Abstract
BackgroundThe Prostate Imaging Reporting and Data System, version 2.1 (PI-RADSv2.1) standardizes reporting of multiparametric MRI of the prostate. Assigned assessment categories are a risk stratification algorithm, higher categories indicate a higher probability of clinically significant cancer compared to lower categories. PI-RADSv2.1 does not define these probabilities numerically. We conduct a systematic review and meta-analysis to determine the cancer detection rates (CDR) of the PI-RADSv2.1 assessment categories on lesion level and patient level.MethodsTwo independent reviewers screen a systematic PubMed and Cochrane CENTRAL search for relevant articles (primary outcome: clinically significant cancer, index test: prostate MRI reading according to PI-RADSv2.1, reference standard: histopathology). We perform meta-analyses of proportions with random-effects models for the CDR of the PI-RADSv2.1 assessment categories for clinically significant cancer. We perform subgroup analysis according to lesion localization to test for differences of CDR between peripheral zone lesions and transition zone lesions.ResultsA total of 17 articles meet the inclusion criteria and data is independently extracted by two reviewers. Lesion level analysis includes 1946 lesions, patient level analysis includes 1268 patients. On lesion level analysis, CDR are 2% (95% confidence interval: 0–8%) for PI-RADS 1, 4% (1–9%) for PI-RADS 2, 20% (13–27%) for PI-RADS 3, 52% (43–61%) for PI-RADS 4, 89% (76–97%) for PI-RADS 5. On patient level analysis, CDR are 6% (0–20%) for PI-RADS 1, 9% (5–13%) for PI-RADS 2, 16% (7–27%) for PI-RADS 3, 59% (39–78%) for PI-RADS 4, 85% (73–94%) for PI-RADS 5. Higher categories are significantly associated with higher CDR (P < 0.001, univariate meta-regression), no systematic difference of CDR between peripheral zone lesions and transition zone lesions is identified in subgroup analysis.ConclusionsOur estimates of CDR demonstrate that PI-RADSv2.1 stratifies lesions and patients as intended. Our results might serve as an initial evidence base to discuss management strategies linked to assessment categories.
Highlights
Multiparametric MRI of the prostate has emerged as the imaging modality of choice for the diagnosis of prostate cancer, being utilized in primary diagnosis [1], active surveillance [2], and relapse diagnosis [3]
In the setting of primary diagnosis, prostate MRI is interpreted according to the Prostate Imaging Reporting and Data System (PI-RADS), developed by the European Society of Urogenital Radiology (ESUR) and the American College of Radiology (ACR) [4, 5]
Eligibility criteria, information sources, and search algorithm Studies are considered eligible for this systematic review if they report on the diagnostic performance of the PI-RADSv2.1 assessment categories in treatment naïve patients
Summary
Multiparametric MRI of the prostate has emerged as the imaging modality of choice for the diagnosis of prostate cancer, being utilized in primary diagnosis [1], active surveillance [2], and relapse diagnosis [3]. PI-RADS requires the interpreting radiologist to assign assessment categories to observed lesions. Assigned assessment categories are a risk stratification algorithm, higher categories indicate a higher probability of clinically significant cancer compared to lower categories. We conduct a systematic review and meta-analysis to determine the cancer detection rates (CDR) of the PI-RADSv2.1 assessment categories on lesion level and patient level. METHODS: Two independent reviewers screen a systematic PubMed and Cochrane CENTRAL search for relevant articles (primary outcome: clinically significant cancer, index test: prostate MRI reading according to PI-RADSv2.1, reference standard: histopathology). We perform meta-analyses of proportions with random-effects models for the CDR of the PI-RADSv2.1 assessment categories for clinically significant cancer. Higher categories are significantly associated with higher CDR (P < 0.001, univariate meta-regression), no systematic difference of CDR between peripheral zone lesions and transition zone lesions is identified in subgroup analysis. Our results might serve as an initial evidence base to discuss management strategies linked to assessment categories
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