Comparison of Prostate Imaging Reporting and Data System versions 1 and 2 for the Detection of Peripheral Zone Gleason Score 3 + 4 = 7 Cancers

Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis?

In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen's Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen's Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.

Interpretation of Prostate Magnetic Resonance Imaging Using Prostate Imaging and Data Reporting System Version 2.1: A Primer

Prostate magnetic resonance imaging (MRI) is increasingly being used to diagnose and stage prostate cancer. The Prostate Imaging and Data Reporting System (PI-RADS) version 2.1 is a consensus-based reporting system that provides a standardized and reproducible method for interpreting prostate MRI. This primer provides an overview of the PI-RADS system, focusing on its current role in clinical interpretation. It discusses the appropriate use of PI-RADS and how it should be applied by radiologists in clinical practice to assign and report PI-RADS assessments. We also discuss the changes from prior versions and published validation studies on PI-RADS accuracy and reproducibility.

Current magnetic resonance imaging-based diagnostic strategies for prostate cancer

Recent developments in multiparametric MRI and MRI-targeted biopsy have made it possible to detect clinically significant cancers more accurately and efficiently than ever before. Furthermore, software that enables easy MRI/US image fusion has been developed and is already available on the market, and this has provided a tailwind for the spread of MRI-based prostate cancer diagnostic strategies. Such precise diagnosis of prostate cancer localization is essential for highly accurate focal therapy. In addition, a recent large-scale study applying MRI to community screening for prostate cancer has reported its usefulness. By contrast, concerns about overdiagnosis and overtreatment, the existence of inter-reader variability in MRI diagnosis, and issues with current MRI-targeted biopsy have emerged. In this article, we review the development of multiparametric MRI and MRI-targeted biopsy to date and the current issues and discuss future directions.

Structured reporting in radiologic education - Potential of different PI-RADS versions in prostate MRI controlled by in-bore MR-guided biopsies

Objectives:

To evaluate the efficiency of structured reporting in radiologic education – based on the example of different PI-RADS score versions for multiparametric MRI (mpMRI) of the prostate.

Methods:

MpMRI of 688 prostate lesions in 180 patients were retrospectively reviewed by an experienced radiologist and by a student using PI-RADS V1 and V2. Data sets were reviewed for changes according to PI-RADS V2.1. The results were correlated with results obtained by MR-guided biopsy. Diagnostic potency was evaluated by ROC analysis. Sensitivity, specificity and correct-graded samples were evaluated for different cutpoints. The agreement between radiologist and student was determined for the aggregation of the PI-RADS score in three categories. The student’s time needed for evaluation was measured.

Results:

The area under curve of the ROC analysis was 0.782/0.788 (V1/V2) for the student and 0.841/0.833 (V1/V2) for the radiologist. The agreement between student and radiologist showed a Cohen‘s weighted κ coefficient of 0.495 for V1 and 0.518 for V2. Median student’s time needed for score assessment was 4:34 min for PI-RADSv1 and 2:00 min for PI-RADSv2 (p < 0.001). Re-evaluation for V2.1 changed the category in 1.4% of all ratings.

Conclusion:

The capacity of prostate cancer detection using PI-RADS V1 and V2 is dependent on the reader‘s experience. The results from the two observers indicate that structured reporting using PI-RADS and, controlled by histopathology, can be a valuable and quantifiable tool in students‘ or residents’ education. Herein, V2 was superior to V1 in terms of inter-observer agreement and time efficacy.

Advances in knowledge:

Structured reporting can be a valuable and quantifiable tool in radiologic education. Structured reporting using PI-RADS can be used by a student with good performance. PI-RADS V2 is superior to V1 in terms of inter-observer agreement and time efficacy.

PI-RADS 2.1 - Image Interpretation: The Most Important Updates and Their Clinical Implications

BACKGROUND

 Multiparametric magnetic resonance imaging (MRI) of the prostate plays a central role in the diagnosis of patients with suspected prostate cancer. The increasing distribution and application of the guideline for the standardization of image acquisition, evaluation, and reporting (Prostate Imaging - Reporting and Data System, PI-RADS), which was updated in 2019 to version 2.1, contributes to the success of the technique.

MATERIALS AND METHODS

 The most important updates of PI-RADS version 2.1 presented in 2019 compared to the previous version PI-RADS 2.0 are highlighted and interpreted with regard to their clinical implications.

RESULTS

 PI-RADS version 2.1 aims to simplify the application of the scoring scheme without changing the basic concept of dominant sequences (DWI in the peripheral zone, T2 in the transition zone). Of particular importance are the increasing role of diffusion-weighted imaging in the transition zone, the now mandatory high b-value of at least 1400 s/mm², and new information on the assessment of the central zone and the anterior fibromuscular stroma.

CONCLUSION

 PI-RADS version 2.1 published in 2019 addresses a number of changes to the previous version, including both the examination technique and image interpretation. Prospective clinical studies have yet to prove the extent to which the goals of reducing interreader variability and increasing the detection rate in the transition zone will be achieved.

KEY POINTS

  · The new PI-RADS version 2.1. includes changes regarding image interpretation and examination technique. · The role of diffusion-weighted imaging is strengthened in the transition zone. · An ultra-high b-value of at least 1400 s/mm2 is mandatory according to PI-RADS 2.1. · Biparametric MRI is not recommended for general application.

CITATION FORMAT

· Beyer T, Schlemmer H, Weber M et al. PI-RADS 2.1 - Image Interpretation: The Most Important Updates and Their Clinical Implications. Fortschr Röntgenstr 2021; 193: 787 - 795.

Visibility of significant prostate cancer on multiparametric magnetic resonance imaging (MRI)-do we still need contrast media?

Objectives

To assess the visibility of clinically significant prostate cancer (PCA) lesions on the sequences multiparametric MRI of the prostate (mpMRI) and to evaluate whether the addition of dynamic contrast–enhanced imaging (DCE) improves the overall visibility.

Methods

We retrospectively evaluated multiparametric MRI images of 119 lesions in 111 patients with biopsy-proven clinically significant PCA. Three readers assigned visual grading scores for visibility on each sequence, and a visual grading characteristic analysis was performed. Linear regression was used to explore which factors contributed to visibility in individual sequences.

Results

The visibility of lesions was significantly better with mpMRI when compared to biparametric MRI in visual grading characteristic (VGC) analysis, with an AUC_VGC of 0.62 (95% CI 0.55–0.69; p < 0.001). This benefit was seen across all readers. Multivariable linear regression revealed that a location in the peripheral zone was associated with better visibility on T2-weighted imaging (T2w). A higher Prostate Imaging-Reporting and Data System (PI-RADS) score was associated with better visibility on both diffusion-weighted imaging (DWI) and DCE. Increased lesion size was associated with better visibility on all sequences.

Conclusions

Visibility of clinically significant PCA is improved by using mpMRI. DCE and DWI images independently improve lesion visibility compared to T2w images alone. Further research into the potential of DCE to impact on clinical decision-making is suggested.

Key Points

• DCE and DWI images independently improve clinically significant prostate cancer lesion visibility compared to T2w images alone.

• Multiparametric MRI (DCE, DWI, T2w) achieved significantly higher visibility scores than biparametric MRI (DWI, T2w).

• Location in the transition zone is associated with poor visibility on T2w, while it did not affect visibility on DWI or DCE.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07494-1.

PI-RADS version 2.1 scoring system is superior in detecting transition zone prostate cancer: a diagnostic study

PURPOSE

The studies comparing the versions 2 vs. 2.1 of the Prostate Imaging Reporting and Data System (PI-RADS) are rare. This study aimed to evaluate whether PI-RADS version 2.1 is superior in detecting transition zone prostate cancer in comparison with PI-RADS version 2.

METHODS

This was a diagnostic study of patients with prostate diseases who visited the Urology Department of The Second Affiliated Hospital of Soochow University and underwent a magnetic resonance imaging (MRI) examination between 03-01-2016 and 10-31-2018. The images originally analyzed using PI-RADS version 2 were retrospectively re-analyzed and scored in 2019 according to the updated PI-RADS version 2.1. The kappa and receiver operating characteristic (ROC) curves were used.

RESULTS

For Reader 1, compared with PI-RADS version 2, version 2.1 had higher sensitivity (85% vs. 79%, P = 0.03), lower specificity (65% vs. 83%, P < 0.001), and lower area under the curve (AUC) (0.749 vs. 0.809, P < 0.001). For Reader 2 (first attempt), compared with PI-RADS version 2, version 2.1 had lower specificity (67% vs. 91%, P < 0.001) and lower AUC (0.702 vs. 0.844, P < 0.001). For Reader 2 (second attempt), compared with PI-RADS version 2, version 2.1 had higher sensitivity (88% vs. 78%, P < 0.001) and lower specificity (77% vs. 91%, P < 0.001). The kappa between the two attempts for Reader 2 was 0.321.

CONCLUSION

These results suggest that PI-RADS version 2.1 might improve the detection of prostate cancers in the transition zone compared with PI-RADS version 2 but that it might results in higher numbers of biopsies because of lower specificity.

The predictive value of the prostate health index vs. multiparametric magnetic resonance imaging for prostate cancer diagnosis in prostate biopsy

PURPOSE

To compare the ability of Prostate Health Index (PHI) to diagnose csPCa, with that of total PSA, PSA density (PSAD) and the multiparametric magnetic resonance (mpMRI) of the prostate.

METHODS

We analysed a group of 395 men planned for a prostate biopsy who underwent a mpMRI of the prostate evaluated using the PIRADS v1 criteria. All patients had their PHI measured before prostate biopsy. In patients with an mpMRI suspicious lesions, an mpMRI/ultrasound software fusion-guided biopsy was performed first, with 12 core systematic biopsy performed in all patients. A ROC analysis was performed for PCa detection for total PSA, PSAD, PIRADS score and PHI; with an AUC curve calculated for all criteria and a combination of PIRADS score and PHI. Subsequent sub-analyses included patients undergoing first and repeat biopsy.

RESULTS

The AUC for predicting the presence of csPCa in all patients was 59.5 for total PSA, 69.7 for PHI, 64.9 for PSAD and 62.5 for PIRADS. In biopsy naive patients it was 61.6 for total PSA, 68.9 for PHI, 64.6 for PSAD and 63.1 for PIRADS. In patients with previous negative biopsy the AUC for total PSA, PHI, PSAD and PIRADS was 55.4, 71.2, 64.4 and 69.3, respectively. Adding of PHI to PIRADS increased significantly (p = 0.007) the accuracy for prediction of csPCa.

CONCLUSION

Prostate Health Index could serve as a tool in predicting csPCa. When compared to the mpMRI, it shows comparable results. The PHI cannot, however, help us guide prostate biopsies in any way, and its main use may, therefore, be in pre-MRI or pre-biopsy triage.

Prostate MRI with PI-RADS v2.1: initial detection and active surveillance

The role of prostate MRI in clinical practice has continued to broaden over time. Multiple iterations of PI-RADS reporting have aided in improving detection and reporting of prostate cancer. In addition, recent recommendations from the PI-RADS Steering Committee promote an MRI-first approach with an MRI-directed prostate cancer diagnostic pathway. It is imperative for radiologists to be knowledgeable and familiar with prostate MRI and PI-RADS recommendations, as there is an increasing demand for prostate imaging by clinicians and patients alike.

Quantitative Prostate MRI

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

The role of gadolinium in magnetic resonance imaging for early prostate cancer diagnosis: A diagnostic accuracy study

Objective

Prostate lesions detected with multiparametric magnetic resonance imaging (mpMRI) are classified for their malignant potential according to the Prostate Imaging-Reporting And Data System (PI-RADS™2). In this study, we evaluate the diagnostic accuracy of the mpMRI with and without gadolinium, with emphasis on the added diagnostic value of the dynamic contrast enhancement (DCE).

Materials and methods

The study was retrospective for 286 prostate lesions / 213 eligible patients, n = 116/170, and 49/59% malignant for the peripheral (Pz) and transitional zone (Tz), respectively. A stereotactic MRI-guided prostate biopsy served as the histological ground truth. All patients received a mpMRI with DCE. The influence of DCE in the prediction of malignancy was analyzed by blinded assessment of the imaging protocol without DCE and the DCE separately.

Results

Significant (CSPca) and insignificant (IPca) prostate cancers were evaluated separately to enhance the potential effects of the DCE in the detection of CSPca. The Receiver Operating Characteristics Area Under Curve (ROC-AUC), sensitivity (Se) and specificity (Spe) of PIRADS-without-DCE in the Pz was 0.70/0.47/0.86 for all cancers (IPca and CSPca merged) and 0.73/0.54/0.82 for CSPca. PIRADS-with-DCE for the same patients showed ROC-AUC/Se/Spe of 0.70/0.49/0.86 for all Pz cancers and 0.69/0.54/0.81 for CSPca in the Pz, respectively, p>0.05 chi-squared test. Similar results for the Tz, AUC/Se/Spe for PIRADS-without-DCE was 0.75/0.61/0.79 all cancers and 0.67/0.54/0.71 for CSPca, not influenced by DCE (0.66/0.47/0.81 for all Tz cancers and 0.61/0.39/0.75 for CSPca in Tz). The added Se and Spe of DCE for the detection of CSPca was 88/34% and 78/33% in the Pz and Tz, respectively.

Conclusion

DCE showed no significant added diagnostic value and lower specificity for the prediction of CSPca compared to the non-enhanced sequences. Our results support that gadolinium might be omitted without mitigating the diagnostic accuracy of the mpMRI for prostate cancer.

PI-RADS version 2.1: one small step for prostate MRI

Multiparametric (mp) prostate magnetic resonance imaging (MRI) is playing an increasingly prominent role in the diagnostic work-up of patients with suspected prostate cancer. Performing mpMRI before biopsy offers several advantages including biopsy avoidance under certain clinical circumstances and targeting biopsy of suspicious lesions to enable the correct diagnosis. The success of the technique is heavily dependent on high-quality image acquisition, interpretation, and report communication, all areas addressed by previous versions of the Prostate Imaging-Reporting and Data System (PI-RADS) recommendations. Numerous studies have validated the approach, but the widespread adoption of PI-RADS version 2 has also highlighted inconsistencies and limitations, particularly relating to interobserver variability for evaluation of the transition zone. These limitations are addressed in the recently released version 2.1. In this article, we highlight the key changes proposed in PI-RADS v2.1 and explore the background reasoning and evidence for the recommendations.

Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets

PURPOSE

Accurate regional segmentation of the prostate boundaries on magnetic resonance (MR) images is a fundamental requirement before automated prostate cancer diagnosis can be achieved. In this paper, we describe a novel methodology to segment prostate whole gland (WG), central gland (CG), and peripheral zone (PZ), where PZ + CG = WG, from T2W and apparent diffusion coefficient (ADC) map prostate MR images.

METHODS

We designed two similar models each made up of two U-Nets to delineate the WG, CG, and PZ from T2W and ADC map MR images, separately. The U-Net, which is a modified version of a fully convolutional neural network, includes contracting and expanding paths with convolutional, pooling, and upsampling layers. Pooling and upsampling layers help to capture and localize image features with a high spatial consistency. We used a dataset consisting of 225 patients (combining 153 and 72 patients with and without clinically significant prostate cancer) imaged with multiparametric MRI at 3 Tesla.

RESULTS AND CONCLUSION

Our proposed model for prostate zonal segmentation from T2W was trained and tested using 1154 and 1587 slices of 100 and 125 patients, respectively. Median of Dice similarity coefficient (DSC) on test dataset for prostate WG, CG, and PZ were 95.33 ± 7.77%, 93.75 ± 8.91%, and 86.78 ± 3.72%, respectively. Designed model for regional prostate delineation from ADC map images was trained and validated using 812 and 917 slices from 100 and 125 patients. This model yielded a median DSC of 92.09 ± 8.89%, 89.89 ± 10.69%, and 86.1 ± 9.56% for prostate WG, CG, and PZ on test samples, respectively. Further investigation indicated that the proposed algorithm reported high DSC for prostate WG segmentation from both T2W and ADC map MR images irrespective of WG size. In addition, segmentation accuracy in terms of DSC does not significantly vary among patients with or without significant tumors.

SIGNIFICANCE

We describe a method for automated prostate zonal segmentation using T2W and ADC map MR images independent of prostate size and the presence or absence of tumor. Our results are important in terms of clinical perspective as fully automated methods for ADC map images, which are considered as one of the most important sequences for prostate cancer detection in the PZ and CG, have not been reported previously.

Predictive Factors and Oncologic Outcome of Downgrade to Pathologic Gleason Score 6⁻7 after Radical Prostatectomy in Patients with Biopsy Gleason Score 8⁻10

Gleason score (GS) 8⁻10 is associated with adverse outcomes in prostate cancer (PCa). However, biopsy GS (bGS) may be upgraded or downgraded post-radical prostatectomy (RP). We aimed to investigate predictive factors and oncologic outcomes of downgrade to pathologic GS (pGS) 6⁻7 after RP in PCa patients with bGSs 8⁻10. We retrospectively reviewed clinical data of patients with bGS ≥ 8 undergoing RP. pGS downgrade was defined as a pGS ≤ 7 from bGS ≥ 8 post-RP. Univariate and multivariate cox regression analysis, logistic regression analysis, and Kaplan⁻Meier curves were used to analyze pGS downgrade and biochemical recurrence (BCR). Of 860 patients, 623 and 237 had bGS 8 and bGS ≥ 9, respectively. Post-RP, 332 patients were downgraded to pGS ≤ 7; of these, 284 and 48 had bGS 8 and bGS ≥ 9, respectively. Prostate-specific antigen (PSA) levels; clinical stage; and adverse pathologic features such as extracapsular extension, seminal vesicle invasion and positive surgical margin were significantly different between patients with pGS ≤ 7 and pGS ≥ 8. Furthermore, bGS 8 (odds ratio (OR): 0.349, p < 0.001), PSA level < 10 ng/mL (OR: 0.634, p = 0.004), and ≤cT3a (OR: 0.400, p < 0.001) were identified as significant predictors of pGS downgrade. pGS downgrade was a significant positive predictor of BCR following RP in patients with high bGS (vs. pGS 8, hazard radio (HR): 1.699, p < 0.001; vs. pGS ≥ 9, HR: 1.765, p < 0.001). In addition, the 5-year BCR-free survival rate in patients with pGS downgrade significantly differed from that in patients with bGS 8 and ≥ 9 (52.9% vs. 40.7%, p < 0.001). Among patients with bGS ≥ 8, those with bGS 8, PSA level < 10 ng/mL, and ≤cT3a may achieve pGS downgrade after RP. These patients may have fewer adverse pathologic features and show a favorable prognosis; thus we suggest that active treatment is needed in these patients. In addition, patients with high-grade bGS should be managed aggressively, even if they show pGS downgrade.

Characteristics of missed prostate cancer lesions on 3T multiparametric-MRI in 518 patients: based on PI-RADSv2 and using whole-mount histopathology reference

PURPOSE

To determine the characteristics of missed prostate cancer (PCa) lesions on 3T multiparametric-MRI (mpMRI) based on PI-RADSv2 with whole-mount histopathology (WMHP) correlation.

MATERIALS AND METHODS

This IRB-approved, HIPAA-compliant study, included 614 consecutive men with 3T mpMRI prior to prostatectomy at a single tertiary center between 12/2009 and 4/2017. Clinical, mpMRI, and pathologic features were obtained. PI-RADSv2-based MRI detected lesions were matched with previously finalized WMHP by a genitourinary (GU) radiologist and a GU pathologist. Patients with no mpMRI detected PCa lesion, but with at least one lesion ≥ 1 cm on WMHP, were reviewed retrospectively and assigned a PI-RADSv2 score. Tumor characteristics were compared between missed and detected lesions.

RESULT

The final cohort included 518 patients with 1085 WMHP lesions. 51.9% (563/1085) of lesions were missed on 3T mpMRI. 71.4% (402/563), 21.7% (122/563), 4.4% (25/563), and 2.5% (14/563) of the missed lesions were Gleason scores (GS) 3 + 3, 3 + 4, 4 + 3, and 8 - 10, respectively. Missed PCa lesions had significantly lower proportion of GS ≥ 7 (p < 0.001) and smaller size for overall (p < 0.001) and index subcohorts (p < 0.001), as compared to detected lesions. 34.5% (194) of overall and 71.2% (79) index missed lesions were larger than 1 cm. In 13.7% (71/518) of patients without MR detected PCa, 149 lesions were detected on WMHP, with 70 (47%) lesions ≥ 1 cm. In retrospective review of these lesions, 42.9% (30), 18.6% (13), 21.5% (15), 10% (7), and 7% (5) were PI-RADSv2 1, 2, 3, 4, and 5, respectively.

CONCLUSION

3T mpMRI has an excellent per patients diagnostic performance for PCa and majority of missed lesions are clinically nonsignificant.

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.

Multiparametric magnetic resonance imaging - Transrectal ultrasound-guided cognitive fusion biopsy of the prostate: Clinically significant cancer detection rates stratified by the Prostate Imaging and Data Reporting System version 2 assessment category

INTRODUCTION

We aimed to report the clinically significant prostate cancer (PCa) detection rate in men undergoing magnetic resonance imaging-transrectal ultrasound (MRI-TRUS)-cognitive fusion (CF) targeted biopsies stratified by the Prostate Imaging and Data Reporting System (PI-RADS) version 2 (v2) scores.

METHODS

With a quality assurance waiver from the IRB, we identified a cohort of men who underwent MRI-TRUS-CF and synchronous template biopsy from 2015-2017. MRI (PI-RADS v2 score, lesion size, lesion location [peripheral or transition zone (PZ/TZ)]), and CF-TRUS biopsy (operator experience, TRUS visibility, and number of biopsies) features were extracted. The primary outcome was diagnosis of clinically significant (Gleason score ≥3+4=7 or International Society of Urological Pathology (ISUP) grade group ≥2) PCa.

RESULTS

During the study period, 131 men (with 142 PIRADS v2 score ≥3 lesions) met inclusion criteria; 98 men had previously negative template biopsy and 33 were on active surveillance for previously detected low-grade PCa. In total, 41.9% (55/131) men had clinically significant PCa - 17.6% (23/131) detected on targeted biopsy only, 8.4% (11/131) on template biopsy only, and 16.0% (21/131) on both targeted and template biopsy. Clinically significant PCa detection stratified by PI-RADS v2 scores were: 11.1% (3/27) for score 3 (indeterminate), 42.9% (24/56) for score 4 (significant cancer likely), and 35.6% (21/59) for score 5 (significant cancer very likely). Clinically significant PCa detection rates in targeted biopsies were better among PZ (41.8% [33/79]) compared to TZ (23.8% [15/63]) lesions (p=0.025) in TRUS visible lesions (p=0.033) and in the most experienced radiologists (p=0.05), with no difference by lesion size or number of additional core biopsies performed (all p>0.05).

CONCLUSIONS

Cognitive fusion MRI-TRUS-guided targeted biopsy yielded substantially lower rates of clinically significant cancer in PI-RADS v2 score 4 and 5 lesions when compared to published results using in-bore MR-guided or automated MRI-TRUS fusion guidance systems. Cancer detection was worst for TZ lesions.