreases during prostate tumorigenesis. To date, several reviews have suggested antitumor activities of zinc and its potential as a therapeutic strategy of PCa. However, an overview about the role of zinc and its signaling in PCa is needed. Here, we review literature related to the content, biological function, compounds and clinical application of zinc in PCa. We first summarize zinc content in prostate tissue and sera of PCa patients with their clinical relevance. We then elaborate biological functions of zinc signaling in PCa on three main aspects, including cell proliferation, death and tumor metastasis. Finally, we discuss clinical applications of zinc-containing compounds and proteins involved in PCa signaling pathways. Based on currently available studies, we conclude that zinc plays a tumor suppressive role and can serve as a biomarker in PCa diagnosis and therapies.

MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology

Personalized medicine aims at offering optimized treatment options and improved survival for cancer patients based on individual variability. The success of precision medicine depends on robust biomarkers. Recently, the requirement for improved non-biologic biomarkers that reflect tumor biology has emerged and there has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics. Radiomics as a methodological approach can be applied to any image and most studies have focused on PET, CT, ultrasound, and MRI. Here, we aim to present an overview of the radiomics workflow as well as the major challenges with special emphasis on the use of multiparametric MRI datasets. We then reviewed recent studies on radiomics in the field of pelvic oncology including prostate, cervical, and colorectal cancer.

MR Fingerprinting and ADC Mapping for Characterization of Lesions in the Transition Zone of the Prostate Gland

BackgroundPreliminary studies have shown that MR fingerprinting-based relaxometry combined with apparent diffusion coefficient (ADC) mapping can be used to differentiate normal peripheral zone from prostate cancer and prostatitis. The utility of relaxometry and ADC mapping for the transition zone (TZ) is unknown.PurposeTo evaluate the utility of MR fingerprinting combined with ADC mapping for characterizing TZ lesions.Materials and MethodsTZ lesions that were suspicious for cancer in men who underwent MRI with T2-weighted imaging and ADC mapping (b values, 50-1400 sec/mm2), MR fingerprinting with steady-state free precession, and targeted biopsy (60 in-gantry and 15 cognitive targeting) between September 2014 and August 2018 in a single university hospital were retrospectively analyzed. Two radiologists blinded to Prostate Imaging Reporting and Data System (PI-RADS) scores and pathologic diagnosis drew regions of interest on cancer-suspicious lesions and contralateral visually normal TZs (NTZs) on MR fingerprinting and ADC maps. Linear mixed models compared two-reader means of T1, T2, and ADC. Generalized estimating equations logistic regression analysis was used to evaluate both MR fingerprinting and ADC in differentiating NTZ, cancers and noncancers, clinically significant (Gleason score ≥ 7) cancers from clinically insignificant lesions (noncancers and Gleason 6 cancers), and characterizing PI-RADS version 2 category 3 lesions.ResultsIn 67 men (mean age, 66 years ± 8 [standard deviation]) with 75 lesions, targeted biopsy revealed 37 cancers (six PI-RADS category 3 cancers and 31 PI-RADS category 4 or 5 cancers) and 38 noncancers (31 PI-RADS category 3 lesions and seven PI-RADS category 4 or 5 lesions). The T1, T2, and ADC of NTZ (1800 msec ± 150, 65 msec ± 22, and [1.13 ± 0.19] × 10-3 mm2/sec, respectively) were higher than those in cancers (1450 msec ± 110, 36 msec ± 11, and [0.57 ± 0.13] × 10-3 mm2/sec, respectively; P < .001 for all). The T1, T2, and ADC in cancers were lower than those in noncancers (1620 msec ± 120, 47 msec ± 16, and [0.82 ± 0.13] × 10-3 mm2/sec, respectively; P = .001 for T1 and ADC and P = .03 for T2). The area under the receiver operating characteristic curve (AUC) for T1 plus ADC was 0.94 for separation. T1 and ADC in clinically significant cancers (1440 msec ± 140 and [0.58 ± 0.14] × 10-3 mm2/sec, respectively) were lower than those in clinically insignificant lesions (1580 msec ± 120 and [0.75 ± 0.17] × 10-3 mm2/sec, respectively; P = .001 for all). The AUC for T1 plus ADC was 0.81 for separation. Within PI-RADS category 3 lesions, T1 and ADC of cancers (1430 msec ± 220 and [0.60 ± 0.17] × 10-3 mm2/sec, respectively) were lower than those of noncancers (1630 msec ± 120 and [0.81 ± 0.13] × 10-3 mm2/sec, respectively; P = .006 for T1 and P = .004 for ADC). The AUC for T1 was 0.79 for differentiating category 3 lesions.ConclusionMR fingerprinting-based relaxometry combined with apparent diffusion coefficient mapping may improve transition zone lesion characterization.© RSNA, 2019Online supplemental material is available for this article.

Modern imaging and image-guided treatments of the prostate gland: MR and ablation for cancer and prostatic artery embolization for benign prostatic hyperplasia

Multiparametric MRI (mpMRI) has proven to be an essential tool for diagnosis, post-treatment follow-up, aggressiveness assessment, and active surveillance of prostate cancer. Currently, this imaging technique is part of the daily practice in many oncological centres. This manuscript aims to review the use of mpMRI in the set of prostatic diseases, either malignant or benign: mpMRI to detect and stage prostate cancer is discussed, as well as its use for active surveillance. Image-guided ablation techniques for prostate cancer are also reviewed. The need to establish minimum acceptable technical parameters for prostate mpMRI, standardize reports, uniform terminology for describing imaging findings, and develop assessment categories that differentiate levels of suspicion for clinically significant prostate cancer led to the development of the Prostate Imaging Reporting and Data System that is reviewed. Special focus will also be given on the most up-to-date evidence of prostatic artery embolization (PAE) for symptomatic benign prostatic hyperplasia (BPH). Management of patients with BPH, technical aspects of PAE, expected outcomes and level of evidence are reviewed with the most recent literature. PAE is a challenging technique that requires dedicated anatomical knowledge and comprehensive embolization skills. PAE has been shown to be an effective minimally-invasive treatment option for symptomatic BPH patients, that can be viewed between medical therapy and surgery. PAE may be a good option for symptomatic BPH patients that do not want to be operated and can obviate the need for prostatic surgery in up to 80% of treated patients.

Interobserver Agreement and Positivity of PI-RADS Version 2 Among Radiologists with Different Levels of Experience

RATIONALE AND OBJECTIVES

To evaluate interobserver agreement of Prostate Imaging Reporting and Data System (PI-RADS) v2 category among radiologists with different levels of experience. The secondary objective was to evaluate the positivity for significant cancer among each category (splitting category 4 into two) and among different lesion sizes.

MATERIALS AND METHODS

Institutional review board and ethics comitee approved retrospective study. Eight radiologists with different levels of experienced in prostatic magnetic resonance imaging-two more experienced, four with intermediate experience, and two abdominal radiology fellows-interpreted 160 lesions. Reference standard was fusion-targeted biopsy. Percentage agreement, k coefficients, and analysis concordance were used.

RESULTS

Coefficient of concordance according to categories was 0.71 considering both zones, 0.72 for peripheral zone (PZ) and 0.44 for peripheral zone (TZ). Agreement for PI-RADS score of 3 or greater was 0.48 in PZ and 0.57 in TZ. Tumor positivity rates were 54.3% and 66.0% for PI-RADS 3 + 1 and 4 for PZ, respectively; and 25.0 and 49.2% for PI-RADS 3 + 1 and 4 for TZ, respectively (p < 0.001 in both analysis). Lesions <10, 10-14, and ≥15 mm had 55.3%, 74.6%, and 93.5% of positivity rates for cancer in PZ (p = 0.002 and <0.001) and 26.7%, 56.5%, and 59.6% in TZ, respectively (p = 0.245 and 0.632). Sensitivities, specificities, and accuracies of magnetic resonance imaging for prostate cancer using PI-RADS v2 were 76%, 72%, and 74% for PZ; and 76%, 69%, and 71% for TZ, respectively.

CONCLUSION

This study shows that PI-RADS v2 has overall good interobserver agreement among radiologists with different levels of experience. PI-RADS category 3 + 1 showed lower positivity rates for significant cancer compared to category 4. Lastly, lesions 10-14 mm has similar positivity rates compared to ≥15 mm for TZ lesions.

Volumetric and Voxel-Wise Analysis of Dominant Intraprostatic Lesions on Multiparametric MRI

Introduction: Multiparametric MR imaging (mpMRI) has shown promising results in the diagnosis and localization of prostate cancer. Furthermore, mpMRI may play an important role in identifying the dominant intraprostatic lesion (DIL) for radiotherapy boost. We sought to investigate the level of correlation between dominant tumor foci contoured on various mpMRI sequences. Methods: mpMRI data from 90 patients with MR-guided biopsy-proven prostate cancer were obtained from the SPIE-AAPM-NCI Prostate MR Classification Challenge. Each case consisted of T2-weighted (T2W), apparent diffusion coefficient (ADC), and K^trans images computed from dynamic contrast-enhanced sequences. All image sets were rigidly co-registered, and the dominant tumor foci were identified and contoured for each MRI sequence. Hausdorff distance (HD), mean distance to agreement (MDA), and Dice and Jaccard coefficients were calculated between the contours for each pair of MRI sequences (i.e., T2 vs. ADC, T2 vs. K^trans, and ADC vs. K^trans). The voxel wise spearman correlation was also obtained between these image pairs. Results: The DILs were located in the anterior fibromuscular stroma, central zone, peripheral zone, and transition zone in 35.2, 5.6, 32.4, and 25.4% of patients, respectively. Gleason grade groups 1-5 represented 29.6, 40.8, 15.5, and 14.1% of the study population, respectively (with group grades 4 and 5 analyzed together). The mean contour volumes for the T2W images, and the ADC and K^trans maps were 2.14 ± 2.1, 2.22 ± 2.2, and 1.84 ± 1.5 mL, respectively. K^trans values were indistinguishable between cancerous regions and the rest of prostatic regions for 19 patients. The Dice coefficient and Jaccard index were 0.74 ± 0.13, 0.60 ± 0.15 for T2W-ADC and 0.61 ± 0.16, 0.46 ± 0.16 for T2W-K^trans. The voxel-based Spearman correlations were 0.20 ± 0.20 for T2W-ADC and 0.13 ± 0.25 for T2W-K^trans. Conclusions: The DIL contoured on T2W images had a high level of agreement with those contoured on ADC maps, but there was little to no quantitative correlation of these results with tumor location and Gleason grade group. Technical hurdles are yet to be solved for precision radiotherapy to target the DILs based on physiological imaging. A Boolean sum volume (BSV) incorporating all available MR sequences may be reasonable in delineating the DIL boost volume.

Diagnostic Value of CT in Detecting Peripheral Zone Prostate Cancer

OBJECTIVE. The purpose of this study was to identify the sensitivity of contrast-enhanced CT in detecting high-grade prostate adenocarcinoma. MATERIALS AND METHODS. A retrospective analysis included 100 patients with prostate cancer proven by biopsy between January 2010 and December 2017 who underwent staging CT of the abdomen and pelvis within 3 months of diagnosis. The control subjects were 100 randomly selected aged-matched male outpatients with no known history of malignancy who underwent contrast-enhanced CT of the abdomen and pelvis in the same time period as the patients with cancer. Two readers, blinded to both groups, independently assessed the likelihood of prostate cancer on the basis of the CT finding of focal abnormally increased peripheral enhancement in the prostate. Binary classification of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) was used to assess the diagnostic utility of CT versus the reference standard of transrectal ultrasound-guided biopsy. RESULTS. Eighty-three of 100 patients with biopsy-proven prostate cancer and 92 of 100 control subjects were correctly identified (sensitivity, 0.83; specificity, 0.92; PPV, 0.91; NPV, 0.84). There was no significant difference in diagnostic accuracy among subjects with different Gleason scores. Interrater agreement on both the cancer and control patients was 0.76 as assessed by Cohen kappa statistic. CONCLUSION. Incidental detection of a focal area of increased enhancement in the periphery of the prostate at contrast-enhanced CT may represent a clinically significant cancer and deserves further workup with prostate-specific antigen measurement and correlation with clinical risk factors for prostate cancer.

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

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

Correlations between Apparent Diffusion Coefficient and Gleason Score in Prostate Cancer: A Systematic Review

BACKGROUND

Reported data regarding the associations between apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) and Gleason score in prostate cancer (PC) are inconsistent.

OBJECTIVE

The aim of the present systematic review was to analyze relationships between ADC and Gleason score in PC.

DESIGN, SETTING, AND PARTICIPANTS

MEDLINE library, SCOPUS, and EMBASE databases were screened for relationships between ADC and Gleason score in PC up to April 2018. Overall, 39 studies with 2457 patients were identified. Data on the following parameters were extracted from the literature: number of patients, cancer localization, and correlation coefficients between ADC and Gleason score.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Associations between ADC and Gleason score were analyzed by the Spearman's correlation coefficient.

RESULTS AND LIMITATIONS

In overall sample, the pooled correlation coefficient between ADC and Gleason score was -0.45 (95% confidence interval [CI]=[-0.50; -0.40]). In PC in the transitional zone, the pooled correlation coefficient was -0.22 (95% CI=[-0.47; 0.03]). In PC in the peripheral zone, the pooled correlation coefficient was -0.48 (95% CI=[-0.54; -0.42]).

CONCLUSIONS

In PC located in the peripheral zone, ADC correlated moderately with Gleason score. In PC located in the transitional zone, ADC correlated weakly with Gleason score.

PATIENT SUMMARY

We reviewed studies using apparent diffusion coefficient for the prediction of Gleason score in prostate cancer patients.

A Multireader Exploratory Evaluation of Individual Pulse Sequence Cancer Detection on Prostate Multiparametric Magnetic Resonance Imaging (MRI)

RATIONALE AND OBJECTIVES

To determine independent contribution of each prostate multiparametric magnetic resonance imaging (mpMRI) sequence to cancer detection when read in isolation.

MATERIALS AND METHODS

Prostate mpMRI at 3-Tesla with endorectal coil from 45 patients (n = 30 prostatectomy cases, n = 15 controls with negative magnetic resonance imaging [MRI] or biopsy) were retrospectively interpreted. Sequences (T2-weighted [T2W] MRI, diffusion-weighted imaging [DWI], and dynamic contrast-enhanced [DCE] MRI; N = 135) were separately distributed to three radiologists at different institutions. Readers evaluated each sequence blinded to other mpMRI sequences. Findings were correlated to whole-mount pathology. Cancer detection sensitivity, positive predictive value for whole prostate (WP), transition zone, and peripheral zone were evaluated per sequence by reader, with reader concordance measured by index of specific agreement. Cancer detection rates (CDRs) were calculated for combinations of independently read sequences.

RESULTS

44 patients were evaluable (cases median prostate-specific antigen 6.83 [ range 1.95-51.13] ng/mL, age 62 [45-71] years; controls prostate-specific antigen 6.85 [2.4-10.87] ng/mL, age 65.5 [47-71] years). Readers had highest sensitivity on DWI (59%) vs T2W MRI (48%) and DCE (23%) in WP. DWI-only positivity (DWI+/T2W-/DCE-) achieved highest CDR in WP (38%), compared to T2W-only (CDR 24%) and DCE-only (CDR 8%). DWI+/T2W+/DCE- achieved CDR 80%, an added benefit of 56.4% from T2W-only and of 42% from DWI-only (P < .0001). All three sequences interpreted independently positive gave highest CDR of 90%. Reader agreement was moderate (index of specific agreement: T2W = 54%, DWI = 58%, DCE = 33%).

CONCLUSIONS

When prostate mpMRI sequences are interpreted independently by multiple observers, DWI achieves highest sensitivity and CDR in transition zone and peripheral zone. T2W and DCE MRI both add value to detection; mpMRI achieves highest detection sensitivity when all three mpMRI sequences are positive.

Outcomes of magnetic resonance imaging fusion-targeted biopsy of prostate imaging reporting and data system 3 lesions

PURPOSE

To evaluate the characteristics and histological outcomes in patients with Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions undergoing magnetic resonance imaging-guided fusion-targeted biopsy (MRIFTB).

METHODS

We retrospectively reviewed 138 patients with PI-RADS category 3 lesions classified using multiparametric MRI who underwent MRIFTB between May 2016 and March 2018. The study population included biopsy-naïve and patients with prior negative biopsy. Univariate and multivariate analyzes were performed to determine significant predictors of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The definition of csPCa was set at Gleason score ≥ 3 + 4.

RESULTS

Overall, 114 (82.6%) biopsied lesions were benign and 24 (17.4%) were identified as prostate cancer. Of these 24 lesions, 14 (58.3%) harbored csPCa. Peripheral zone (PZ) lesions were more likely to be associated with malignant disease than transition zone lesions (13.7 vs. 6.2%). Multivariate logistic analysis revealed that age, PZ location, and prostate-specific antigen (PSA) density (P < 0.05) were independent predictors of both PCa and csPCa.

CONCLUSIONS

A non-negligible number of PI-RADS 3 patients harbor csPCa. Moreover, age, lesion location, and PSA density could be potential clinical predictors of PCa and csPCa. Physicians should be aware of the cancer prevalence of PI-RADS 3 lesions, as the use of the aforementioned factors can help in the decision-making process for these patients.

The applied research of simultaneous image acquisition of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in the assessment of patients with prostate cancer

We aimed to evaluate the feasibility of simultaneous image acquisition of multiple instantaneous switchable scan (MISS) for prostate magnetic resonance imaging (MRI) on 3T. Fifty-three patients were scanned with MRI due to suspected prostate cancer. Twenty-eight of them got histological results. First, two readers assessed the structure delineation and image quality based on images of conventional T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) (CTD). Second, two readers identified the index lesion together, and then, reader one evaluated the contrast of index lesion on T2WI and signal ratio on apparent diffusion coefficient map. Third, they assigned Prostate Imaging Reporting and Data System (PI-RADS) score in consensus for the index lesion. After 4 weeks, the images of MISS were reviewed by the same readers following the same process. Finally, two readers gave preference for image interpretation, respectively. Kappa coefficient, Wilcoxon signed-rank test, paired-sample t-test, Bland–Altman analysis, and receiver operating characteristic (ROC) analysis were used for statistical analysis. The acquisition time of CTD was 6 min and 10 s, while the acquisition time of MISS was 4 min and 30 s. Interobserver agreements for image evaluation were κ = 0.65 and κ = 0.80 for CTD and MISS, respectively. MISS-T2WI showed better delineation for seminal vesicles than CTD-T2WI (reader 1: P < 0.001, reader 2: P = 0.001). The index lesion demonstrated higher contrast in MISS-T2WI (P < 0.001). The PI-RADS scores based on CTD and MISS exhibited high ability in predicting clinically significant cancer (area under curve [AUC] = 0.828 vs 0.854). Readers preferred to use MISS in 41.5%–47.2% of cases. MISS showed comparable performance to conventional technique with less acquisition time.