Comparison of prostate imaging reporting and data system v2.1 and 2 in transition and peripheral zones: evaluation of interreader agreement and diagnostic performance in detecting clinically significant prostate cancer

2021 ◽  
pp. 20201434
Author(s):  
Yasuyo Urase ◽  
Yoshiko Ueno ◽  
Tsutomu Tamada ◽  
Keitaro Sofue ◽  
Satoru Takahashi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Area Under The Curve ◽  
Data System ◽  
Transition Zones ◽  
Prostate Imaging ◽  
Institutional Review ◽  
3.0 T ◽  
Clinically Significant ◽  
Interreader Agreement

Objectives: To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1, in comparison with v2. Methods: Institutional review board approval was obtained for this retrospective study. Seventy-seven consecutive patients who underwent a prostate multiparametric magnetic resonance imaging at 3.0 T before radical prostatectomy were included. Four radiologists (two experienced uroradiologists and two inexperienced radiologists) independently scored eight regions [six peripheral zones (PZ) and two transition zones (TZ)] using v2.1 and v2. Interreader agreement was assessed using κ statistics. To evaluate diagnostic performance for clinically significant prostate cancer (csPC), area under the curve (AUC) was estimated. Results 228 regions were pathologically diagnosed as positive for csPC. With a cutoff ≥3, the agreement among all readers was better with v2.1 than v2 in TZ, PZ, or both zones combined (κ-value: TZ, 0.509 vs 0.414; PZ, 0.686 vs 0.568; both zones combined, 0.644 vs 0.531). With a cutoff ≥4, the agreement among all readers was also better with v2.1 than v2 in the PZ or both zones combined (κ-value: PZ, 0.761 vs 0.701; both zones combined, 0.756 vs 0.709). For all readers, AUC with v2.1 was higher than with v2 (TZ, 0.826–0.907 vs 0.788–0.856; PZ, 0.857–0.919 vs 0.853–0.902). Conclusions: Our study suggests that the PI-RADS v2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v2. Advances in knowledge: PI-RADS v2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.

scholarly journals Evaluating the efficacy of a low-cost, minimally-invasive cognitive MRI targeted biopsy protocol in the Prostate Imaging Reporting and Data System (PI-RADS) version 2 era.

2019 ◽  
Author(s):  
Yuta Takeshima ◽  
Yoshinori Tanaka ◽  
Kotaro Takemura ◽  
Shusaku Nakazono ◽  
Eiko Yamashita ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Low Cost ◽  
Multiparametric Mri ◽  
Data System ◽  
Targeted Biopsy ◽  
Prostate Imaging ◽  
Biopsy Protocol ◽  
Clinically Significant ◽  
Systematic Biopsy

Abstract Background: New MRI-guided targeting biopsy methods have increased cancer yield of prostate biopsies. However, cost and time constraints have made it difficult for many institutions to implement these newer methods. We evaluated the diagnostic performance of a low-cost, minimally-invasive, cognitive MRI-targeted biopsy protocol based on 1.5T multiparametric MRI graded with Prostate Imaging Reporting and Data System version 2 that is easily implemented in any low- to intermediate- volume center. Methods: Retrospective analysis of 255 patients who underwent prostate biopsy between December 2016 and March 2019 at a single facility. Indication for biopsy was based on clinical parameters including 1.5T multiparametric MRI. In addition to 10-core systematic biopsy, targeted cores were obtained with cognitive recognition under ultrasound. A control group of 198 patients biopsied without prior MRI from January to December 2015 was also analyzed. Results: Prostate biopsy preceded by MRI had a significantly higher probability of detecting both prostate cancer (68.1% vs. 43.6%) and clinically significant cancer (56.2% vs. 29.4%) (p values< 0.01). Combination of systematic biopsy and targeted biopsy outperformed either regimen alone for detection of prostate cancer. Multivariate analysis showed PSA density and prostate imaging reporting and data system score were independent risk factors of prostate cancer. A proposed diagnostic model showed sensitivity of 88.6%, specificity of 55%, PPV of 81.2%, NPV of 68.8%, and accuracy of 78%. Prostate imaging reporting and data system score was correlated with a higher presence of prostate cancer, clinically significant prostate cancer, and a higher pathological grade. Conclusions: Incorporation of pre-biopsy MRI imaging, scoring, and targeted biopsy improved cancer yield and achieved diagnostic performance comparable to newer methods of higher cost. Future alterations of possible benefit included increasing the number of target cores per lesion, and combining prostate imaging reporting and data system score and PSA density as indicators for biopsy.

scholarly journals Comparison of Likert and PI-RADS version 2 MRI scoring systems for the detection of clinically significant prostate cancer

2020 ◽  
Vol 93 (1112) ◽  
pp. 20200298 ◽  
Author(s):  
Jeries P Zawaideh ◽  
Evis Sala ◽  
Maria Pantelidou ◽  
Nadeem Shaida ◽  
Brendan Koo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Area Under The Curve ◽  
Scoring Systems ◽  
Detection Rates ◽  
Prostate Imaging ◽  
Prostate Biopsies ◽  
Threshold Score ◽  
Lesion Level ◽  
Clinically Significant ◽  
Histological Results

Objective: To compare the performance of Likert and Prostate Imaging–Reporting and Data System (PI-RADS) multiparametric (mp) MRI scoring systems for detecting clinically significant prostate cancer (csPCa). Methods: 199 biopsy-naïve males undergoing prostate mpMRI were prospectively scored with Likert and PI-RADS systems by four experienced radiologists. A binary cut-off (threshold score ≥3) was used to analyze histological results by three groups: negative, insignificant disease (Gleason 3 + 3; iPCa), and csPCa (Gleason ≥3 +4). Lesion-level results and prostate zonal location were also compared. Results: 129/199 (64.8%) males underwent biopsy, 96 with Likert or PI-RADS score ≥3, and 21 with negative MRI. A further 12 patients were biopsied during follow-up (mean 507 days). Prostate cancer was diagnosed in 87/199 (43.7%) patients, 65 with (33.6%) csPCa. 30/92 (32.6%) patients with negative MRI were biopsied, with an NPV of 83.3% for cancer and 86.7% for csPCa. Likert and PI-RADS score differences were observed in 92 patients (46.2%), but only for 16 patients (8%) at threshold score ≥3. Likert scoring had higher specificity than PI-RADS (0.77 vs 0.66), higher area under the curve (0.92 vs 0.87, p = 0.002) and higher PPV (0.66 vs 0.58); NPV and sensitivity were the same. Likert had more five score results (58%) compared to PI-RADS (36%), but with similar csCPa detection (81.0 and 80.6% respectively). Likert demonstrated lower proportion of false positive in the predominately AFMS-involving lesions. Conclusion: Likert and PI-RADS systems both demonstrate high cancer detection rates. Likert scoring had a higher AUC with moderately higher specificity and lower positive call rate and could potentially help to reduce the number of unnecessary biopsies performed. Advances in knowledge: This paper illustrates that the Likert scoring system has potential to help urologists reduce the number of prostate biopsies performed.

scholarly journals The Effect of Different Levels of Prostate-Specific Antigen and Prostate Imaging Report and Data System Scores on Logistic Regression Model to Predict Clinically Significant Prostate Cancer—A Single Center Retrospective Study

2020 ◽  
Author(s):  
Loudong Zhang ◽  
Hua Zhu ◽  
Donghua Gu ◽  
Xiaodong Pan ◽  
bing zheng
Keyword(s):  
Prostate Cancer ◽  
Retrospective Study ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Data System ◽  
Diagnostic Efficiency ◽  
Prostate Imaging ◽  
Clinically Significant ◽  
Different Levels

Abstract Background: At present, there are various clinical regression models for predicting prostate cancer. But what about the diagnostic effectiveness of these models in different parameter ranges, and are the models applicable to everyone? This study aimed to study the influence of different levels of prostate-specific antigen (PSA) and Prostate Imaging Report and Data System version 2 (PI-RADS v2) scores on the regression model to predict clinically significant prostate cancer (csPCa).Methods: This retrospective study screened 251 patients from our hospital, who were divided into different groups. The regression model was established for each group to predict csPCa, and the effects of PSA and PI-RADS scores on each model were analyzed through the diagnostic effects of the model.Results: In patients with lower PSA scores, although the model was less sensitive than PSA, the AUC of the model was much greater. With the rise of PSA, the sensitivity of the model surpassed that of PSA, while the specificity became the opposite, and the AUC gap also gradually decreased. In the group with low PI-RADS score, the sensitivity and specificity of PI-RADS were lower than the model, and the gap was larger. Although the gap between the two gradually decreased with the increase of PI-RADS, the diagnostic efficiency of the model was still slightly larger than that of pure PI-RADS.Conclusion: As the PSA and PI-RADS v2 scores increase, the diagnostic advantages of the regression model will gradually decrease. However, for patients with low levels of PSA and PI-RADS scores,the regression model is less affected by PSA and PI-RADS, and can better utilize its clinical diagnostic advantages.

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