Dataset Information

Using the prostate imaging reporting and data system version 2 (PI-RIDS v2) to detect prostate cancer can prevent unnecessary biopsies and invasive treatment.

ABSTRACT: Prostate cancer (PCa) is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) to classify men with PCa, clinically significant PCa (CSPCa), or no PCa, especially among those with serum total prostate-specific antigen (tPSA) levels in the "gray zone" (4-10 ng ml^-1). A total of 308 patients (355 lesions) were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density (PSAD) were independent predictors of PCa and CSPCa. A PI-RADS v2 score ?4 provided high negative predictive values (91.39% for PCa and 95.69% for CSPCa). A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group (PI-RADS score = 5 and PSAD ?0.15 ng ml^-1 cm^-3, with tPSA in the gray zone, or PI-RADS score ?4 with high tPSA level) with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.

SUBMITTER: Liu C

PROVIDER: S-EPMC6116681 | biostudies-literature | 2018 Sep-Oct

REPOSITORIES: biostudies-literature

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Using the prostate imaging reporting and data system version 2 (PI-RIDS v2) to detect prostate cancer can prevent unnecessary biopsies and invasive treatment.

Liu Chang C Liu Shi-Liang SL Wang Zhi-Xian ZX Yu Kai K Feng Chun-Xiang CX Ke Zan Z Wang Liang L Zeng Xiao-Yong XY

Asian journal of andrology 20180901 5

Prostate cancer (PCa) is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) to classify men with PCa, clinically significant PCa (CSPCa), or no PCa, especially among those with serum total prostate-specific antigen (tPSA) levels in the "gray zone" (4-10 ng ml<sup>-1</sup>). A total of 308 patients (355 lesions) were enrolled in this study. Diagnostic efficiency was determined. Univa ...[more]

PMID: 29667616

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Project description:CONTEXT:The Prostate Imaging-Reporting and Data System (PI-RADS) v2 analysis system for multiparametric magnetic resonance imaging (mpMRI) detection of prostate cancer (PCa) is based on PI-RADS v1, accumulated scientific evidence, and expert consensus opinion. OBJECTIVE:To summarize the accuracy, strengths and weaknesses of PI-RADS v2, discuss pathway implications of its use and outline opportunities for improvements and future developments. EVIDENCE ACQUISITION:For this consensus expert opinion from the PI-RADS steering committee, clinical studies, systematic reviews, and professional guidelines for mpMRI PCa detection were evaluated. We focused on the performance characteristics of PI-RADS v2, comparing data to systems based on clinicoradiologic Likert scales and non-PI-RADS v2 imaging only. Evidence selections were based on high-quality, prospective, histologically verified data, with minimal patient selection and verifications biases. EVIDENCE SYNTHESIS:It has been shown that the test performance of PI-RADS v2 in research and clinical practice retains higher accuracy over systematic transrectal ultrasound (TRUS) biopsies for PCa diagnosis. PI-RADS v2 fails to detect all cancers but does detect the majority of tumors capable of causing patient harm, which should not be missed. Test performance depends on the definition and prevalence of clinically significant disease. Good performance can be attained in practice when the quality of the diagnostic process can be assured, together with joint working of robustly trained radiologists and urologists, conducting biopsy procedures within multidisciplinary teams. CONCLUSIONS:It has been shown that the test performance of PI-RADS v2 in research and clinical practice is improved, retaining higher accuracy over systematic TRUS biopsies for PCa diagnosis. PATIENT SUMMARY:Multiparametric magnetic resonance imaging (MRI) and MRI-directed biopsies using the Prostate Imaging-Reporting and Data System improves the detection of prostate cancers likely to cause harm, and at the same time decreases the detection of disease that does not lead to harms if left untreated. The keys to success are high-quality imaging, reporting, and biopsies by radiologists and urologists working together in multidisciplinary teams.

Project description:BackgroundMultiparametric MRI (mp-MRI) combined with machine-aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics-based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI-RADS v2) scores.PurposeTo develop and validate a radiomics-based model for differentiating PCa and assessing its aggressiveness compared with PI-RADS v2 scores.Study typeRetrospective.PopulationIn all, 182 patients with biopsy-proven PCa and 199 patients with a biopsy-proven absence of cancer were enrolled in our study.Field strength/sequenceConventional and diffusion-weighted MR images (b values = 0, 1000 sec/mm2 ) were acquired on a 3.0T MR scanner.AssessmentA total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T2 WI, respectively. A predictive model was constructed for differentiating PCa from non-PCa and high-grade from low-grade PCa. The diagnostic performance of each radiomics-based model was compared with that of the PI-RADS v2 scores.Statistical testsA radiomics-based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups.ResultsFor PCa versus non-PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T2 WI, ADC, and T2 WI&ADC features, respectively. For low-grade versus high-grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T2 WI, ADC, and T2 WI&ADC features, respectively. PI-RADS v2 had an AUC of 0.867 in differentiating PCa from non-PCa and an AUC of 0.763 in differentiating high-grade from low-grade PCa.Data conclusionBoth the T2 WI- and ADC-based radiomics models showed high diagnostic efficacy and outperformed the PI-RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high-grade vs. low-grade PCa.Level of evidence3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:875-884.

Dataset Information

Using the prostate imaging reporting and data system version 2 (PI-RIDS v2) to detect prostate cancer can prevent unnecessary biopsies and invasive treatment.

Publications

Using the prostate imaging reporting and data system version 2 (PI-RIDS v2) to detect prostate cancer can prevent unnecessary biopsies and invasive treatment.

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