Dataset Information

Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI.

ABSTRACT: OBJECTIVE. The purpose of this study was to evaluate agreement among radiologists in detecting and assessing prostate cancer at multiparametric MRI using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2). MATERIALS AND METHODS. Treatment-naïve patients underwent 3-T multipara-metric MRI between April 2012 and June 2015. Among the 163 patients evaluated, 110 underwent prostatectomy after MRI and 53 had normal MRI findings and transrectal ultrasound-guided biopsy results. Nine radiologists participated (three each with high, intermediate, and low levels of experience). Readers interpreted images of 58 patients on average (range, 56-60) using PI-RADSv2. Prostatectomy specimens registered to MRI were ground truth. Interob-server agreement was evaluated with the index of specific agreement for lesion detection and kappa and proportion of agreement for PI-RADS category assignment. RESULTS. The radiologists detected 336 lesions. Sensitivity for index lesions was 80.9% (95% CI, 75.1-85.9%), comparable across reader experience (p = 0.392). Patient-level specificity was experience dependent; highly experienced readers had 84.0% specificity versus 55.2% for all others (p < 0.001). Interobserver agreement was excellent for detecting index lesions (index of specific agreement, 0.871; 95% CI, 0.798-0.923). Agreement on PI-RADSv2 category assignment of index lesions was moderate (κ = 0.419; 95% CI, 0.238-0.595). For individual category assignments, proportion of agreement was slight for PI-RADS category 3 (0.208; 95% CI, 0.086-0.284) but substantial for PI-RADS category 4 (0.674; 95% CI, 0.540-0.776). However, proportion of agreement for T2-weighted PI-RADS 4 in the transition zone was 0.250 (95% CI, 0.108-0.372). Proportion of agreement for category assignment of index lesions on dynamic contrast-enhanced MR images was 0.822 (95% CI, 0.728-0.903), on T2-weighted MR images was 0.515 (95% CI, 0.430-0623), and on DW images was 0.586 (95% CI, 0.495-0.682). Proportion of agreement for dominant lesion was excellent (0.828; 95% CI, 0.742-0.913). CONCLUSION. Radiologists across experience levels had excellent agreement for detecting index lesions and moderate agreement for category assignment of lesions using PI-RADS. Future iterations of PI-RADS should clarify PI-RADS 3 and PI-RADS 4 in the transition zone.

SUBMITTER: Greer MD

PROVIDER: S-EPMC8268760 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI.

Greer Matthew D MD Shih Joanna H JH Lay Nathan N Barrett Tristan T Bittencourt Leonardo L Borofsky Samuel S Kabakus Ismail I Law Yan Mee YM Marko Jamie J Shebel Haytham H Merino Maria J MJ Wood Bradford J BJ Pinto Peter A PA Summers Ronald M RM Choyke Peter L PL Turkbey Baris B

AJR. American journal of roentgenology 20190327 6

<b>OBJECTIVE.</b> The purpose of this study was to evaluate agreement among radiologists in detecting and assessing prostate cancer at multiparametric MRI using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2). <b>MATERIALS AND METHODS.</b> Treatment-naïve patients underwent 3-T multipara-metric MRI between April 2012 and June 2015. Among the 163 patients evaluated, 110 underwent prostatectomy after MRI and 53 had normal MRI findings and transrectal ultrasound-guided biopsy resul ...[more]

PMID: 30917023

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Project description:BackgroundCurrent protocols endorse biopsies for men with Prostate Imaging-Reporting and Data System (PI-RADS v2.1) scores ≥3. However, the subjective nature of PI-RADS can lead to increased false positives and unnecessary biopsies. Synthetic magnetic resonance imaging (MRI), which quantifies multiple relaxation parameters, and apparent diffusion coefficient (ADC), which is the most commonly used quantitative metric, have not yet been combined with a predictive tool. This study aimed to develop and validate novel nomograms using multiparametric MRI, including synthetic MRI, to forecast the risk of prostate cancer (PCa) and clinically significant prostate cancer (csPCa), and to assess the potential of these nomograms to reduce unnecessary biopsies in PI-RADS ≥3 cases.MethodsBetween August 2020 and August 2022, 323 patients suspected of PCa were enrolled from two centers (cohort 1: 243; cohort 2: 80). All participants underwent multiparametric MRI, including synthetic MRI, before targeted biopsy. Univariable and multivariable logistic regression identified risk factors for PCa and csPCa. Internal validation was conducted using bootstrap resampling, and nomogram performance was evaluated through receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). External validation was performed with cohort 2 data. The impact of the nomograms on biopsy decisions was measured by the avoidance rate and the risk of missed diagnoses.ResultsThe predictive nomogram for PCa incorporated four risk factors: age, quantitative transverse relaxation time (T2 value) from synthetic MRI, ADC value, and PI-RADS score. The csPCa nomogram included age, ADC value, and PI-RADS score. The nomograms showed high diagnostic accuracy with the area under the curves (AUCs) of 0.916 [95% confidence interval (CI): 0.901-0.974] and 0.947 (95% CI: 0.900-0.994) for PCa prediction in training and external datasets, and 0.884 (95% CI: 0.840-0.928) and 0.935 (95% CI: 0.871-0.998) for csPCa. Calibration curves confirmed the accuracy of predictions. DCA indicated that the nomograms possessed significant net benefit. For PCa detection, biopsy strategy combining our nomogram reduced biopsy procedures by 20.2% and 13.8% in the training and external cohorts, respectively, with a PCa miss rate of 4.5% for both cohorts. The csPCa-targeted biopsy strategy also provided clinical benefits, with biopsy avoidance rates of 20.2% and 10.0%, and csPCa miss rates of 4.8% and 1.7% for PI-RADS ≥3 patients in the two cohorts.ConclusionsThe nomograms integrating multiparametric MRI and synthetic MRI are highly effective in predicting PCa and csPCa, concurrently, reducing unnecessary biopsies for patients with PI-RADS ≥3 lesion.

Dataset Information

Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI.

Publications

Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI.

Similar Datasets

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